{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas2)=\n",
    "# Table reshaping and joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import myst_nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2024-02-06 00:05:42\n"
     ]
    }
   ],
   "source": [
    "!echo Last updated: `date +'%Y-%m-%d %H:%M:%S'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9be78d54",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import options"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous chapter (see {ref}`pandas1`), we introduced the `pandas` package, and covered the basic operations with tables using `pandas`, such as importing and exporting, subsetting, renaming columns, sorting, and calculating new columns. In practice, we often need to do more than that. For example, we may need to aggregate, or to reshape, tables into a different form, or to combine numerous tables into one. In this chapter, we cover some of the common table operations which involve modifying table structure or combining multiple tables."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working with data, it is often necessary to *combine* data from multiple sources, and/or *reshape* data into the right form that a particular function accepts: \n",
    "\n",
    "* Combining may simply involve \"pasting\", or concatenating, two tables one on top of the other, or side-by-side (see {ref}`pandas-concatenation`). \n",
    "* In more complex cases, combining involves a table *join*, where the information from two tables is combined according to one or more \"key\" columns which are common to both tables (see {ref}`pandas-joining-tables`). \n",
    "* Moreover, we may need to summarize the values of a table according to a particular grouping, which is known as *aggregation* (see {ref}`pandas-aggregation`). \n",
    "* A special type of aggregation is *applying* a function across each row, or each column, of a table ({ref}`pandas-row-col-wise-operations`)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this chapter, we learn about all of those common methods for combining and reshaping tables. By the end of the chapter, you will have the ability to not just import and transform tables in Python (see {ref}`pandas1`), but also to reshape and combine tables to the right form, which facilitates further analysis and insight from the data in those tables."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's recreate the small table named `stations` which we used in the previous chapter (see {ref}`creating-dataframe`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat\n",
       "0      Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288\n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284\n",
       "2                 Dimona      Dimona      1  False  35.011635  31.068616"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "name = pd.Series(['Beer-Sheva Center', 'Beer-Sheva University', 'Dimona'])\n",
    "city = pd.Series(['Beer-Sheva', 'Beer-Sheva', 'Dimona'])\n",
    "lines = pd.Series([4, 5, 1])\n",
    "piano = pd.Series([False, True, False])\n",
    "lon = pd.Series([34.798443, 34.812831, 35.011635])\n",
    "lat = pd.Series([31.243288, 31.260284, 31.068616])\n",
    "d = {'name': name, 'city': city, 'lines': lines, 'piano': piano, 'lon': lon, 'lat': lat}\n",
    "stations = pd.DataFrame(d)\n",
    "stations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also load the sample climatic data which we are also familiar with from the previous chapter ({ref}`pandas-reading-from-file`), into a `DataFrame` named `dat`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Glob</th>\n",
       "      <th>NHem</th>\n",
       "      <th>SHem</th>\n",
       "      <th>24N-90N</th>\n",
       "      <th>...</th>\n",
       "      <th>EQU-24N</th>\n",
       "      <th>24S-EQU</th>\n",
       "      <th>44S-24S</th>\n",
       "      <th>64S-44S</th>\n",
       "      <th>90S-64S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1881</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1882</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1883</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1884</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2020</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2021</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2022</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.52</td>\n",
       "      <td>...</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2023</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.78</td>\n",
       "      <td>...</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Glob  NHem  SHem  24N-90N  ...  EQU-24N  24S-EQU  44S-24S  64S-44S  \\\n",
       "0    1880 -0.17 -0.28 -0.05    -0.38  ...    -0.14    -0.11    -0.04     0.05   \n",
       "1    1881 -0.09 -0.18  0.00    -0.36  ...     0.11     0.10    -0.06    -0.07   \n",
       "2    1882 -0.11 -0.21 -0.01    -0.31  ...    -0.04    -0.05     0.01     0.04   \n",
       "3    1883 -0.17 -0.28 -0.07    -0.34  ...    -0.17    -0.16    -0.04     0.07   \n",
       "4    1884 -0.28 -0.42 -0.15    -0.61  ...    -0.12    -0.17    -0.19    -0.02   \n",
       "..    ...   ...   ...   ...      ...  ...      ...      ...      ...      ...   \n",
       "139  2019  0.98  1.20  0.75     1.42  ...     0.90     0.90     0.75     0.39   \n",
       "140  2020  1.01  1.35  0.68     1.67  ...     0.88     0.84     0.58     0.39   \n",
       "141  2021  0.85  1.14  0.56     1.42  ...     0.72     0.60     0.72     0.32   \n",
       "142  2022  0.89  1.16  0.62     1.52  ...     0.62     0.51     0.79     0.38   \n",
       "143  2023  1.17  1.49  0.85     1.78  ...     1.07     1.04     0.91     0.44   \n",
       "\n",
       "     90S-64S  \n",
       "0       0.67  \n",
       "1       0.60  \n",
       "2       0.63  \n",
       "3       0.50  \n",
       "4       0.65  \n",
       "..       ...  \n",
       "139     0.83  \n",
       "140     0.89  \n",
       "141     0.30  \n",
       "142     1.09  \n",
       "143     0.63  \n",
       "\n",
       "[144 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = pd.read_csv('data/ZonAnn.Ts+dSST.csv')\n",
    "dat"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas-concatenation)=\n",
    "## Concatenation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most basic method for combining several tables into one is *concatenation*, which essentially means binding tables one on top of another, or side-by-side:\n",
    "\n",
    "* Concatenation *by row* is usually desired when the two (or more) tables share exactly the same columns. For example, combining several tables reflecting the same data (e.g., measured on different dates, or for different subjects) involves concatenation by row. \n",
    "* Concatenation *by column* is useful when we need to attach new variables for the same observations we already have. For example, we may want to combine several different type of observations which are split across separate files."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `pandas`, concatenation can be done using [`pd.concat`](https://pandas.pydata.org/docs/reference/api/pandas.concat.html). The `pd.concat` function accepts a `list` of tables, which are combined:\n",
    "\n",
    "* *By row* when using `axis=0` (the default)\n",
    "* *By column* when using `axis=1`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(concatenation-by-row)=\n",
    "### Concatenation by row"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate concatenation *by row*, let's create two subsets `x` and `y` of `stations`, with the 1<sup>st</sup> row and the 2<sup>nd</sup>-3<sup>rd</sup> rows, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name        city  lines  piano        lon        lat\n",
       "0  Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = stations.iloc[[0], :]     ## 1st row\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat\n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284\n",
       "2                 Dimona      Dimona      1  False  35.011635  31.068616"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = stations.iloc[[1, 2], :]  ## 2nd and 3rd rows\n",
    "y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expression `pd.concat([x,y])` then can be used to concatenates `x` and `y`, by row, re-creating the complete table `stations`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat\n",
       "0      Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288\n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284\n",
       "2                 Dimona      Dimona      1  False  35.011635  31.068616"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([x, y])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `pd.concat` accepts a `list` of `DataFrame`s to concatenate, such as `[x,y]`. In this example the `list` is of length 2, but in general it can be of any length. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(concatenation-by-column)=\n",
    "### Concatenation by column"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate concatenation by column, let's now split `stations` to subsets `x` and `y` *by column*. For example, the `x` table is going to contain one column `'name'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name\n",
       "0      Beer-Sheva Center\n",
       "1  Beer-Sheva University\n",
       "2                 Dimona"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = stations[['name']]\n",
    "x"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the `y` table is going to contain two columns `'lon'` and `'lat'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.798443</td>\n",
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       "      <th>1</th>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lon        lat\n",
       "0  34.798443  31.243288\n",
       "1  34.812831  31.260284\n",
       "2  35.011635  31.068616"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = stations[['lon', 'lat']]\n",
    "y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To concatenate by column, we use `pd.concat` with the `axis=1` argument. This concatenates the tables `x` and `y` side-by-side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        lon        lat\n",
       "0      Beer-Sheva Center  34.798443  31.243288\n",
       "1  Beer-Sheva University  34.812831  31.260284\n",
       "2                 Dimona  35.011635  31.068616"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([x, y], axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alignment by index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that concatenation—like many other operations in `pandas`—operates based on the *index*, rather than position. This is not a problem when concatenating by row, where columns typically need to be aligned by their index, i.e., column name (see {ref}`concatenation-by-row`). When concatenating by column, however, we need to pay attention to the index: is it meaningful, and we want to use it when aligning rows? Or is it arbitarary, and we want to reset it before concatenating?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, here is what happens in concatenation by column, when the index is not aligned (see {ref}`setting-series-index`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
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       "      <th>name</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        lon        lat\n",
       "0      Beer-Sheva Center        NaN        NaN\n",
       "1  Beer-Sheva University  34.798443  31.243288\n",
       "2                 Dimona  34.812831  31.260284\n",
       "3                    NaN  35.011635  31.068616"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.index = [1,2,3]\n",
    "pd.concat([x, y], axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure we concatenate based on *position*, we may reset the index (using `.reset_index(drop=True)`) on both tables before concatenation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        lon        lat\n",
       "0      Beer-Sheva Center  34.798443  31.243288\n",
       "1  Beer-Sheva University  34.812831  31.260284\n",
       "2                 Dimona  35.011635  31.068616"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.reset_index(drop=True)\n",
    "pd.concat([x, y], axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas-row-col-wise-operations)=\n",
    "## Row/col-wise operations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas-row-colwise-built-in)=\n",
    "### Built-in methods"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pandas` defines summary methods which can be used to calculate row-wise and column-wise summaries of `DataFrame`s. The `pandas` methods are generally similar to the `numpy` 2D summary functions and methods (see {ref}`numpy-summaries-per-dimension-2d`) which we learned about earlier, but there are several important differences to keep in mind:\n",
    "\n",
    "* `pandas` **only has methods**, not functions, i.e., there is a `.mean` method but there is *no* `pd.mean` function—unlike `numpy`, which in many cases has both, e.g., both `.mean` and `np.mean` (see {numref}`numpy-summary-functions`)\n",
    "* The **default `axis`** is different: in `pandas` the default is column-wise summary (`axis=0`), while in `numpy` the default is \"global\" summary (`axis=None`) (see {ref}`numpy-global-summaries`)\n",
    "* `pandas` methods **exclude \"No Data\"** by default (see {ref}`pandas-operations-with-missing-data`), unless we explicitly specify `skipna=False`—unlike `numpy` which by default includes \"No Data\", unless using the specific \"No Data\"-safe functions such as `np.nanmean` (see {ref}`numpy-operations-with-nodata`)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Commonly used `pandas` summary methods are listed in {numref}`pandas-aggregation-methods`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{table} Pandas aggregation methods\n",
    ":name: pandas-aggregation-methods\n",
    "\n",
    "| Operation | Method |\n",
    "|:---:|:---:|\n",
    "| `.sum`     | Sum |\n",
    "| `.min`     | Minimum | \n",
    "| `.max`     | Maximum | \n",
    "| `.mean`    | Mean | \n",
    "| `.median`  | Median |\n",
    "| `.count`   | Count (of non-missing values) |\n",
    "| `.nunique` | Number of unique values |\n",
    "| `.idxmin`  | Index of (first) minimum |\n",
    "| `.idxmax`  | Index of (first) maximum |\n",
    "| `.any`     | Is at least one element `True`? |\n",
    "| `.all`     | Are all elements `True`? |\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the next few examples, let's take the *regional* temperature anomaly time series into a separate `DataFrame` named `regions`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>90S-64S</th>\n",
       "      <th>64S-44S</th>\n",
       "      <th>44S-24S</th>\n",
       "      <th>24S-EQU</th>\n",
       "      <th>EQU-24N</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.67</td>\n",
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       "      <td>-0.14</td>\n",
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       "      <td>-0.80</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.60</td>\n",
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       "      <td>-0.06</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.11</td>\n",
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       "      <td>-0.93</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.65</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.43</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.09</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     90S-64S  64S-44S  44S-24S  24S-EQU  EQU-24N  24N-44N  44N-64N  64N-90N\n",
       "0       0.67     0.05    -0.04    -0.11    -0.14    -0.26    -0.52    -0.80\n",
       "1       0.60    -0.07    -0.06     0.10     0.11    -0.19    -0.50    -0.93\n",
       "2       0.63     0.04     0.01    -0.05    -0.04    -0.13    -0.31    -1.41\n",
