Arrays (numpy
)#
Last updated: 20230527 16:41:33
Introduction#
Arrays are fundamental when working with data. Many types of data are convenient to arrange and represent using arrays, and many types of data processing operations are facilitated through the use of arrays. For example:
Digital images and spatial rasters are represented using arrays, possibly with spatial metadata (such as the Coordinate Reference System) (see Rasters (rasterio) and Rastervector interactions)
Tables can also be thought as a collection of onedimensional arrays, having the same length, representing the different columns in a table (see Tables (pandas))
In this chapter we cover our first thirdparty package, named numpy
[]. The numpy
package deals with arrays. We are going to learn about methods for working with arrays, including:
Plotting (numpy) (using the
matplotlib
package)
In this chapter we work with the numpy
package on its own. However, as we will see in later chapters, the numpy
package is also the foundation and basis for most data analysisrelated domains in Python, inclusing spatial data analysis. For example, table (see Tables (pandas)) and vector layer (see Vector layers (geopandas)) columns, as well as rasters (see Rasters (rasterio)), are internally represented by numpy
arrays. Therefore, although the material in this chapter may seem abstract at first, keep in mind that it is going to be practically applicable soon enough through all of the remaining chapters.
What is numpy
?#
numpy
(short for “numerical python”) is a wellestablished Python package for scientific computing with arrays, and for working with data in general. The numpy
package provides standardized data structures, functions, and operators for homogeous arrays, facilitating efficient computation and shorter code. Namely, thanks to the fact that a numpy
array is uniform, in terms of data types, processing is much more efficient compared to a list
(see Lists (list)). numpy
also provides “vectorized” operators for arrays, which otherwise require using for
loops (see for loops) or list comprehension (see List comprehension) when working with lists.
numpy
is a core package in the data science ecosystem of Python, so it is worthwhile to learn it no matter what aspect of data science you are going to explore later on. numpy
is the foundation for many other, more specialized, packages in Python, including most of the packages we learn about later on in this book:
pandas
for working with tables (see Tables (pandas))geopandas
for working with vector layers (see Vector layers (geopandas))rasterio
andrichdem
for working with rasters (see Rasters (rasterio))
Assuming it is installed (see Installing packages, to start working with numpy
we first need to import it (see Loading packages). By convention, numpy
is imported as np
:
import numpy as np
What is an array?#
An array is an ordered sequence of values, arranged in an ndimensional structure. In this book, we are going to focus on the three most useful types of arrays:
Onedimensional—A sequence of values, analogous to a
list
Twodimensional—A matrix, i.e., a collection of sequences, all of the same length, arranged into rows or columns
Threedimensional—A “cubic” array, i.e., a collection of matrices of the same size
Keep in mind that an array can also be:
0dimensional—a single value, such as when extracting an individual element from an array (see Individual values), or
≥4dimensional—highdimensional arrays, less useful for our purposes and in general
In spatial raster terminology (see Rasters (rasterio)),
a twodimensional array corresponds to a singleband raster, and
a threedimensional array corresponds to a multiband raster.
In numpy
, an array data structure is represented by a data type named ndarray
(short for “Ndimensional array”).
Creating arrays#
Overview#
An ndarray
can be created in several ways, which we demonstrate in this section:
From a
list
(see From list)By repeating a sequence (see Using np.tile and Using np.repeat)
By calculating a consecutive sequence (see Using np.arange and Using np.linspace)
Later on we will also demostrate that an array can be imported from a CSV file (see Reading array from file).
From list
#
In case we wish to manually specify all of the array values, the np.array
function can be used to convert a list
into an array. For example, a plain list
can be converted to a onedimensional array named, hereby named a
:
a = np.array([3, 8, 2, 43, 12, 1, 8])
a
array([ 3, 8, 2, 43, 12, 1, 8])
A “nested” list, i.e., a list of lists, can be converted to a twodimensional array, where the internal lists comprise the rows, hereby named b
:
b = np.array([[1, 0, 0], [2, 1, 2], [2, 1, 0], [2, np.nan, 1]])
b
array([[ 1., 0., 0.],
[ 2., 1., 2.],
[ 2., 1., 0.],
[ 2., nan, 1.]])
Note that array b
contains a special value np.nan
, which represents “No Data”, i.e., an unknown value in the array. We will elaborate on how np.nan
values are treated in various operations, later on (see Working with missing data (numpy)). For now, you can get an impression of the np.nan
behavior, and start getting used to the possibility of having missing values in a numpy
array.
Let us also create a threedimensional array named c
. Rather than typing all values manually, here we are using the np.arange
function and the .reshape
function, which we will explain later on (see Using np.arange and General reshaping (.reshape), respectively). What is important, for now, is that the resulting object is a threedimensional array:
c = np.arange(1, 25).reshape((2, 3, 4))
c
array([[[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12]],