       "3       0.50     0.07    -0.04    -0.16    -0.17    -0.24    -0.58    -0.18\n",
       "4       0.65    -0.02    -0.19    -0.17    -0.12    -0.45    -0.66    -1.30\n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...\n",
       "139     0.83     0.39     0.75     0.90     0.90     0.99     1.43     2.71\n",
       "140     0.89     0.39     0.58     0.84     0.88     1.19     1.81     2.88\n",
       "141     0.30     0.32     0.72     0.60     0.72     1.26     1.35     2.05\n",
       "142     1.09     0.38     0.79     0.51     0.62     1.27     1.50     2.34\n",
       "143     0.63     0.44     0.91     1.04     1.07     1.47     1.87     2.58\n",
       "\n",
       "[144 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    '90S-64S', \n",
    "    '64S-44S', \n",
    "    '44S-24S', \n",
    "    '24S-EQU', \n",
    "    'EQU-24N', \n",
    "    '24N-44N', \n",
    "    '44N-64N', \n",
    "    '64N-90N'\n",
    "]\n",
    "regions = dat[cols]\n",
    "regions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `.mean` method combined with `axis=0` (i.e., the function is applied on each column), we can calculate the mean temperature anomaly in each region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90S-64S   -0.074028\n",
       "64S-44S   -0.053681\n",
       "44S-24S    0.047917\n",
       "24S-EQU    0.083056\n",
       "EQU-24N    0.066181\n",
       "24N-44N    0.052500\n",
       "44N-64N    0.142500\n",
       "64N-90N    0.273333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions.mean(axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `axis=0` is the default, so we can also get the same result without specifying it (unlike `numpy` where the default is global summary, i.e., `axis=None`; see {ref}`numpy-global-summaries`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90S-64S   -0.074028\n",
       "64S-44S   -0.053681\n",
       "44S-24S    0.047917\n",
       "24S-EQU    0.083056\n",
       "EQU-24N    0.066181\n",
       "24N-44N    0.052500\n",
       "44N-64N    0.142500\n",
       "64N-90N    0.273333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions.mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 06-a\n",
    ":class: important\n",
    "* Calculate a `list` of (the first) years when the maximum temperature was observed per region (answer: {glue:}`year-of-max-per-region`)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1996, 1985, 2023, 2016, 2023, 2023, 2023, 2016]"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "year-of-max-per-region"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myst_nb.glue('year-of-max-per-region', dat['Year'].loc[regions.idxmax()].tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversely, using `axis=1`, we can calculate *row* means, i.e., the average temperature anomaly for each year across all regions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.14375\n",
       "1     -0.11750\n",
       "2     -0.15750\n",
       "3     -0.10000\n",
       "4     -0.28250\n",
       "        ...   \n",
       "139    1.11250\n",
       "140    1.18250\n",
       "141    0.91500\n",
       "142    1.06250\n",
       "143    1.25125\n",
       "Length: 144, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions.mean(axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If necessary, the result can be assigned into a new column (see {ref}`pandas-creating-new-columns`), hereby named `mean`, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Glob</th>\n",
       "      <th>NHem</th>\n",
       "      <th>SHem</th>\n",
       "      <th>24N-90N</th>\n",
       "      <th>...</th>\n",
       "      <th>24S-EQU</th>\n",
       "      <th>44S-24S</th>\n",
       "      <th>64S-44S</th>\n",
       "      <th>90S-64S</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.67</td>\n",
       "      <td>-0.14375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1881</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-0.11750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1882</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.63</td>\n",
       "      <td>-0.15750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1883</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1884</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-0.28250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.11250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2020</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.18250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2021</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.42</td>\n",
       "      <td>...</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.91500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2022</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.52</td>\n",
       "      <td>...</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.06250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2023</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.78</td>\n",
       "      <td>...</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.25125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Glob  NHem  SHem  24N-90N  ...  24S-EQU  44S-24S  64S-44S  90S-64S  \\\n",
       "0    1880 -0.17 -0.28 -0.05    -0.38  ...    -0.11    -0.04     0.05     0.67   \n",
       "1    1881 -0.09 -0.18  0.00    -0.36  ...     0.10    -0.06    -0.07     0.60   \n",
       "2    1882 -0.11 -0.21 -0.01    -0.31  ...    -0.05     0.01     0.04     0.63   \n",
       "3    1883 -0.17 -0.28 -0.07    -0.34  ...    -0.16    -0.04     0.07     0.50   \n",
       "4    1884 -0.28 -0.42 -0.15    -0.61  ...    -0.17    -0.19    -0.02     0.65   \n",
       "..    ...   ...   ...   ...      ...  ...      ...      ...      ...      ...   \n",
       "139  2019  0.98  1.20  0.75     1.42  ...     0.90     0.75     0.39     0.83   \n",
       "140  2020  1.01  1.35  0.68     1.67  ...     0.84     0.58     0.39     0.89   \n",
       "141  2021  0.85  1.14  0.56     1.42  ...     0.60     0.72     0.32     0.30   \n",
       "142  2022  0.89  1.16  0.62     1.52  ...     0.51     0.79     0.38     1.09   \n",
       "143  2023  1.17  1.49  0.85     1.78  ...     1.04     0.91     0.44     0.63   \n",
       "\n",
       "        mean  \n",
       "0   -0.14375  \n",
       "1   -0.11750  \n",
       "2   -0.15750  \n",
       "3   -0.10000  \n",
       "4   -0.28250  \n",
       "..       ...  \n",
       "139  1.11250  \n",
       "140  1.18250  \n",
       "141  0.91500  \n",
       "142  1.06250  \n",
       "143  1.25125  \n",
       "\n",
       "[144 rows x 16 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['mean'] = regions.mean(axis=1)\n",
    "dat"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a visualization (see {ref}`pandas-line-plots`) of the mean time series we just calculated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat.set_index('Year')['mean'].plot();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(apply-and-custom-functions)=\n",
    "### `apply` and custom functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case we need to apply a custom function on each row, or column, we can use the [`.apply`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html) method combined with a lambda function (see {ref}`lambda-functions`). For example, here is an alternative way to calculate row-wise means, using the `.apply` method and a lambda function. The result is the same as `regions.mean()` (see {ref}`pandas-row-colwise-built-in`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90S-64S   -0.074028\n",
       "64S-44S   -0.053681\n",
       "44S-24S    0.047917\n",
       "24S-EQU    0.083056\n",
       "EQU-24N    0.066181\n",
       "24N-44N    0.052500\n",
       "44N-64N    0.142500\n",
       "64N-90N    0.273333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions.apply(lambda x: x.mean())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advantage is that we can use any custom function definition. For example, the following expression calculates whether any region had temperature anomaly above 1 degree, in at least one of the 140 years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90S-64S     True\n",
       "64S-44S    False\n",
       "44S-24S    False\n",
       "24S-EQU     True\n",
       "EQU-24N     True\n",
       "24N-44N     True\n",
       "44N-64N     True\n",
       "64N-90N     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions.apply(lambda x: (x > 1).any(), axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 06-b\n",
    ":class: important\n",
    "* Function `np.polyfit` can be used to calculate the slope of linear regression between two columns. For example, using the code section shown below, you can find out that the linear trend of temperature in the `'44S-24S'` region was an increasing one, at `0.0066` degrees per year. \n",
    "* Adapt the code into to the form of a function named `slope` which, given two `Series` objects `x` and `y`, calculates the linear slope. For example, `f(dat['Year'],regions['44S-24S'])` should return `0.0066`.\n",
    "* Use the `slope` function you defined within the `.apply` method, to calculate the slope in all columns of `regions` at once. \n",
    "* Hint: you need to keep the `x` argument of `slope` fixed at `dat['Year']`, while the various columns of `regions` are passed to `y`. To do that, write another function (or use a lambda function) that \"wraps\" `slope` and runs it with just the `y` parameter (keeping `x` fixed). \n",
    "\n",
    "```py\n",
    "x = dat['Year']\n",
    "y = regions['44S-24S']\n",
    "np.polyfit(x, y, 1)[0]\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas-joining-tables)=\n",
    "## Joining tables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most common operations when working with data is a *table join*. In a table join, two tables are combined into one, based on one or more common column(s). For example, when compiling a dataset about different cities, we may collect data on different aspects of each city (population size, socio-economic status, built area, etc.) from different sources. To work with these data together, the various tables need to be joined into one. The common column, in this case, would be the city name, or city ID. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working with `pandas`, tables can be joined, based on one or more common column, using the [`pd.merge`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html) function. `pd.merge` has the following important parameters:\n",
    "\n",
    "* `left`—The first (\"left\") table\n",
    "* `right`—The second (\"right\") table\n",
    "* `how`—The type of join, one of: `'left'`, `'right'`, `'outer'`, `'inner'` (the default), `'cross'`\n",
    "* `on`—The name(s) of the common column(s) where identical values are considered a \"match\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *type* of join (the `how` parameter) basically determines what should be done with non-matching records. The options are `'left'`, `'right'`, `'outer'`, `'inner'` (the default), and `'cross'`. The meaning of the join types is the same as in SQL [^sql-join-types], and will be demonstrated through examples below. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[^sql-join-types]: For a visual explanation of join types, see <https://dataschool.com/how-to-teach-people-sql/sql-join-types-explained-visually/>."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stations example"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a first small example, let's get back to the `stations` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat\n",
       "0      Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288\n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284\n",
       "2                 Dimona      Dimona      1  False  35.011635  31.068616"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we have another small table named `status` which describes the status (`'Closed'` or `'Open'`) of select stations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tel-Aviv University</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  status\n",
       "0      Beer-Sheva Center  Closed\n",
       "1  Beer-Sheva University    Open\n",
       "2    Tel-Aviv University    Open"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = pd.Series(['Beer-Sheva Center', 'Beer-Sheva University', 'Tel-Aviv University'])\n",
    "status = pd.Series(['Closed', 'Open', 'Open'])\n",
    "status = pd.DataFrame({'name': name, 'status': status})\n",
    "status"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `stations` and `status` can be joined as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat  \\\n",
       "0      Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288   \n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284   \n",
       "\n",
       "   status  \n",
       "0  Closed  \n",
       "1    Open  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(stations, status)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This implies the default, `on` being equal to the column(s) sharing the same name in both tables (`on='name'`, in this case), and an `'inner'` join (i.e., `how='inner'`). As you can see, an inner join returns just the matching rows. It is better to be explicit, for clarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat  \\\n",
       "0      Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288   \n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284   \n",
       "\n",
       "   status  \n",
       "0  Closed  \n",
       "1    Open  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(stations, status, on='name', how='inner')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike an `'inner'` join, a `'left'` join returns all records from the first (\"left\") table, even those that have no match in the second (\"right\") table. For example, `'Dimona'` station does not appear in the `status` table, but it is still returned in the result (with `status` set to `np.nan`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat  \\\n",
       "0      Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288   \n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284   \n",
       "2                 Dimona      Dimona      1  False  35.011635  31.068616   \n",
       "\n",
       "   status  \n",
       "0  Closed  \n",
       "1    Open  \n",
       "2     NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(stations, status, on='name', how='left')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `'right'` join is the opposite of a `'left'` join, i.e., it returns all records from the \"right\" table, even those that have no match in the \"left\" table. This time, values absent in the \"left\" table are set to `np.nan`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tel-Aviv University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat  \\\n",
       "0      Beer-Sheva Center  Beer-Sheva    4.0  False  34.798443  31.243288   \n",
       "1  Beer-Sheva University  Beer-Sheva    5.0   True  34.812831  31.260284   \n",
       "2    Tel-Aviv University         NaN    NaN    NaN        NaN        NaN   \n",
       "\n",
       "   status  \n",
       "0  Closed  \n",
       "1    Open  \n",
       "2    Open  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(stations, status, on='name', how='right')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, an `'outer'` (also known as \"full\") join returns all record from both tables, regardless of whether they have a matching record in the other table. Values that have no match in both the \"left\" and \"right\" tables are set to `np.nan`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "      <td>Closed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tel-Aviv University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Open</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat  \\\n",
       "0      Beer-Sheva Center  Beer-Sheva    4.0  False  34.798443  31.243288   \n",
       "1  Beer-Sheva University  Beer-Sheva    5.0   True  34.812831  31.260284   \n",
       "2                 Dimona      Dimona    1.0  False  35.011635  31.068616   \n",
       "3    Tel-Aviv University         NaN    NaN    NaN        NaN        NaN   \n",
       "\n",
       "   status  \n",
       "0  Closed  \n",
       "1    Open  \n",
       "2     NaN  \n",
       "3    Open  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(stations, status, on='name', how='outer')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working with data, we often have one main, or \"template\", table we are working with (\"left\") and we are interested to join data coming from another external (\"right\") table to it. In this scenario we usually prefer not to lose any records from the \"left\" table. Even if some of the records have no match and get \"No Data\" values (namely, `np.nan`), we usually prefer to keep those records and deal with the missing values as necessary, rather than lose the records altogether. Therefore, a `'left'` join is often most useful in data analysis. For example, in the above situation a `'left'` join makes sense if our focus is on the station properties given in `stations`, so even though the `status` of `'Dimona'` is unknown---we prefer to keep the `'Dimona'` station record. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(what-is-gtfs)=\n",
    "### What is GTFS?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next examples, we demonstrate table join with a more realistic large dataset, namely a [GTFS](https://en.wikipedia.org/wiki/GTFS) public transport dataset. Before we go into the example, we need to be familiar with the GTFS format and structure. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The General Transit Feed Specification (GTFS) is a standard format for describing public transport routes and timetables. GTFS is typically published by government transport agencies, periodically. For example, the GTFS dataset provided as part of the book's sample data was downloaded from Israel ministry of transport [GTFS website](https://www.gov.il/he/departments/general/gtfs_general_transit_feed_specifications) on `2023-09-30`. GTFS data are used by both commercial companies, such as for routing in Google Maps, and in open-source tools. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Technically, a GTFS dataset is a collection of several CSV files (even though their file extension is `.txt`), of specific designation and structure, linked via common columns, or *keys*. In the examples, we are going to use only the six most important files of the GTFS dataset ({numref}`gtfs-files`). Recall that we already met one of the GTFS files, namely `'stops.txt'`, earlier, in an example of text file processing through `for` loops (see {ref}`file-object-and-reading-lines`)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{table} GTFS files used in the examples\n",
    ":name: gtfs-files\n",
    "\n",
    "| Name | Contents | Keys | \n",
    "|:---:|:---:|:---:|\n",
    "| `agency.txt` | Agencies | `agency_id` | \n",
    "| `routes.txt` | Routes | `route_id`, `agency_id` |\n",
    "| `trips.txt` | Trips | `route_id`, `trip_id`, `shape_id` |\n",
    "| `stop_times.txt` | Stop times | `trip_id`, `stop_id` | \n",
    "| `stops.txt` | Stops | `stop_id` | \n",
    "| `shapes.txt` | Shapes | `shape_id` |\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the GTFS tables look like, what is their purpose, and how are they related with one another. To make things easier, we immediately subset the most important columns, which are also the ones we use in subsequent examples."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start with `'agency.txt'`. This file lists the public transport *agencies*. Each row represents one agency, identified by `'agency_id'` (note that we are using `usecols` to read just the necessary columns from the CSV file):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>agency_id</th>\n",
       "      <th>agency_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>רכבת ישראל</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>אגד</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>אלקטרה אפיקים תחבורה</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>דן</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>ש.א.מ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>51</td>\n",
       "      <td>ירושלים-צור באהר איחוד</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>91</td>\n",
       "      <td>מוניות מטרו קו</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>93</td>\n",
       "      <td>מוניות מאיה יצחק שדה</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>97</td>\n",
       "      <td>אודליה מוניות בעמ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>98</td>\n",
       "      <td>מוניות רב קווית 4-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    agency_id             agency_name\n",
       "0           2              רכבת ישראל\n",
       "1           3                     אגד\n",
       "2           4    אלקטרה אפיקים תחבורה\n",
       "3           5                      דן\n",
       "4           6                   ש.א.מ\n",
       "..        ...                     ...\n",
       "31         51  ירושלים-צור באהר איחוד\n",
       "32         91          מוניות מטרו קו\n",
       "33         93    מוניות מאיה יצחק שדה\n",
       "34         97       אודליה מוניות בעמ\n",
       "35         98     מוניות רב קווית 4-5\n",
       "\n",
       "[36 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['agency_id', 'agency_name']\n",
    "pd.read_csv('data/gtfs/agency.txt', usecols=cols)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "`pd.read_csv` uses the `'utf-8'` encoding by default. All sample CSV files in this book are in the UTF-8 encoding, so there is no need to specify the encoding when reading them with `pd.read_csv`. Otherwise, the right encoding can be specified with the `encoding` parameter of `pd.read_csv`.\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next file we look into is `'routes.txt'`. This file describes the public transport routes, identified by `'route_id'` and also specifying the name of the route: \n",
    "* `'route_short_name'`—typically a number, such as bus line number \n",
    "* `'route_long_name'`—the route textual description"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also contains `'agency_id'` to identify the agency which operates each route:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>ת. רכבת יבנה מערב-יבנה&lt;-&gt;ת. רכב...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2א</td>\n",
       "      <td>ת. רכבת יבנה מערב-יבנה&lt;-&gt;ת. רכב...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>ת. רכבת יבנה מזרח-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>8183</th>\n",
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       "    <tr>\n",
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       "      <th>8185</th>\n",
       "      <td>37342</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>גן טכנולוגי-נצרת&lt;-&gt;מסרארה חט''ב...</td>\n",
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       "    <tr>\n",
       "      <th>8186</th>\n",
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       "      <td>46</td>\n",
       "      <td>גן טכנולוגי-נצרת&lt;-&gt;מסרארה חט''ב...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8187</th>\n",
       "      <td>37350</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8188 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      route_id  agency_id route_short_name                     route_long_name\n",
       "0            1         25                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...\n",
       "1            2         25                1  ת. רכבת יבנה מזרח-יבנה<->ת. רכב...\n",
       "2            3         25                2  ת. רכבת יבנה מערב-יבנה<->ת. רכב...\n",
       "3            4         25               2א  ת. רכבת יבנה מערב-יבנה<->ת. רכב...\n",
       "4            5         25                2  ת. רכבת יבנה מזרח-יבנה<->ת. רכב...\n",
       "...        ...        ...              ...                                 ...\n",
       "8183     37315          2              NaN  תא אוניברסיטה-תל אביב יפו<->באר...\n",
       "8184     37316          2              NaN  תא אוניברסיטה-תל אביב יפו<->הרצ...\n",
       "8185     37342          6               46  גן טכנולוגי-נצרת<->מסרארה חט''ב...\n",
       "8186     37343          6               46  גן טכנולוגי-נצרת<->מסרארה חט''ב...\n",
       "8187     37350          2              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה\n",
       "\n",
       "[8188 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['route_id', 'agency_id', 'route_short_name', 'route_long_name']\n",
    "pd.read_csv('data/gtfs/routes.txt', usecols=cols)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `'routes.txt'` table is linked to the `'trips.txt'` table, which characterizes trips taking place at specific *times of day* for a given route. Trips are uniquely identified by `'trip_id'` and linked with the routes table via the `'route_id'` column. As you can see from the small subset below, each route may be associated with multiple trips. The trips table also has a `'shape_id'` column which associates a trip with the geographical line geometry (see `'shapes.txt'` below). You can see that trips of the same route at different times of day typically follow exactly the same geographical shape, thus having identical `'shape_id'` values, but this is not guaranteed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "      <td>43</td>\n",
       "      <td>2807_011023</td>\n",
       "      <td>127560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398091 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        route_id          trip_id  shape_id\n",
       "0             60      3764_081023  126055.0\n",
       "1             68   3143332_011023  128020.0\n",
       "2             68   3143338_011023  128020.0\n",
       "3             68   3244955_011023  128020.0\n",
       "4             68   3244956_011023  128020.0\n",
       "...          ...              ...       ...\n",
       "398086        43      2827_081023  127560.0\n",
       "398087        43  17254928_011023  127560.0\n",
       "398088        43      2796_011023  127560.0\n",
       "398089        43      2806_011023  127560.0\n",
       "398090        43      2807_011023  127560.0\n",
       "\n",
       "[398091 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['route_id', 'trip_id', 'shape_id']\n",
    "pd.read_csv('data/gtfs/trips.txt', usecols=cols)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `'stop_times.txt'` table lists the stops associated with each trip (via the `'trip_id'` column), including the (scheduled) time of arrival at each stop. The stop times table also includes the `'stop_id'` column to associate it with the stop characteristics (see `'stops.txt'` below). In addition, the `'shape_dist_traveled'` column contains cumulative distance traveled (in $m$) at each stop. You can see the increasing distance traveled from one from stop to another (in the same trip) in the printout below. The `'stop_times.txt'` table is the largest one, in terms of file size, among the GTFS files; keep in mind that it lists all stops, at all times of day, of all public transport routes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>departure_time</th>\n",
       "      <th>stop_id</th>\n",
       "      <th>shape_dist_traveled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_011023</td>\n",
       "      <td>05:10:00</td>\n",
       "      <td>05:10:00</td>\n",
       "      <td>38725</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_011023</td>\n",
       "      <td>05:12:23</td>\n",
       "      <td>05:12:23</td>\n",
       "      <td>15582</td>\n",
       "      <td>714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_011023</td>\n",
       "      <td>05:13:20</td>\n",
       "      <td>05:13:20</td>\n",
       "      <td>15583</td>\n",
       "      <td>1215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1_011023</td>\n",
       "      <td>05:14:31</td>\n",
       "      <td>05:14:31</td>\n",
       "      <td>16086</td>\n",
       "      <td>1791.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1_011023</td>\n",
       "      <td>05:15:09</td>\n",
       "      <td>05:15:09</td>\n",
       "      <td>16085</td>\n",
       "      <td>2152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15169524</th>\n",
       "      <td>9999194_151023</td>\n",
       "      <td>20:19:45</td>\n",
       "      <td>20:19:45</td>\n",
       "      <td>9272</td>\n",
       "      <td>12666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15169525</th>\n",
       "      <td>9999194_151023</td>\n",
       "      <td>20:20:59</td>\n",
       "      <td>20:20:59</td>\n",
       "      <td>11495</td>\n",
       "      <td>12970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15169526</th>\n",
       "      <td>9999194_151023</td>\n",
       "      <td>20:21:59</td>\n",
       "      <td>20:21:59</td>\n",
       "      <td>9550</td>\n",
       "      <td>13237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15169527</th>\n",
       "      <td>9999194_151023</td>\n",
       "      <td>20:22:40</td>\n",
       "      <td>20:22:40</td>\n",
       "      <td>10483</td>\n",
       "      <td>13395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15169528</th>\n",
       "      <td>9999194_151023</td>\n",
       "      <td>20:23:08</td>\n",
       "      <td>20:23:08</td>\n",
       "      <td>11809</td>\n",
       "      <td>13597.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15169529 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 trip_id arrival_time departure_time  stop_id  \\\n",
       "0               1_011023     05:10:00       05:10:00    38725   \n",
       "1               1_011023     05:12:23       05:12:23    15582   \n",
       "2               1_011023     05:13:20       05:13:20    15583   \n",
       "3               1_011023     05:14:31       05:14:31    16086   \n",
       "4               1_011023     05:15:09       05:15:09    16085   \n",
       "...                  ...          ...            ...      ...   \n",
       "15169524  9999194_151023     20:19:45       20:19:45     9272   \n",
       "15169525  9999194_151023     20:20:59       20:20:59    11495   \n",
       "15169526  9999194_151023     20:21:59       20:21:59     9550   \n",
       "15169527  9999194_151023     20:22:40       20:22:40    10483   \n",
       "15169528  9999194_151023     20:23:08       20:23:08    11809   \n",
       "\n",
       "          shape_dist_traveled  \n",
       "0                         0.0  \n",
       "1                       714.0  \n",
       "2                      1215.0  \n",
       "3                      1791.0  \n",
       "4                      2152.0  \n",
       "...                       ...  \n",
       "15169524              12666.0  \n",
       "15169525              12970.0  \n",
       "15169526              13237.0  \n",
       "15169527              13395.0  \n",
       "15169528              13597.0  \n",
       "\n",
       "[15169529 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['trip_id', 'arrival_time', 'departure_time', 'stop_id', 'shape_dist_traveled']\n",
    "pd.read_csv('data/gtfs/stop_times.txt', usecols=cols)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `'stops.txt'` table, which we already worked with earlier (see {ref}`reading-csv-files`), describes the fixed, i.e., unrelated to time of day, properties of the stops. It is linked with the `'stops_time.txt'` table via the `'stop_id'` column. Stop properties include the stop name (`'stop_name'`), and its coordinates (`'stop_lon'` and `'stop_lat'`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_id</th>\n",
       "      <th>stop_name</th>\n",
       "      <th>stop_lat</th>\n",
       "      <th>stop_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>בי''ס בר לב/בן יהודה</td>\n",