[[13, 14, 15, 16],
[17, 18, 19, 20],
[21, 22, 23, 24]]])
Note that the type
of a
, b
and c
, is ndarray
:
type(a)
numpy.ndarray
type(b)
numpy.ndarray
type(c)
numpy.ndarray
Using np.tile
#
Repetitive arrays can be created using the np.tile
function, which accepts:
A
—A number, list, or array to repeatreps
—How many times to repeat it?
For example:
np.tile(12, 7)
array([12, 12, 12, 12, 12, 12, 12])
np.tile([1, 2], 3)
array([1, 2, 1, 2, 1, 2])
np.tile(a, 2)
array([ 3, 8, 2, 43, 12, 1, 8, 3, 8, 2, 43, 12, 1, 8])
Using np.repeat
#
There is a another function, named np.repeat
, for creating repetitive arrays. Unlike np.tile
, the np.repeat
function repeats each element consecutively and not the entire array (or list). Compare the following example with np.tile([1,2],3)
(above):
np.repeat([1, 2], 3)
array([1, 1, 1, 2, 2, 2])
Using np.arange
#
It is often useful to create sequential arrays. For example, the np.arange
function can be used to create sequential arrays, given:
start
—The start valuestop
—The end valuestep
—The step size
The np.arange
function is very similar, in terms of its design, to the range
function (see The range function).
For example, the following expression creates an array starting from 5
, ending before 12
, with step size 1.5
:
np.arange(5, 12, 1.5)
array([ 5. , 6.5, 8. , 9.5, 11. ])
The default value for step
is 1
, as demonstrated in the following expression, where we omit step
:
np.arange(5, 12)
array([ 5, 6, 7, 8, 9, 10, 11])
When passing a single argument to np.arange
, that argument is treated it as stop
, whereas the default start
is 0
and the default step
is 1
:
np.arange(12)
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
Using np.linspace
#
Another useful method to create sequential arrays is np.linspace
(short for “linear space”) accepts three arguments:
start
—The start valuestop
—The end valuenum
—Number of points (default is50
)
The np.linspace
function then returns evenly spaced points, in the interval specified by start
and stop
. The step size is automatically calculated to accommodate the specified number of points (default is 50
).
For example, the following expression creates a sequence of 9
equally spaced points, between 0
and 4
.
np.linspace(0, 4, 9)
array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ])
Note
There are several other functions to facilitate creation of arrays, such as functions to create repetitive arrays:
np.zeros
—Array of given shape filled with0
np.ones
—Array of given shape filled with1
np.full
—Array of given shape filled with userspecified value
and functions to create random number arrays, such as:
np.random.random
—Array of given shape filled with random numbers in the interval[0.0, 1.0)
np.random.normal
—Array of given shape filled with random numbers from a normal distributionnp.random.randint
—Array of given shape filled with random integers in the specified interval
Array dimensions#
The most basic property of arrays is their shape: the size of each array dimension. This can be accessed using the .shape
property. The returned object is a tuple
(see Tuples (tuple)), with length according to the number of dimensions:
a.shape
(7,)
b.shape
(4, 3)
c.shape
(2, 3, 4)
A derived property of .shape
is the number of dimensions .ndim
, which is equal to the “length” of the .shape
property:
a.ndim
1
b.ndim
2
c.ndim
3
Another derived property of .shape
is the number of elements .size
, which is equal to a multiplication of all dimension lengths:
a.size
7
b.size
12
c.size
24
At this point, you may wonder what is the reason for using an array rather than simply a list
. This leads us to the main difference between arrays and lists: the fact that an array, unlike a list
, is composed of values of the same type. This makes arrays more restrictive than lists, but also much more efficient in terms of storage size and computation speed.
Data types#
Checking data type#
As mentioned above, ndarray
objects are composed of values of the same type. We can check the type of an existing array using the .dtype
(“data type”) property. For example:
a.dtype
dtype('int64')
b.dtype
dtype('float64')
Note
The int64
and float64
data types in numpy
are considered “standard”. Therefore, when printing an array object, the data type specification appears for all data types except for those. Try printing a
and b
to see for yourself.
The above printout means that a
is an integer array, while b
is a float array. You can also see that numpy
accommodates more detailed distinction between data types than basic Python, with integer and float varieties according to specific precision. For example, int64
and float64
mean 64bit precision integer and float arrays, respectively (numpy array data types).
The required data type can be specified when constructing an array, using the dtype
parameter. The value can be specified using the respective object representing each data type (see numpy array data types). For example:
x = np.array([1, 1, 1], dtype=np.float32)
x
array([1., 1., 1.], dtype=float32)
Commonly used data types in numpy
are summarized in Table 12. As you can see, there are many varieties of numeric types in numpy
. This may seem confusing, compared to basic Python or other programming languages such as R, which have just one data type for integer and one for float. However, keep in mind that the purpose of having many specific data types is conserving memory and making calculations more efficient. In practice, int64
and float64
are most commonly used to represent int
and float
, respectively.
Data type 
Description 