       "      <td>32.183985</td>\n",
       "      <td>34.917554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>הרצל/צומת בילו</td>\n",
       "      <td>31.870034</td>\n",
       "      <td>34.819541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>הנחשול/הדייגים</td>\n",
       "      <td>31.984553</td>\n",
       "      <td>34.782828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>משה פריד/יצחק משקה</td>\n",
       "      <td>31.888325</td>\n",
       "      <td>34.790700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>ת. מרכזית לוד/הורדה</td>\n",
       "      <td>31.956392</td>\n",
       "      <td>34.898098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34002</th>\n",
       "      <td>50182</td>\n",
       "      <td>מסוף שכונת החותרים/הורדה</td>\n",
       "      <td>32.748564</td>\n",
       "      <td>34.966691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34003</th>\n",
       "      <td>50183</td>\n",
       "      <td>מסוף שכונת החותרים/איסוף</td>\n",
       "      <td>32.748776</td>\n",
       "      <td>34.966844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34004</th>\n",
       "      <td>50184</td>\n",
       "      <td>מדבריום</td>\n",
       "      <td>31.260857</td>\n",
       "      <td>34.744373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34005</th>\n",
       "      <td>50185</td>\n",
       "      <td>כביש 25/מדבריום</td>\n",
       "      <td>31.263478</td>\n",
       "      <td>34.747607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34006</th>\n",
       "      <td>50186</td>\n",
       "      <td>כביש 25/מדבריום</td>\n",
       "      <td>31.264262</td>\n",
       "      <td>34.746768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34007 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       stop_id                 stop_name   stop_lat   stop_lon\n",
       "0            1      בי''ס בר לב/בן יהודה  32.183985  34.917554\n",
       "1            2            הרצל/צומת בילו  31.870034  34.819541\n",
       "2            3            הנחשול/הדייגים  31.984553  34.782828\n",
       "3            4        משה פריד/יצחק משקה  31.888325  34.790700\n",
       "4            6       ת. מרכזית לוד/הורדה  31.956392  34.898098\n",
       "...        ...                       ...        ...        ...\n",
       "34002    50182  מסוף שכונת החותרים/הורדה  32.748564  34.966691\n",
       "34003    50183  מסוף שכונת החותרים/איסוף  32.748776  34.966844\n",
       "34004    50184                   מדבריום  31.260857  34.744373\n",
       "34005    50185           כביש 25/מדבריום  31.263478  34.747607\n",
       "34006    50186           כביש 25/מדבריום  31.264262  34.746768\n",
       "\n",
       "[34007 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['stop_id', 'stop_name', 'stop_lat', 'stop_lon']\n",
    "pd.read_csv('data/gtfs/stops.txt', usecols=cols)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the `'shapes.txt'` table contains the trip geometries. It is linked with the `'trips.txt'` table via the common `'shape_id'` column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shape_id</th>\n",
       "      <th>shape_pt_lat</th>\n",
       "      <th>shape_pt_lon</th>\n",
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       "      <th>7546509</th>\n",
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       "    <tr>\n",
       "      <th>7546510</th>\n",
       "      <td>146260</td>\n",
       "      <td>32.084884</td>\n",
       "      <td>34.796040</td>\n",
       "      <td>954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7546511</th>\n",
       "      <td>146260</td>\n",
       "      <td>32.084908</td>\n",
       "      <td>34.795895</td>\n",
       "      <td>955</td>\n",
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       "    <tr>\n",
       "      <th>7546512</th>\n",
       "      <td>146260</td>\n",
       "      <td>32.084039</td>\n",
       "      <td>34.795698</td>\n",
       "      <td>956</td>\n",
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       "    <tr>\n",
       "      <th>7546513</th>\n",
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       "      <td>34.795645</td>\n",
       "      <td>957</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>7546514 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         shape_id  shape_pt_lat  shape_pt_lon  shape_pt_sequence\n",
       "0           69895     32.164723     34.848813                  1\n",
       "1           69895     32.164738     34.848972                  2\n",
       "2           69895     32.164771     34.849177                  3\n",
       "3           69895     32.164841     34.849429                  4\n",
       "4           69895     32.164889     34.849626                  5\n",
       "...           ...           ...           ...                ...\n",
       "7546509    146260     32.084831     34.796385                953\n",
       "7546510    146260     32.084884     34.796040                954\n",
       "7546511    146260     32.084908     34.795895                955\n",
       "7546512    146260     32.084039     34.795698                956\n",
       "7546513    146260     32.083737     34.795645                957\n",
       "\n",
       "[7546514 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['shape_id', 'shape_pt_lat', 'shape_pt_lon', 'shape_pt_sequence']\n",
    "pd.read_csv('data/gtfs/shapes.txt', usecols=cols)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relations between the six above-mentioned files in the GTFS dataset are summarized in {numref}`gtfs-files-drawing`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} images/gtfs_files_drawing.svg\n",
    "---\n",
    "name: gtfs-files-drawing\n",
    "---\n",
    "Relation between the six GTFS files listed in {numref}`gtfs-files`\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas2-gtfs-example)=\n",
    "### GTFS example"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have an idea about the structure and purpose of the GTFS dataset, let's demonstrate join operations between its tables. This is a classical example where joining tables is essential: you cannot go very far with GTFS data analysis, or processing, without joining the various tables together (see {numref}`gtfs-files-drawing`)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make things simple, in this example, consider just two of the tables in the GTFS dataset:\n",
    "\n",
    "* `'agency.txt'`, where rows represent public transport *operators*\n",
    "* `'routes.txt'`, where rows represent public transport *routes*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown above (see {ref}`what-is-gtfs`), the two tables share a common column `'agency_id'`, which we can use to join the two, to find out which public routes belong to each operator."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import the `'agency.txt'` table and subset the columns of interest, `'agency_id'` and `'agency_name'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>36 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    agency_id             agency_name\n",
       "0           2              רכבת ישראל\n",
       "1           3                     אגד\n",
       "2           4    אלקטרה אפיקים תחבורה\n",
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       "4           6                   ש.א.מ\n",
       "..        ...                     ...\n",
       "31         51  ירושלים-צור באהר איחוד\n",
       "32         91          מוניות מטרו קו\n",
       "33         93    מוניות מאיה יצחק שדה\n",
       "34         97       אודליה מוניות בעמ\n",
       "35         98     מוניות רב קווית 4-5\n",
       "\n",
       "[36 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['agency_id', 'agency_name']\n",
    "agency = pd.read_csv('data/gtfs/agency.txt', usecols=cols)\n",
    "agency"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we import the `'routes.txt'` table, this time selecting the `'route_id'`, `'agency_id'`, `'route_short_name'`, and `'route_long_name'` columns. Note the common `'agency_id'` column, which we are going to use when joining the two tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      route_id  agency_id route_short_name                     route_long_name\n",
       "0            1         25                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...\n",
       "1            2         25                1  ת. רכבת יבנה מזרח-יבנה<->ת. רכב...\n",
       "2            3         25                2  ת. רכבת יבנה מערב-יבנה<->ת. רכב...\n",
       "3            4         25               2א  ת. רכבת יבנה מערב-יבנה<->ת. רכב...\n",
       "4            5         25                2  ת. רכבת יבנה מזרח-יבנה<->ת. רכב...\n",
       "...        ...        ...              ...                                 ...\n",
       "8183     37315          2              NaN  תא אוניברסיטה-תל אביב יפו<->באר...\n",
       "8184     37316          2              NaN  תא אוניברסיטה-תל אביב יפו<->הרצ...\n",
       "8185     37342          6               46  גן טכנולוגי-נצרת<->מסרארה חט''ב...\n",
       "8186     37343          6               46  גן טכנולוגי-נצרת<->מסרארה חט''ב...\n",
       "8187     37350          2              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה\n",
       "\n",
       "[8188 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['route_id', 'agency_id', 'route_short_name', 'route_long_name']\n",
    "routes = pd.read_csv('data/gtfs/routes.txt', usecols=cols)\n",
    "routes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can join the `routes` and `agency` tables based on the common `'agency_id'` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      route_id  agency_id route_short_name  \\\n",
       "0            1         25                1   \n",
       "1            2         25                1   \n",
       "2            3         25                2   \n",
       "3            4         25               2א   \n",
       "4            5         25                2   \n",
       "...        ...        ...              ...   \n",
       "8183     37315          2              NaN   \n",
       "8184     37316          2              NaN   \n",
       "8185     37342          6               46   \n",
       "8186     37343          6               46   \n",
       "8187     37350          2              NaN   \n",
       "\n",
       "                         route_long_name    agency_name  \n",
       "0     ת. רכבת יבנה מערב-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "1     ת. רכבת יבנה מזרח-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "2     ת. רכבת יבנה מערב-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "3     ת. רכבת יבנה מערב-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "4     ת. רכבת יבנה מזרח-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "...                                  ...            ...  \n",
       "8183  תא אוניברסיטה-תל אביב יפו<->באר...     רכבת ישראל  \n",
       "8184  תא אוניברסיטה-תל אביב יפו<->הרצ...     רכבת ישראל  \n",
       "8185  גן טכנולוגי-נצרת<->מסרארה חט''ב...          ש.א.מ  \n",
       "8186  גן טכנולוגי-נצרת<->מסרארה חט''ב...          ש.א.מ  \n",
       "8187  באר שבע מרכז-באר שבע<->נהריה-נהריה     רכבת ישראל  \n",
       "\n",
       "[8188 rows x 5 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes = pd.merge(routes, agency, on='agency_id', how='left')\n",
    "routes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the additional `'agency_name'` column in the `routes` table, which tells us the name of the operator for each route."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "For more information and examples of `pd.merge`, check out the following official tutorials from the `pandas` documentation: \n",
    "* [Join tables using a common identifier](https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/08_combine_dataframes.html#join-tables-using-a-common-identifier)\n",
    "* [Database-style `DataFrame` or named `Series` joining/merging](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging)\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas-aggregation)=\n",
    "## Aggregation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aggregation is a commonly used to summarize tabular data. In an aggregation procedure, a function is applied on *subsets* of table rows, whereas subsets are specified through one or more grouping variables, to obtain a new (smaller) table with summaries per group. Aggregation is one of the techniques following the [split-apply-combine](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html) concept."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `pandas`, there are several different ways of specifying the function(s) to be applied on the table column(s). We will go over three commonly used scenarios:\n",
    "\n",
    "* {ref}`aggregation-same-function-on-all-columns`\n",
    "* {ref}`aggregation-different-functions` on each column\n",
    "* {ref}`aggregate-custom-functions`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterwards, we are going to introduce a useful shortcut for a particularly common type of aggregation: counting the occurence of each unique value in a column (see {ref}`pandas-value-counts`)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(aggregation-same-function-on-all-columns)=\n",
    "### Same function on all columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a minimal example of aggregation, let's go back to the small `stations` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva Center</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>34.798443</td>\n",
       "      <td>31.243288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name        city  lines  piano        lon        lat\n",
       "0      Beer-Sheva Center  Beer-Sheva      4  False  34.798443  31.243288\n",
       "1  Beer-Sheva University  Beer-Sheva      5   True  34.812831  31.260284\n",
       "2                 Dimona      Dimona      1  False  35.011635  31.068616"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose that we are interested in the number of lines (the `lines` column) and the number of stations with a piano (the `piano` column), which involves summing the values in those two columns. We already know how the *totals*, i.e., sums of each column, can be calculated (see {ref}`pandas-row-col-wise-operations`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lines    10\n",
       "piano     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations[['lines', 'piano']].sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, what if we wanted to calculate the number of lines and stations with a piano per town, *separately*? To do that, we basically need to *split* the table according to the `'city'` column, then apply the `.sum` method on each subset. Here is how this can be done. Instead of simply using `.sum`, we split (or \"group\") the table by `'city'`, and only then apply the `.sum` method. Splitting is done using the [`.groupby`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html) method, which accepts the column name (or `list` of column names) that specifies the grouping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Beer-Sheva</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimona</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            lines  piano\n",
       "city                    \n",
       "Beer-Sheva      9      1\n",
       "Dimona          1      0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.groupby('city')[['lines', 'piano']].sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the grouping variable(s) become the *index* of the resulting `DataFrame`. For our purposes, we are usually going to prefer to work with the grouping variable in its own column. In such case, we end the expression with `.reset_index()` (see {ref}`resetting-the-index`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         city  lines  piano\n",
       "0  Beer-Sheva      9      1\n",
       "1      Dimona      1      0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.groupby('city')[['lines', 'piano']].sum().reset_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "Tables can also be grouped by two or more columns, in which case the column names are passed in a `list`, as in `dat.groupby(['a','b'])`, where `'a'` and `'b'` are column names in `dat`.\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.sum` method is just one example, there are many other useful functions we can use, such as the ones listed in {numref}`pandas-aggregation-methods`. Additional methods specific to `.groupby` are `.first` and `.last`, to select the first and last row per group. For example, to get the maximum per group we can combine `.groupby` with `.max`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>name</th>\n",
       "      <th>lines</th>\n",
       "      <th>piano</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beer-Sheva</td>\n",
       "      <td>Beer-Sheva University</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>34.812831</td>\n",
       "      <td>31.260284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dimona</td>\n",
       "      <td>Dimona</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>35.011635</td>\n",
       "      <td>31.068616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         city                   name  lines  piano        lon        lat\n",
       "0  Beer-Sheva  Beer-Sheva University      5   True  34.812831  31.260284\n",
       "1      Dimona                 Dimona      1  False  35.011635  31.068616"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.groupby('city').max().reset_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more realistic example, consider the `routes` table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>route_id</th>\n",
       "      <th>agency_id</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "      <th>agency_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>ת. רכבת יבנה מערב-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "      <td>אלקטרה אפיקים</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>ת. רכבת יבנה מזרח-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "      <td>אלקטרה אפיקים</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>ת. רכבת יבנה מערב-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "      <td>אלקטרה אפיקים</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>2א</td>\n",
       "      <td>ת. רכבת יבנה מערב-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "      <td>אלקטרה אפיקים</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>ת. רכבת יבנה מזרח-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "      <td>אלקטרה אפיקים</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8183</th>\n",
       "      <td>37315</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>תא אוניברסיטה-תל אביב יפו&lt;-&gt;באר...</td>\n",
       "      <td>רכבת ישראל</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8184</th>\n",
       "      <td>37316</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>תא אוניברסיטה-תל אביב יפו&lt;-&gt;הרצ...</td>\n",
       "      <td>רכבת ישראל</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8185</th>\n",
       "      <td>37342</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>גן טכנולוגי-נצרת&lt;-&gt;מסרארה חט''ב...</td>\n",
       "      <td>ש.א.מ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8186</th>\n",
       "      <td>37343</td>\n",
       "      <td>6</td>\n",
       "      <td>46</td>\n",
       "      <td>גן טכנולוגי-נצרת&lt;-&gt;מסרארה חט''ב...</td>\n",
       "      <td>ש.א.מ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8187</th>\n",
       "      <td>37350</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>באר שבע מרכז-באר שבע&lt;-&gt;נהריה-נהריה</td>\n",
       "      <td>רכבת ישראל</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8188 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      route_id  agency_id route_short_name  \\\n",
       "0            1         25                1   \n",
       "1            2         25                1   \n",
       "2            3         25                2   \n",
       "3            4         25               2א   \n",
       "4            5         25                2   \n",
       "...        ...        ...              ...   \n",
       "8183     37315          2              NaN   \n",
       "8184     37316          2              NaN   \n",
       "8185     37342          6               46   \n",
       "8186     37343          6               46   \n",
       "8187     37350          2              NaN   \n",
       "\n",
       "                         route_long_name    agency_name  \n",
       "0     ת. רכבת יבנה מערב-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "1     ת. רכבת יבנה מזרח-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "2     ת. רכבת יבנה מערב-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "3     ת. רכבת יבנה מערב-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "4     ת. רכבת יבנה מזרח-יבנה<->ת. רכב...  אלקטרה אפיקים  \n",
       "...                                  ...            ...  \n",
       "8183  תא אוניברסיטה-תל אביב יפו<->באר...     רכבת ישראל  \n",
       "8184  תא אוניברסיטה-תל אביב יפו<->הרצ...     רכבת ישראל  \n",
       "8185  גן טכנולוגי-נצרת<->מסרארה חט''ב...          ש.א.מ  \n",
       "8186  גן טכנולוגי-נצרת<->מסרארה חט''ב...          ש.א.מ  \n",
       "8187  באר שבע מרכז-באר שבע<->נהריה-נהריה     רכבת ישראל  \n",
       "\n",
       "[8188 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use aggregation with the `.nunique` function ({numref}`pandas-aggregation-methods`) to calculate the number of unique route IDs that each public transport agency operates, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency_name</th>\n",
       "      <th>route_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>אגד</td>\n",
       "      <td>1621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>אודליה מוניות בעמ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>אלקטרה אפיקים</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>אלקטרה אפיקים תחבורה</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>אקסטרה</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>קווים</td>\n",
       "      <td>1161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>רכבת ישראל</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ש.א.מ</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>תבל</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>תנופה</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             agency_name  route_id\n",
       "0                    אגד      1621\n",
       "1      אודליה מוניות בעמ         2\n",
       "2          אלקטרה אפיקים       413\n",
       "3   אלקטרה אפיקים תחבורה       297\n",
       "4                 אקסטרה        65\n",
       "..                   ...       ...\n",
       "31                 קווים      1161\n",
       "32            רכבת ישראל      1008\n",
       "33                 ש.א.מ       158\n",
       "34                   תבל         5\n",
       "35                 תנופה       287\n",
       "\n",
       "[36 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = routes.groupby('agency_name')['route_id'].nunique().reset_index()\n",
    "tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find out which agencies have the most routes, the resulting table can be sorted using `.sort_values` combined with `ascending=False` (see {ref}`pandas-sorting`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency_name</th>\n",
       "      <th>route_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>אגד</td>\n",
       "      <td>1621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>קווים</td>\n",
       "      <td>1161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>רכבת ישראל</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>מטרופולין</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>נתיב אקספרס</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>כפיר</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>כרמלית</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>כבל אקספרס</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>אודליה מוניות בעמ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>מוניות מאיה יצחק שדה</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             agency_name  route_id\n",
       "0                    אגד      1621\n",
       "31                 קווים      1161\n",
       "32            רכבת ישראל      1008\n",
       "27             מטרופולין       691\n",
       "29           נתיב אקספרס       577\n",
       "..                   ...       ...\n",
       "20                  כפיר         3\n",
       "21                כרמלית         2\n",
       "19            כבל אקספרס         2\n",
       "1      אודליה מוניות בעמ         2\n",
       "22  מוניות מאיה יצחק שדה         1\n",
       "\n",
       "[36 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.sort_values('route_id', ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 06-c\n",
    ":class: important\n",
    "* Calculate a series of global temperature means for each *decade*, based on the information in `ZonAnn.Ts+dSST.csv`. \n",
    "* First, you need to create a new variable named `decade` based on the `'Year'`, using an expression such as the one shown below.\n",
    "* Second, use `.groupby` to calculate the mean global temperature in each decade.\n",
    "\n",
    "```py\n",
    "dat['Year'] // 10 * 10\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 06-d\n",
    ":class: important\n",
    "* The `'routes.txt'` table describes public transport routes (uniquely identified by `route_id`). The `'trips.txt'` table, however, descibes specific journeys of a along those routes, at a specific time of day (uniquely identified by `trip_id`). The two tables are associated with one another via the common `route_id` column (see {ref}`what-is-gtfs`). For example, a route that operates five times a day will be represented in one row in the `'routes.txt'` table, and five rows in the `'trips.txt'` table.\n",
    "* Join `'routes.txt'` to `'trips.txt'`, using the common `route_id` column.\n",
    "* Calculate a table with the number of unique `trip_id`'s per `route_id`.\n",
    "* Sort the table according to number of unique trips, and print a summary with the numbers and names (`route_short_name` and `route_long_name` columns from `routes.txt`, respectively) of the most and least frequent routes ({numref}`exercise-06-c`).\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} exercise-06-c\n",
    "---\n",
    "name: exercise-06-c\n",
    "---\n",
    "Solution of exercise-06-c\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(aggregation-different-functions)=\n",
    "### Different functions (`agg`)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes we need to aggregate a `DataFrame` while applying a different function on each column, rather than the same function on all columns. For the next example, consider the `'world_cities.csv'` file, which has information about major cities in the world:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>pop</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'Abasan al-Jadidah</td>\n",
       "      <td>Palestine</td>\n",
       "      <td>5629</td>\n",
       "      <td>31.31</td>\n",
       "      <td>34.34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'Abasan al-Kabirah</td>\n",
       "      <td>Palestine</td>\n",
       "      <td>18999</td>\n",
       "      <td>31.32</td>\n",
       "      <td>34.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'Abdul Hakim</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>47788</td>\n",
       "      <td>30.55</td>\n",
       "      <td>72.11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'Abdullah-as-Salam</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>21817</td>\n",
       "      <td>29.36</td>\n",
       "      <td>47.98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'Abud</td>\n",
       "      <td>Palestine</td>\n",
       "      <td>2456</td>\n",
       "      <td>32.03</td>\n",
       "      <td>35.07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43640</th>\n",
       "      <td>az-Zubayr</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>124611</td>\n",
       "      <td>30.39</td>\n",
       "      <td>47.71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43641</th>\n",
       "      <td>az-Zulfi</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>54070</td>\n",
       "      <td>26.30</td>\n",
       "      <td>44.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43642</th>\n",
       "      <td>az-Zuwaytinah</td>\n",
       "      <td>Libya</td>\n",
       "      <td>21984</td>\n",
       "      <td>30.95</td>\n",
       "      <td>20.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43643</th>\n",
       "      <td>s-Gravenhage</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>479525</td>\n",
       "      <td>52.07</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43644</th>\n",
       "      <td>s-Hertogenbosch</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>135529</td>\n",
       "      <td>51.68</td>\n",
       "      <td>5.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43645 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     city       country     pop    lat    lon  capital\n",
       "0      'Abasan al-Jadidah     Palestine    5629  31.31  34.34        0\n",
       "1      'Abasan al-Kabirah     Palestine   18999  31.32  34.35        0\n",
       "2            'Abdul Hakim      Pakistan   47788  30.55  72.11        0\n",
       "3      'Abdullah-as-Salam        Kuwait   21817  29.36  47.98        0\n",
       "4                   'Abud     Palestine    2456  32.03  35.07        0\n",
       "...                   ...           ...     ...    ...    ...      ...\n",
       "43640           az-Zubayr          Iraq  124611  30.39  47.71        0\n",
       "43641            az-Zulfi  Saudi Arabia   54070  26.30  44.80        0\n",
       "43642       az-Zuwaytinah         Libya   21984  30.95  20.12        0\n",
       "43643        s-Gravenhage   Netherlands  479525  52.07   4.30        0\n",
       "43644     s-Hertogenbosch   Netherlands  135529  51.68   5.30        0\n",
       "\n",
       "[43645 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = pd.read_csv('data/world_cities.csv')\n",
    "cities"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose we want to summarize the information per country (`'country'` column). However, we want each column to be treated differently:\n",
    "\n",
    "* The city names (`'city'` column) will be *counted* \n",
    "* Population (`'pop'` column) will be *summed*\n",
    "* Longitude and latitude (`'lon'` and `'lat'` columns, respectively) will be *averaged*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be done using the [`.agg`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.agg.html) method applied on a *grouped* `DataFrame` instead of a specific method (such as `.nunique`). The `.agg` method accepts a dictionary, with `key:value` pairs of the form `'column':function`, where: \n",
    "\n",
    "* `'column'`—Name of the column to be aggregated\n",
    "* `function`—The function to apply on each group in that column. This can be:\n",
    "    * a *string* referring to a `pandas` method (such as `'nunique'`, see {numref}`pandas-aggregation-methods`)\n",
    "    * a standalone *function* that can be applied on a `Series`, such as `np.mean` or `np.sum`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our case, the aggregation expression is as follows. As a result, we get a new table where each row represents a country, with the count of unique city names, sum of the population across all cities, and average longitute and latitude across all cities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_55311/2985202954.py:1: FutureWarning: The provided callable <function sum at 0x7fb1a40dd510> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead.\n",