Boolean ( 

Platformdependent default integer type (normally 

Integer in a single byte ( 

Integer in 16 bits ( 

Integer in 32 bits ( 

Integer in 64 bits ( 

Unsigned integer ( 

Unsigned integer ( 

Unsigned integer ( 

Unsigned integer ( 

Default float type, shorthand for 

Halfprecision (16 bit) float ( 

Singleprecision (32 bit) float ( 

Doubleprecision (64 bit) float ( 
numpy
also supports creating arrays of “customized” types, and even arrays that do not conform to any of the builtin data types. The latter means that you can create an array where the elements are any type of Python object. Accordingly, the dtype
of the resulting array will be "object"
.
For example, the following array of strings has dtype
of '<U5'
, namely “unicode string of length 5”:
s = np.array(['John', 'James', 'Bob'])
s
array(['John', 'James', 'Bob'], dtype='<U5')
An array of dictionaries, however, results in dtype
of "object"
, because there is no builtin data type to represent an array of dict
elements in numpy
:
s = np.array([{'a': 1}, {'b': 2}])
s
array([{'a': 1}, {'b': 2}], dtype=object)
In practice, an array of type “object” implies that processing the array is going to employ “general” Python functions, rather than optimized numpy
functions, which is less efficient.
Changing data type#
If necessary, an array can be transformed from one data type to another, using the .astype
method. The argument of .astype
is one of the numpy
data types (Table 12). For example, here is how we can transform array a
from int64
to float64
:
a = a.astype(np.float64)
a
array([ 3., 8., 2., 43., 12., 1., 8.])
Here is how we can transform a
to np.int8
:
a = a.astype(np.int8)
a
array([ 3, 8, 2, 43, 12, 1, 8], dtype=int8)
and here we go back to int64
:
a = a.astype(np.int64)
a
array([ 3, 8, 2, 43, 12, 1, 8])
Keep in mind that we must choose a data range which encompasses the entire range of values in the array (see Table 12). Otherwise, we get meaningless (random) values instead of those beyond the valid range.
Subsetting arrays#
Overview#
Array subsets are created by passing indices, separated by commas (i.e., tuples of integers), inside square brackets ([
). The number of indices needs to be in agreement with the number of dimensions. Remember that the order of dimensions (and indices) is:
array[rows,columns]
—for a twodimensional arrayarray[layers,rows,columns]
—for a threedimensional array
The indices start from 0
, like in other Python data sctructures (see Accessing list elements).
Individual values#
For example, here is how we can get individual elements out of an array:
a[1] ## 2nd element
8
b[1, 2] ## Element in 2nd row, 3rd column
2.0
c[1, 0, 3] ## Element in 2nd layer, 1st row, 4th column
16
When subsetting individual values from an array, we get a plain numpy
value, such as an int64
or float64
. Technically speaking, the result is simplified to a zerodimensional numpy
array:
type(a[1])
numpy.int64
Array slices#
Any of the indices can be replaced with :
, which means “return all subsets in that dimension”. For example:
b[1, :] ## 2nd row, all columns
array([2., 1., 2.])
b[:, 1] ## 2nd column, all rows
array([ 0., 1., 1., nan])
c[:, 2, :] ## 3rd row, all layers and all columns
array([[ 9, 10, 11, 12],
[21, 22, 23, 24]])
We can also assign new values into an individual element, or a slice, of an array, in which case the respective elements are replaced. Try this for yourself to see how it works.
Exercise 04a
How many dimensions does
c[1,2,:]
have?What about
c[1,:,:]
?Check your answers using an expression which returns the number of dimensions.
The start:stop
notation which we learned about for list
subsetting (see list slicing) is applicable to numpy
arrays too. For example:
b[:2, :] ## 1st and 2nd rows
array([[1., 0., 0.],
[2., 1., 2.]])
b[2:, :] ## 3rd row and onward
array([[ 2., 1., 0.],
[ 2., nan, 1.]])
b[1:3, :] # 2nd and 3rd rows
array([[2., 1., 2.],
[2., 1., 0.]])
When using individual indices, the result is simplified in terms of the number of dimensions. For example, b[:,1]
is a onedimensional array, with the values of one column of a twodimensional array. When using slices, however, the result is never simplified, even the slice refers to just one element along the dimension. For example, compare the following methods to subset to second column of b
:
b[:, 1] ## Subset 2nd column using individual index
array([ 0., 1., 1., nan])
b[:, 1:2] ## Subset 2nd column using slice
array([[ 0.],
[ 1.],
[ 1.],
[nan]])
Examples of subsetting a twodimensional array are visualized in Fig. 21.
Exercise 04b
Replace one of the values in the array
b
with a new value.Replace an entire row with a new value.
Replace an entire column with a new value.
Views and copies#
An array is a mutable object. In agreement with the basic behavior of mutable objects, such as list
s in Python (see Mutability and copies), this means that a copy of the original object is (by default) a reference to the same memory location. Any change we make in such a references is therefore “reflected” in both.
In Mutability and copies, we have seen that assigning a list
to another variable creates a reference to the same list
. The same behavior takes places with numpy
arrays. Moreover, and unlike list
s, a subset of a numpy
array is also a reference to the original (whereas a list
subset is automatically copied). Therefore modifying an array subset is, too, reflected in the original array, unless we explicitly make an independent copy of the array and subset the copy.
Let us demonstrate the behavior described in the last paragraph. Suppose that we take a subset with the 2^{nd} row from array b
:
b_sub = b[1, :]
b_sub
array([2., 1., 2.])
and then replace one of the values with a different value, such as 999
:
b_sub[1] = 999
As expected, the subset b_sub
now contains the new value:
b_sub
array([ 2., 999., 2.])
What may be unexpected, is that the original array b
, where the subset comes from, is also updated:
b
array([[ 1., 0., 0.],
[ 2., 999., 2.],
[ 2., 1., 0.],
[ 2., nan, 1.]])
The reason for this behavior is that a subset is not a true copy of the underlying array. Instead, it can be thought of as a view, or reference, to the array, or to a subset of it. Any modification of the “view” modifies the original array as well.
In case we are interested in a “real” copy, which is independent of the original, we must explicitly use the .copy
method (see Mutability and copies). For example, let us return b
to its original form (again through the view b_sub
!):
b_sub[1] = 1
b
array([[ 1., 0., 0.],
[ 2., 1., 2.],
[ 2., 1., 0.],
[ 2., nan, 1.]])
Now, let us create a subset which is a copy of b
, and modify it:
b_sub2 = b[1, :].copy()
b_sub2[1] = 999
Again, the subset is modified as expected:
b_sub2
array([ 2., 999., 2.])
The original array b
, however, is unchanged, since b_sub2
is an independent copy:
b
array([[ 1., 0., 0.],
[ 2., 1., 2.],
[ 2., 1., 0.],
[ 2., nan, 1.]])
Keep in mind that there are operations other than subsetting that return a “view”, rather than a copy, such as:
.reshape
(see General reshaping (.reshape)).T
(see Transposing (.T)).
ndarray
to list
#
A numpy
array can be converted to a list
using the .tolist
method. This can be thought of as the opposite creating an array from a list
with np.array
(see From list). For example:
b.tolist()
[[1.0, 0.0, 0.0], [2.0, 1.0, 2.0], [2.0, 1.0, 0.0], [2.0, nan, 1.0]]
This may be useful if we prefer, or are required, to process our data using basic Python functions, rather than numpy
functions and methods.
Array reshaping#
General reshaping (.reshape
)#
Array reshaping means transferring the array values to an array with a different shape. Arrays can be reshaped using the .reshape
method, which accepts a tuple
with the new shape. For example, the following expression transforms the onedimensional array a
with shape (7,)
into a twodimensional array with shape (7,1)
, i.e., one column and seven rows:
a.reshape((7, 1))
array([[ 3],
[ 8],
[2],
[43],
[12],
[ 1],
[ 8]])
The following expression transforms b
into a twodimensional array of a different shape, from shape (4,3)
to shape (2,6)
:
b.reshape((2, 6))
array([[ 1., 0., 0., 2., 1., 2.],
[ 2., 1., 0., 2., nan, 1.]])
As mentioned above, .reshape
returns a “view” of the original array (see Views and copies).
Note
You can use 1
for dimensions in .reshape
to be calculated automatically, according to the other dimension(s) and the length of the data. For example, try b.reshape((1, 6))
or b.reshape((2,1))
instead of the last expression.
Note