      "  cities.groupby('country').agg({\n",
      "/tmp/ipykernel_55311/2985202954.py:1: FutureWarning: The provided callable <function mean at 0x7fb1a40de440> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  cities.groupby('country').agg({\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>pop</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>115</td>\n",
       "      <td>7543856</td>\n",
       "      <td>66.920342</td>\n",
       "      <td>34.807692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>67</td>\n",
       "      <td>1536232</td>\n",
       "      <td>19.975522</td>\n",
       "      <td>41.087463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>315</td>\n",
       "      <td>20508642</td>\n",
       "      <td>3.535443</td>\n",
       "      <td>35.467025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>35</td>\n",
       "      <td>58021</td>\n",
       "      <td>-170.663714</td>\n",
       "      <td>-14.299143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>7</td>\n",
       "      <td>69031</td>\n",
       "      <td>1.538571</td>\n",
       "      <td>42.534286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>23</td>\n",
       "      <td>11380</td>\n",
       "      <td>-176.585652</td>\n",
       "      <td>-13.512609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>4</td>\n",
       "      <td>338786</td>\n",
       "      <td>-13.820000</td>\n",
       "      <td>25.935000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>30</td>\n",
       "      <td>5492077</td>\n",
       "      <td>45.088667</td>\n",
       "      <td>14.481000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>73</td>\n",
       "      <td>4032170</td>\n",
       "      <td>28.179315</td>\n",
       "      <td>-13.404247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>76</td>\n",
       "      <td>4231859</td>\n",
       "      <td>30.442500</td>\n",
       "      <td>-18.629211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  city       pop         lon        lat\n",
       "0          Afghanistan   115   7543856   66.920342  34.807692\n",
       "1              Albania    67   1536232   19.975522  41.087463\n",
       "2              Algeria   315  20508642    3.535443  35.467025\n",
       "3       American Samoa    35     58021 -170.663714 -14.299143\n",
       "4              Andorra     7     69031    1.538571  42.534286\n",
       "..                 ...   ...       ...         ...        ...\n",
       "234  Wallis and Futuna    23     11380 -176.585652 -13.512609\n",
       "235     Western Sahara     4    338786  -13.820000  25.935000\n",
       "236              Yemen    30   5492077   45.088667  14.481000\n",
       "237             Zambia    73   4032170   28.179315 -13.404247\n",
       "238           Zimbabwe    76   4231859   30.442500 -18.629211\n",
       "\n",
       "[239 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.groupby('country').agg({\n",
    "    'city': 'nunique', \n",
    "    'pop': np.sum, \n",
    "    'lon': np.mean, \n",
    "    'lat': np.mean\n",
    "}).reset_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(aggregate-custom-functions)=\n",
    "### Custom functions (`agg`)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, sometimes we need to apply a custom function when aggregating a table. For example, suppose that we need to calculate the population difference between the most populated, and least populated, cities, in each country. To do that, we can define our own function, named `diff_max_min`, which: \n",
    "\n",
    "* accepts a `Series` named `x`, and \n",
    "* returns the difference between the maximum and minimum, `x.max()-x.min()`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the function definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diff_max_min(x):\n",
    "    return x.max() - x.min()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is worthwhile to test the function, to make sure it works as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_max_min(pd.Series([15, 20, 4, 12, 6]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can use the function inside the `.agg` expression, just like any other predefined function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>115</td>\n",
       "      <td>3117579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>67</td>\n",
       "      <td>379803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>315</td>\n",
       "      <td>2024344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>35</td>\n",
       "      <td>11341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>7</td>\n",
       "      <td>17740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>23</td>\n",
       "      <td>1142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>4</td>\n",
       "      <td>146906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>30</td>\n",
       "      <td>1918607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>73</td>\n",
       "      <td>1305210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>76</td>\n",
       "      <td>1574930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  city      pop\n",
       "0          Afghanistan   115  3117579\n",
       "1              Albania    67   379803\n",
       "2              Algeria   315  2024344\n",
       "3       American Samoa    35    11341\n",
       "4              Andorra     7    17740\n",
       "..                 ...   ...      ...\n",
       "234  Wallis and Futuna    23     1142\n",
       "235     Western Sahara     4   146906\n",
       "236              Yemen    30  1918607\n",
       "237             Zambia    73  1305210\n",
       "238           Zimbabwe    76  1574930\n",
       "\n",
       "[239 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.groupby('country').agg({\n",
    "    'city': 'nunique', \n",
    "    'pop': diff_max_min\n",
    "}).reset_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, here is how the \"new\" pop value is obtained for the first country (`'Afghanistan'`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3117579"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_max_min(cities[cities['country'] == 'Afghanistan']['pop'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use a lambda function (see {ref}`lambda-functions`), to do the same, using more concise syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>115</td>\n",
       "      <td>3117579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>67</td>\n",
       "      <td>379803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>315</td>\n",
       "      <td>2024344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>35</td>\n",
       "      <td>11341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>7</td>\n",
       "      <td>17740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>23</td>\n",
       "      <td>1142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>4</td>\n",
       "      <td>146906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>30</td>\n",
       "      <td>1918607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>73</td>\n",
       "      <td>1305210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>76</td>\n",
       "      <td>1574930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>239 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  city      pop\n",
       "0          Afghanistan   115  3117579\n",
       "1              Albania    67   379803\n",
       "2              Algeria   315  2024344\n",
       "3       American Samoa    35    11341\n",
       "4              Andorra     7    17740\n",
       "..                 ...   ...      ...\n",
       "234  Wallis and Futuna    23     1142\n",
       "235     Western Sahara     4   146906\n",
       "236              Yemen    30  1918607\n",
       "237             Zambia    73  1305210\n",
       "238           Zimbabwe    76  1574930\n",
       "\n",
       "[239 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.groupby('country').agg({\n",
    "    'city': 'nunique', \n",
    "    'pop': lambda x: x.max() - x.min()\n",
    "}).reset_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "For more information and examples on aggregation, and other types of split-apply-combine operations which we have not mentioned, see <https://pandas.pydata.org/docs/user_guide/groupby.html#aggregation>.\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(pandas-value-counts)=\n",
    "## Value counts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very common type of aggregation is to calculate how many times each unique value is \"repeated\" in a `Series`. For example, recall the way that we calculated the number of unique routes per agency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency_name</th>\n",
       "      <th>route_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>אגד</td>\n",
       "      <td>1621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>קווים</td>\n",
       "      <td>1161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>רכבת ישראל</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>מטרופולין</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>נתיב אקספרס</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>כפיר</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>כרמלית</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>כבל אקספרס</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>אודליה מוניות בעמ</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>מוניות מאיה יצחק שדה</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             agency_name  route_id\n",
       "0                    אגד      1621\n",
       "31                 קווים      1161\n",
       "32            רכבת ישראל      1008\n",
       "27             מטרופולין       691\n",
       "29           נתיב אקספרס       577\n",
       "..                   ...       ...\n",
       "20                  כפיר         3\n",
       "21                כרמלית         2\n",
       "19            כבל אקספרס         2\n",
       "1      אודליה מוניות בעמ         2\n",
       "22  מוניות מאיה יצחק שדה         1\n",
       "\n",
       "[36 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes \\\n",
    "    .groupby('agency_name')['route_id'] \\\n",
    "    .nunique() \\\n",
    "    .reset_index() \\\n",
    "    .sort_values('route_id', ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [`.value_counts`](https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html) method which can be thought of as a shortcut (assuming that all `route_id` values are unique, which is true in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agency_name         \n",
       "אגד                     1621\n",
       "קווים                   1161\n",
       "רכבת ישראל              1008\n",
       "מטרופולין                691\n",
       "נתיב אקספרס              577\n",
       "                        ... \n",
       "כפיר                       3\n",
       "כרמלית                     2\n",
       "כבל אקספרס                 2\n",
       "אודליה מוניות בעמ          2\n",
       "מוניות מאיה יצחק שדה       1\n",
       "Name: count, Length: 36, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes[['agency_name']].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a `Series` where the index is the agency name, and the values are the counts. It tells us how many rows are there in the `routes` table, for each agency. For example, `'אגד'` is repeated over `1621` rows."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the resuling `Series` is sorted by count, from highest to lowest. In case we need to sort by index, i.e., by agency name, we can use the [`.sort_index`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_index.html) method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agency_name         \n",
       "אגד                     1621\n",
       "אודליה מוניות בעמ          2\n",
       "אלקטרה אפיקים            413\n",
       "אלקטרה אפיקים תחבורה     297\n",
       "אקסטרה                    65\n",
       "                        ... \n",
       "קווים                   1161\n",
       "רכבת ישראל              1008\n",
       "ש.א.מ                    158\n",
       "תבל                        5\n",
       "תנופה                    287\n",
       "Name: count, Length: 36, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes[['agency_name']].value_counts().sort_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 06-e\n",
    ":class: important\n",
    "* How often is there more than one shape (`shape_id`) for the same route (`route_id`)? \n",
    "* Use the `'trips.txt'` table to calculate the number of unique values of `shape_id` for each `route_id`.\n",
    "* Summarize the resulting column to calculate how many `route_id`s has `0`, `1`, `2`, etc. of `shape_id`.\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(exercise-pandas2)=\n",
    "## More exercises"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 06-f\n",
    ":class: important\n",
    "* Read the `'agency.txt'`, `'routes.txt'`, `'trips.txt'`, and `'shapes.txt'` tables (located in the `gtfs` directory).\n",
    "* Subset just the `'דן באר שבע'` operator routes from the `routes` table (first find out its `agency_id` in the `agency` table)\n",
    "* Subset only those `routes` where `'route_short_name'` is `'24'` (i.e., routes of bus `'24'`)\n",
    "* Join the routes table with `trips` and filter to retain just one trip (the first `trip_id`)\n",
    "* Join the trip table with `shapes`\n",
    "* Print the resulting table with the shape coordinates of one of the trips of bus `'24'` ({numref}`exercise-06-f1`)\n",
    "* Create a scatterplot of the trip coordinates, with `'shape_pt_lon'` values on the x-axis and `'shape_pt_lat'` values on the y-axis ({numref}`exercise-06-f2`)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} exercise-06-f1\n",
    "---\n",
    "name: exercise-06-f1\n",
    "---\n",
    "Solution of exercise-06-f: Coordinates of the 1<sup>st</sup> trip of bus `'24'` by ``'דן באר שבע'``\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} exercise-06-f2\n",
    "---\n",
    "name: exercise-06-f2\n",
    "---\n",
    "Solution of exercise-06-f: Coordinates of the 1<sup>st</sup> trip of bus `'24'` by ``'דן באר שבע'``\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Exercise 06-g\n",
    ":class: important\n",
    "* In this exercise, you are going to find the names of the five longest bus routes (in terms of distance traveled) in Israel. To do that, go through the following steps.\n",
    "* Read the `stop_times.txt` table and select just the `'trip_id'` and `'shape_dist_traveled'` columns. \n",
    "* Calculate the maximum (i.e., total) distance traveled (`'shape_dist_traveled'`) for each public transit trip (`'trip_id'`) ({numref}`exercise-06-g1`).\n",
    "* Read the `trips.txt` table and select just the `'trip_id'`, and `'route_id'`.\n",
    "* Join the resulting `trips` table with the `stop_times` table from the previous step (using the common `\"trip_id\"` column), to get the `'route_id'` value of each trip.\n",
    "* Retain just one (the first) trip per `'route_id'` ({numref}`exercise-06-g2`).\n",
    "* Read the `routes.txt` table and subset the `'route_id'`, `'route_short_name'`, and `'route_long_name'` columns.\n",
    "* Join the resulting table with the trips table (using the common `'route_id'` column), to get the `'route_short_name'` and `'route_long_name'` values of each route ({numref}`exercise-06-g3`).\n",
    "* Sort the table according to `'shape_dist_traveled'` (from largest to smallest), and print the first five rows ({numref}`exercise-06-g4`). Those are the longest-distance public transport routes in the GTFS dataset.\n",
    "* Convert the `'route_long_name'` values of the first five rows from the previous step, collect them into a `list`, and print the `list`, to see the full names of the routes. You can see that all five routes are from Haifa to Eilat or the other way around.\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} exercise-06-g1\n",