Another way of reshaping an array, specific to the case where we add a new dimension, is to use np.newaxis
. For example, a[:,np.newaxis]
and a[np.newaxis,:]
transform a
into a twodimensional array with a new lengthone dimension of columns or rows, respecively. Try these expressions in the console to see for yourself.
To onedimensional (.flatten
)#
A common reshaping operation is “collapsing” a multydimensional array into a onedimensional array. This can be done using the .flatten
method:
b.flatten()
array([ 1., 0., 0., 2., 1., 2., 2., 1., 0., 2., nan, 1.])
“Flattening” can be useful when we need to treat all values of a two (or three) dimensional array as a single sequence, for example to calculate global statistics, or to display a histogram (see Histograms (numpy)).
Transposing (.T
)#
Finally, it is often useful to transpose a twodimensional array, that is, to turn array rows into columns and vice versa. The transposed array is accessible through the .T
property:
b
array([[ 1., 0., 0.],
[ 2., 1., 2.],
[ 2., 1., 0.],
[ 2., nan, 1.]])
b.T
array([[ 1., 2., 2., 2.],
[ 0., 1., 1., nan],
[ 0., 2., 0., 1.]])
Transposing is useful in situations when we need to apply a function that treats array rows and columns differently, such as when plotting (see Line plots (numpy)).
As mentioned above, .T
returns a “view” of the original array (see Views and copies).
Vectorized operations#
Overview#
Vectorized operations, also known as “Universal functions” in numpy
terminology, make it possible to apply the same operation on all elements of an array at once. Vectorized operations (discussed in this section) and summary functions (see Summarizing array values), are two big advantages and motivations of using numpy
, both in terms of shorter code and efficiency, compared to “basic” methods such as lists. Due to their efficient storage, vectorized operations on numpy
arrays are much more efficient and fast than, say, a for
loop or a list complehension repeatedly performing the same operation on all elements of a list
.
There are numerous builtin vectorized operators and functions in numpy
, and there are methods to define new ones. The operations are pairwise, elementbyelement. We can combine:
an array with another array (e.g.,
b+b
), oran array with an individual number (e.g.,
b+1
).
In the latter case, the number is automatically treated as an array with repeated values.
Arithmetic operators#
All arithmetic operators (see Arithmetic operators) are implemented as vectorized numpy
operators. Let us take array a
as an example:
a
array([ 3, 8, 2, 43, 12, 1, 8])
Here are a few examples of arithmetic array operations:
a + 1 ## Add 1 to elements of 'a'
array([ 4, 9, 1, 44, 13, 2, 9])
a ## Inverse of elements 'a'
array([ 3, 8, 2, 43, 12, 1, 8])
a * a ## Multiply each element of 'a' by itself (same as 'a**2')
array([ 9, 64, 4, 1849, 144, 1, 64])
1000  a ## Subtract each element in 'a' from 1000
array([ 997, 992, 1002, 957, 988, 999, 992])
Functions on each element#
Numerous numpy
functions are defined as vectorized functions, to perform an operation on each element, resulting in a new array of exactly the same shape as the input, containing the result. For example, the np.abs
function is used to calculate the absolute value of each element in an array:
np.abs(a)
array([ 3, 8, 2, 43, 12, 1, 8])
which is analogous to using basic abs
function (see Functions) on plain int
or float
values.
numpy
conditional operators#
The conditional operators (see Conditions) are also implemented as vectorized operators. For example:
a > 10 ## Is each element of 'a' greater than 10?
array([False, False, False, True, True, False, False])
a == abs(a) ## Is each element of 'a' equal to its absolute value?
array([ True, True, False, True, True, True, True])
Note that the result of conditional operations on an array is a boolean (see Boolean values (bool)) array, as evident when examining the .dtype
property (see Checking data type):
(a > 10).dtype
dtype('bool')
Boolean arrays can be negated with the ~
operator, or combined with &
(and) and 
(or) operators (Table 13):
The
~
operator is analogous tonot
(see Table 10), i.e., it negates a boolean array, replacingTrue
withFalse
and vice versaThe
&
operator is analogous toand
(see Table 10)The