    "---\n",
    "name: exercise-06-g1\n",
    "---\n",
    "Solution of exercise-06-g: Total distance traveled in each trip, based on `stop_times.txt`\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} exercise-06-g2\n",
    "---\n",
    "name: exercise-06-g2\n",
    "---\n",
    "Solution of exercise-06-g: Joined with `trips.txt`, selecting just the 1<sup>st</sup> trips per `route_id`\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} exercise-06-g3\n",
    "---\n",
    "name: exercise-06-g3\n",
    "---\n",
    "Solution of exercise-06-g: Joined with `routes.txt`\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{glue:figure} exercise-06-g4\n",
    "---\n",
    "name: exercise-06-g4\n",
    "---\n",
    "Solution of exercise-06-g: Sorted by `shape_dist_traveled`, from largest to smallest\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "from myst_nb import glue"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 06-b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006830158347399733"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# Read data\n",
    "dat = pd.read_csv('data/ZonAnn.Ts+dSST.csv')\n",
    "cols = ['90S-64S', '64S-44S', '44S-24S', '24S-EQU', 'EQU-24N', '24N-44N', '44N-64N', '64N-90N']\n",
    "regions = dat[cols]\n",
    "# Function to calculate linear slope\n",
    "def f(x, y): \n",
    "    return np.polyfit(x, y, 1)[0]\n",
    "f(dat['Year'], regions['44S-24S'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90S-64S    0.006262\n",
       "64S-44S    0.005188\n",
       "44S-24S    0.006830\n",
       "24S-EQU    0.006690\n",
       "EQU-24N    0.006562\n",
       "24N-44N    0.008564\n",
       "44N-64N    0.012204\n",
       "64N-90N    0.018884\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate slopes per region\n",
    "regions.apply(lambda i: f(dat['Year'], i), axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 06-d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": [
     "remove-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>route_id</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "      <th>trip_id</th>\n",
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       "      <td>89</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2719</th>\n",
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       "      <td>89</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2717</th>\n",
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       "      <td>88</td>\n",
       "      <td>דרך צמח השדה/העירית-מעלה אדומים...</td>\n",
       "      <td>1</td>\n",
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       "      <th>5651</th>\n",
       "      <td>23676</td>\n",
       "      <td>303</td>\n",
       "      <td>הרב שלום שבזי/שילה-ראש העין&lt;-&gt;י...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3590</th>\n",
       "      <td>15053</td>\n",
       "      <td>91</td>\n",
       "      <td>מעונות הסטודנטים/הנשיא-צפת&lt;-&gt;הא...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7180 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      route_id route_short_name                     route_long_name  trip_id\n",
       "2837     11685                1  ת. מרכזית חוף הכרמל/רציפים עירו...     1344\n",
       "2839     11689                1  מרכזית הקריות-קרית מוצקין<->ת. ...     1300\n",
       "6464     29602               10  מרכזית המפרץ-חיפה<->אוניברסיטת ...     1201\n",
       "6465     29603               10  אוניברסיטת חיפה-חיפה<->מרכזית ה...     1201\n",
       "3192     12405               15  תחנה תפעולית/ביטוח לאומי-ירושלי...      978\n",
       "...        ...              ...                                 ...      ...\n",
       "2720     11411               89  פרי מגדים/חוט השני-מעלה אדומים<...        1\n",
       "2719     11410               89  דרך צמח השדה/העירית-מעלה אדומים...        1\n",
       "2717     11408               88  דרך צמח השדה/העירית-מעלה אדומים...        1\n",
       "5651     23676              303  הרב שלום שבזי/שילה-ראש העין<->י...        1\n",
       "3590     15053               91  מעונות הסטודנטים/הנשיא-צפת<->הא...        1\n",
       "\n",
       "[7180 rows x 4 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Read\n",
    "routes = pd.read_csv('data/gtfs/routes.txt')\n",
    "trips = pd.read_csv('data/gtfs/trips.txt')\n",
    "# Join\n",
    "dat = pd.merge(trips, routes, on='route_id', how='left')\n",
    "# Calculate number of unique trips per route\n",
    "dat = dat.groupby(['route_id', 'route_short_name', 'route_long_name']).nunique()['trip_id'].reset_index()\n",
    "# Sort\n",
    "dat = dat.sort_values('trip_id', ascending=False)\n",
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>route_long_name</th>\n",
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       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>2720</th>\n",
       "      <td>11411</td>\n",
       "      <td>89</td>\n",
       "      <td>פרי מגדים/חוט השני-מעלה אדומים&lt;...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2719</th>\n",
       "      <td>11410</td>\n",
       "      <td>89</td>\n",
       "      <td>דרך צמח השדה/העירית-מעלה אדומים...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2717</th>\n",
       "      <td>11408</td>\n",
       "      <td>88</td>\n",
       "      <td>דרך צמח השדה/העירית-מעלה אדומים...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5651</th>\n",
       "      <td>23676</td>\n",
       "      <td>303</td>\n",
       "      <td>הרב שלום שבזי/שילה-ראש העין&lt;-&gt;י...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3590</th>\n",
       "      <td>15053</td>\n",
       "      <td>91</td>\n",
       "      <td>מעונות הסטודנטים/הנשיא-צפת&lt;-&gt;הא...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7180 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      route_id route_short_name                     route_long_name  trip_id\n",
       "2837     11685                1  ת. מרכזית חוף הכרמל/רציפים עירו...     1344\n",
       "2839     11689                1  מרכזית הקריות-קרית מוצקין<->ת. ...     1300\n",
       "6464     29602               10  מרכזית המפרץ-חיפה<->אוניברסיטת ...     1201\n",
       "6465     29603               10  אוניברסיטת חיפה-חיפה<->מרכזית ה...     1201\n",
       "3192     12405               15  תחנה תפעולית/ביטוח לאומי-ירושלי...      978\n",
       "...        ...              ...                                 ...      ...\n",
       "2720     11411               89  פרי מגדים/חוט השני-מעלה אדומים<...        1\n",
       "2719     11410               89  דרך צמח השדה/העירית-מעלה אדומים...        1\n",
       "2717     11408               88  דרך צמח השדה/העירית-מעלה אדומים...        1\n",
       "5651     23676              303  הרב שלום שבזי/שילה-ראש העין<->י...        1\n",
       "3590     15053               91  מעונות הסטודנטים/הנשיא-צפת<->הא...        1\n",
       "\n",
       "[7180 rows x 4 columns]"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "exercise-06-c"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glue('exercise-06-c', dat)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 06-e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": [
     "remove-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shape_id\n",
       "0    1008\n",
       "1    7016\n",
       "2     164\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "trips = pd.read_csv('data/gtfs/trips.txt')\n",
    "trips.groupby('route_id')['shape_id'].nunique().value_counts().sort_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(exercise-06-f)=\n",
    "### Exercise 06-f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>route_long_name</th>\n",
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       "      <th>shape_pt_lon</th>\n",
       "      <th>shape_pt_lat</th>\n",
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       "      <td>2</td>\n",
       "      <td>34.797988</td>\n",
       "      <td>31.242103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>3</td>\n",
       "      <td>34.797982</td>\n",
       "      <td>31.242829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>4</td>\n",
       "      <td>34.797986</td>\n",
       "      <td>31.242980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>5</td>\n",
       "      <td>34.798000</td>\n",
       "      <td>31.243121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>857</td>\n",
       "      <td>34.822166</td>\n",
       "      <td>31.280183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>858</td>\n",
       "      <td>34.822116</td>\n",
       "      <td>31.280225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>859</td>\n",
       "      <td>34.822038</td>\n",
       "      <td>31.280246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>860</td>\n",
       "      <td>34.821949</td>\n",
       "      <td>31.280250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "      <td>861</td>\n",
       "      <td>34.821860</td>\n",
       "      <td>31.280233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>861 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     route_id route_short_name                     route_long_name  \\\n",
       "0       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "1       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "2       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "3       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "4       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "..        ...              ...                                 ...   \n",
       "856     17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "857     17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "858     17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "859     17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "860     17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "\n",
       "             trip_id  shape_id  shape_pt_sequence  shape_pt_lon  shape_pt_lat  \n",
       "0    26385618_011023  136236.0                  1     34.797979     31.242009  \n",
       "1    26385618_011023  136236.0                  2     34.797988     31.242103  \n",
       "2    26385618_011023  136236.0                  3     34.797982     31.242829  \n",
       "3    26385618_011023  136236.0                  4     34.797986     31.242980  \n",
       "4    26385618_011023  136236.0                  5     34.798000     31.243121  \n",
       "..               ...       ...                ...           ...           ...  \n",
       "856  26385618_011023  136236.0                857     34.822166     31.280183  \n",
       "857  26385618_011023  136236.0                858     34.822116     31.280225  \n",
       "858  26385618_011023  136236.0                859     34.822038     31.280246  \n",
       "859  26385618_011023  136236.0                860     34.821949     31.280250  \n",
       "860  26385618_011023  136236.0                861     34.821860     31.280233  \n",
       "\n",
       "[861 rows x 8 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Read\n",
    "agency = pd.read_csv('data/gtfs/agency.txt')\n",
    "routes = pd.read_csv('data/gtfs/routes.txt')\n",
    "trips = pd.read_csv('data/gtfs/trips.txt')\n",
    "shapes = pd.read_csv('data/gtfs/shapes.txt')\n",
    "# Subset\n",
    "agency = agency[['agency_id', 'agency_name']]\n",
    "routes = routes[['route_id', 'agency_id', 'route_short_name', 'route_long_name']]\n",
    "trips = trips[['trip_id', 'route_id', 'shape_id']]\n",
    "shapes = shapes[['shape_id', 'shape_pt_sequence', 'shape_pt_lon', 'shape_pt_lat']]\n",
    "# Filter agency\n",
    "agency = agency[agency['agency_name'] == 'דן באר שבע']\n",
    "routes = routes[routes['agency_id'] == agency['agency_id'].iloc[0]]\n",
    "routes = routes.drop('agency_id', axis=1)\n",
    "# Filter route\n",
    "routes = routes[routes['route_short_name'] == '24']\n",
    "# Filter trip\n",
    "routes = pd.merge(routes, trips, on='route_id', how='left')\n",
    "trip = routes[routes['trip_id'] == routes['trip_id'].iloc[0]]\n",
    "# Join with 'shapes'\n",
    "trip = pd.merge(trip, shapes, on='shape_id', how='left')\n",
    "trip = trip.sort_values(by='shape_pt_sequence')\n",
    "trip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>route_id</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_long_name</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>shape_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385618_011023</td>\n",
       "      <td>136236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>26385620_011023</td>\n",
       "      <td>136236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>25887153_011023</td>\n",
       "      <td>136236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>29025527_011023</td>\n",
       "      <td>136236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17537</td>\n",
       "      <td>24</td>\n",
       "      <td>ת.מרכזית/עירוניים לצפון-באר שבע...</td>\n",
       "      <td>25887168_011023</td>\n",
       "      <td>136236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>17538</td>\n",
       "      <td>24</td>\n",
       "      <td>מסוף רמות-באר שבע&lt;-&gt;ת.מרכזית/עי...</td>\n",
       "      <td>22834961_011023</td>\n",
       "      <td>127348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>17538</td>\n",
       "      <td>24</td>\n",
       "      <td>מסוף רמות-באר שבע&lt;-&gt;ת.מרכזית/עי...</td>\n",
       "      <td>22834957_011023</td>\n",
       "      <td>127348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>17538</td>\n",
       "      <td>24</td>\n",
       "      <td>מסוף רמות-באר שבע&lt;-&gt;ת.מרכזית/עי...</td>\n",
       "      <td>26385928_011023</td>\n",
       "      <td>127348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>17538</td>\n",
       "      <td>24</td>\n",
       "      <td>מסוף רמות-באר שבע&lt;-&gt;ת.מרכזית/עי...</td>\n",
       "      <td>26385947_011023</td>\n",
       "      <td>127348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>17538</td>\n",
       "      <td>24</td>\n",
       "      <td>מסוף רמות-באר שבע&lt;-&gt;ת.מרכזית/עי...</td>\n",
       "      <td>26385948_011023</td>\n",
       "      <td>127348.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     route_id route_short_name                     route_long_name  \\\n",
       "0       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "1       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "2       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "3       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "4       17537               24  ת.מרכזית/עירוניים לצפון-באר שבע...   \n",
       "..        ...              ...                                 ...   \n",
       "142     17538               24  מסוף רמות-באר שבע<->ת.מרכזית/עי...   \n",
       "143     17538               24  מסוף רמות-באר שבע<->ת.מרכזית/עי...   \n",
       "144     17538               24  מסוף רמות-באר שבע<->ת.מרכזית/עי...   \n",
       "145     17538               24  מסוף רמות-באר שבע<->ת.מרכזית/עי...   \n",
       "146     17538               24  מסוף רמות-באר שבע<->ת.מרכזית/עי...   \n",
       "\n",
       "             trip_id  shape_id  \n",
       "0    26385618_011023  136236.0  \n",
       "1    26385620_011023  136236.0  \n",
       "2    25887153_011023  136236.0  \n",
       "3    29025527_011023  136236.0  \n",
       "4    25887168_011023  136236.0  \n",
       "..               ...       ...  \n",
       "142  22834961_011023  127348.0  \n",
       "143  22834957_011023  127348.0  \n",
       "144  26385928_011023  127348.0  \n",
       "145  26385947_011023  127348.0  \n",
       "146  26385948_011023  127348.0  \n",
       "\n",
       "[147 rows x 5 columns]"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "exercise-06-f1"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glue('exercise-06-f1', routes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trip.plot.scatter(x='shape_pt_lon', y='shape_pt_lat');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/papermill.record/image/png": 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\n",
      "application/papermill.record/text/plain": "<Figure size 600x600 with 1 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "exercise-06-f2"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = trip.plot.scatter(x='shape_pt_lon', y='shape_pt_lat');\n",
    "fig = base.get_figure()\n",
    "glue('exercise-06-f2', fig, display=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 06-g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>shape_dist_traveled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10004214_011023</td>\n",
       "      <td>11152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10004215_011023</td>\n",
       "      <td>11152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001407_011023</td>\n",
       "      <td>15061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10015281_071023</td>\n",
       "      <td>21688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10015281_081023</td>\n",
       "      <td>21688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398086</th>\n",
       "      <td>9998411_091023</td>\n",
       "      <td>14147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398087</th>\n",
       "      <td>9998411_131023</td>\n",
       "      <td>14040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398088</th>\n",
       "      <td>9999194_061023</td>\n",
       "      <td>13597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398089</th>\n",
       "      <td>9999194_081023</td>\n",
       "      <td>13597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398090</th>\n",
       "      <td>9999194_151023</td>\n",
       "      <td>13597.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398091 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                trip_id  shape_dist_traveled\n",
       "0       10004214_011023              11152.0\n",
       "1       10004215_011023              11152.0\n",
       "2        1001407_011023              15061.0\n",
       "3       10015281_071023              21688.0\n",
       "4       10015281_081023              21688.0\n",
       "...                 ...                  ...\n",
       "398086   9998411_091023              14147.0\n",
       "398087   9998411_131023              14040.0\n",
       "398088   9999194_061023              13597.0\n",
       "398089   9999194_081023              13597.0\n",
       "398090   9999194_151023              13597.0\n",
       "\n",
       "[398091 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Get total distance traveled per 'trip_id'\n",
    "stop_times = pd.read_csv('data/gtfs/stop_times.txt')\n",
    "stop_times = stop_times.groupby('trip_id')['shape_dist_traveled'].max()\n",
    "stop_times = stop_times.reset_index()\n",
    "stop_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>shape_dist_traveled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10004214_011023</td>\n",
       "      <td>11152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10004215_011023</td>\n",
       "      <td>11152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001407_011023</td>\n",
       "      <td>15061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10015281_071023</td>\n",
       "      <td>21688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10015281_081023</td>\n",
       "      <td>21688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398086</th>\n",
       "      <td>9998411_091023</td>\n",
       "      <td>14147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398087</th>\n",
       "      <td>9998411_131023</td>\n",
       "      <td>14040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398088</th>\n",
       "      <td>9999194_061023</td>\n",
       "      <td>13597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398089</th>\n",
       "      <td>9999194_081023</td>\n",
       "      <td>13597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398090</th>\n",
       "      <td>9999194_151023</td>\n",
       "      <td>13597.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398091 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                trip_id  shape_dist_traveled\n",
       "0       10004214_011023              11152.0\n",
       "1       10004215_011023              11152.0\n",
       "2        1001407_011023              15061.0\n",
       "3       10015281_071023              21688.0\n",
       "4       10015281_081023              21688.0\n",
       "...                 ...                  ...\n",
       "398086   9998411_091023              14147.0\n",
       "398087   9998411_131023              14040.0\n",
       "398088   9999194_061023              13597.0\n",
       "398089   9999194_081023              13597.0\n",
       "398090   9999194_151023              13597.0\n",
       "\n",
       "[398091 rows x 2 columns]"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "exercise-06-g1"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "glue('exercise-06-g1', stop_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
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   "outputs": [
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       "      route_id           trip_id  shape_dist_traveled\n",
       "0            1   17336167_081023               7258.0\n",
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       "...        ...               ...                  ...\n",
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       "\n",
       "[8188 rows x 3 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Join with trips to get 'route_id' per trip\n",
    "trips = pd.read_csv('data/gtfs/trips.txt')\n",
    "trips = trips[['trip_id', 'route_id']]\n",
    "trips = pd.merge(trips, stop_times, on='trip_id')\n",
    "trips = trips.groupby('route_id').first()\n",
    "trips = trips.reset_index()\n",
    "trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
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       "      route_id           trip_id  shape_dist_traveled\n",
       "0            1   17336167_081023               7258.0\n",
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       "3            4  585026983_011023               7025.0\n",
       "4            5    5656121_081023               9750.0\n",
       "...        ...               ...                  ...\n",
       "8183     37315           1_88437                  NaN\n",
       "8184     37316           1_88438                  NaN\n",
       "8185     37342  585305994_121023              16432.0\n",
       "8186     37343  585305995_121023              16761.0\n",
       "8187     37350           1_88086                  NaN\n",
       "\n",
       "[8188 rows x 3 columns]"
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     "metadata": {
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       "mime_prefix": "",
       "name": "exercise-06-g2"
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   "source": [
    "glue('exercise-06-g2', trips)"
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  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
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   "outputs": [
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      "text/plain": [
       "        route_id route_short_name                     route_long_name  \\\n",
       "0              1                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "1              1                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "2              1                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "3              1                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "4              1                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "...          ...              ...                                 ...   \n",
       "398086     37350              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה   \n",
       "398087     37350              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה   \n",
       "398088     37350              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה   \n",
       "398089     37350              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה   \n",
       "398090     37350              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה   \n",
       "\n",
       "        service_id          trip_id   trip_headsign  direction_id  shape_id  \n",
       "0            12749  17336167_081023  רכבת יבנה מזרח             0  135197.0  \n",
       "1            12750  17336049_081023  רכבת יבנה מזרח             0  135197.0  \n",
       "2            12749   5655787_081023  רכבת יבנה מזרח             0  135197.0  \n",
       "3            12750  17336048_081023  רכבת יבנה מזרח             0  135197.0  \n",
       "4            12750  17336051_081023  רכבת יבנה מזרח             0  135197.0  \n",
       "...            ...              ...             ...           ...       ...  \n",
       "398086    10006108          1_88086            8048             0       NaN  \n",
       "398087    10006097          1_88058            8048             0       NaN  \n",
       "398088    10006102          1_88070            8048             0       NaN  \n",
       "398089    10006105          1_88443            8048             0       NaN  \n",
       "398090    10006100          1_88064            8048             0       NaN  \n",
       "\n",
       "[398091 rows x 8 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
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    }
   ],
   "source": [
    "# Join with routes to get route short/long names\n",
    "routes = pd.read_csv('data/gtfs/routes.txt')\n",
    "routes = routes[['route_id', 'route_short_name', 'route_long_name']]\n",
    "routes = pd.merge(routes, trips, on='route_id')\n",
    "routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
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       "      <td>2א</td>\n",
       "      <td>ת. רכבת יבנה מערב-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "      <td>585026983_011023</td>\n",
       "      <td>7025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>ת. רכבת יבנה מזרח-יבנה&lt;-&gt;ת. רכב...</td>\n",
       "      <td>5656121_081023</td>\n",
       "      <td>9750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8183</th>\n",
       "      <td>37315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>תא אוניברסיטה-תל אביב יפו&lt;-&gt;באר...</td>\n",
       "      <td>1_88437</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8184</th>\n",
       "      <td>37316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>תא אוניברסיטה-תל אביב יפו&lt;-&gt;הרצ...</td>\n",
       "      <td>1_88438</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8185</th>\n",
       "      <td>37342</td>\n",
       "      <td>46</td>\n",
       "      <td>גן טכנולוגי-נצרת&lt;-&gt;מסרארה חט''ב...</td>\n",
       "      <td>585305994_121023</td>\n",
       "      <td>16432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8186</th>\n",
       "      <td>37343</td>\n",
       "      <td>46</td>\n",
       "      <td>גן טכנולוגי-נצרת&lt;-&gt;מסרארה חט''ב...</td>\n",
       "      <td>585305995_121023</td>\n",
       "      <td>16761.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8187</th>\n",
       "      <td>37350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>באר שבע מרכז-באר שבע&lt;-&gt;נהריה-נהריה</td>\n",
       "      <td>1_88086</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8188 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      route_id route_short_name                     route_long_name  \\\n",
       "0            1                1  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "1            2                1  ת. רכבת יבנה מזרח-יבנה<->ת. רכב...   \n",
       "2            3                2  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "3            4               2א  ת. רכבת יבנה מערב-יבנה<->ת. רכב...   \n",
       "4            5                2  ת. רכבת יבנה מזרח-יבנה<->ת. רכב...   \n",
       "...        ...              ...                                 ...   \n",
       "8183     37315              NaN  תא אוניברסיטה-תל אביב יפו<->באר...   \n",
       "8184     37316              NaN  תא אוניברסיטה-תל אביב יפו<->הרצ...   \n",
       "8185     37342               46  גן טכנולוגי-נצרת<->מסרארה חט''ב...   \n",
       "8186     37343               46  גן טכנולוגי-נצרת<->מסרארה חט''ב...   \n",
       "8187     37350              NaN  באר שבע מרכז-באר שבע<->נהריה-נהריה   \n",
       "\n",
       "               trip_id  shape_dist_traveled  \n",
       "0      17336167_081023               7258.0  \n",
       "1      13076786_081023               7088.0  \n",
       "2       5655904_011023               9883.0  \n",
       "3     585026983_011023               7025.0  \n",
       "4       5656121_081023               9750.0  \n",
       "...                ...                  ...  \n",
       "8183           1_88437                  NaN  \n",
       "8184           1_88438                  NaN  \n",
       "8185  585305994_121023              16432.0  \n",
       "8186  585305995_121023              16761.0  \n",
       "8187           1_88086                  NaN  \n",
       "\n",
       "[8188 rows x 5 columns]"
      ]
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "",
       "name": "exercise-06-g3"
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   "source": [
    "glue('exercise-06-g3', routes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "      route_id route_short_name                     route_long_name  \\\n",
       "5155     19964              991  ת. מרכזית חוף הכרמל/רציפים בינע...   \n",
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       "1153      5182              399  ת. מרכזית חדרה/רציפים-חדרה<->ת....   \n",
       "\n",
       "               trip_id  shape_dist_traveled  \n",
       "5155   30526427_091023             446163.0  \n",
       "5475   30526417_061023             445475.0  \n",
       "1456    2125973_151023             432200.0  \n",
       "1455  584673295_011023             432187.0  \n",
       "1153  584660100_091023             401875.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort accordinge to distance traveled\n",
    "routes = routes.sort_values('shape_dist_traveled', ascending=False).head()\n",
    "routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
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       "      route_id route_short_name                     route_long_name  \\\n",
       "5155     19964              991  ת. מרכזית חוף הכרמל/רציפים בינע...   \n",
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       "\n",
       "               trip_id  shape_dist_traveled  \n",
       "5155   30526427_091023             446163.0  \n",
       "5475   30526417_061023             445475.0  \n",
       "1456    2125973_151023             432200.0  \n",
       "1455  584673295_011023             432187.0  \n",
       "1153  584660100_091023             401875.0  "
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       "name": "exercise-06-g4"
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   ],
   "source": [
    "glue(\"exercise-06-g4\", routes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ת. מרכזית חוף הכרמל/רציפים בינעירוני-חיפה<->ת. מרכזית אילת/הורדה-אילת-2#',\n",
       " 'ת. מרכזית אילת/רציפים-אילת<->ת. מרכזית חוף הכרמל/הורדה-חיפה-1#',\n",
       " 'ת. מרכזית המפרץ/רציפים בינעירוני-חיפה<->ת. מרכזית אילת/הורדה-אילת-2#',\n",
       " 'ת. מרכזית אילת/רציפים-אילת<->ת. מרכזית המפרץ/הורדה-חיפה-1#',\n",
       " 'ת. מרכזית חדרה/רציפים-חדרה<->ת. מרכזית אילת/הורדה-אילת-1#']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes.head()[\"route_long_name\"].to_list()"
   ]
  }
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