operator is analogous toor
(see Table 10)
Remember that &
, 
, and ~
should be used when working with arrays. The analogous and
, or
, and not
operators which we covered earlier (see Table 10), are applicable to entire objects, not arrays elements.
Operator 
Meaning 


And 

Or 

Not 
Let us demonstrate the &
, 
, and ~
operators through examples. First, suppose that we have two boolean arrays, a>5
and a<15
:
a > 5
array([False, True, False, True, True, False, True])
a < 15
array([ True, True, True, False, True, True, True])
Here is how they can be combined using the &
or the 
operator. Beware that you must use parentheses around both sides of the expressions, since &
, 
, and ~
have higher precenence than comparison operators such as <
and >
:
(a > 5) & (a < 15) ## Is each element of 'a' greater than 5 *and* smaller than 15?
array([False, True, False, False, True, False, True])
(a > 5)  (a < 15) ## Is each element of 'a' greater than 5 *or* smaller than 15?
array([ True, True, True, True, True, True, True])
Here is an example of the ~
operator in action, negating a boolean array:
~(a > 5) ## Is each element of 'a' *not* greater than 5?
array([ True, False, True, False, False, True, False])
Note
Note that in all of the examples in this section which involve two arrays, the arrays had equal dimensions. numpy
vectorized operations are, in fact, more general than that. In certain conditions, numpy
can accommodate operations between two arrays with different dimensions. This is a powerful technique known as broadcasting. For more details, see https://numpy.org/doc/stable/user/basics.broadcasting.html.
When a boolean array is passed to an arithmetic operation, or function, True
values are treated as 1
and False
values are treated as 0
. The result is then a numeric array. For example, the boolean array a>5
:
a > 5
array([False, True, False, True, True, False, True])
is automatically transformed to a numeric array of 0
’s and 1
’s when multiplied by 10
:
10 * (a > 5)
array([ 0, 10, 0, 10, 10, 0, 10])
Masking#
What are masks?#
Numpy arrays can be subsetted using corresponding boolean arrays, which are also known as masks []. Masking is useful for extracting or modifying some of the values in an array—more specifically, those values which correspond to a specified condition. For example, we may want to:
extract the nonmissing values in an array, or
replace all missing values with a new value, or
delete (i.e., replace with “No Data”) values that above a particular threshold (e.g., assuming they are measurement errors), and so on.
Any boolean array with a corresponding shape can function as a mask. Typically, a mask is created using a conditional expression (see numpy conditional operators) on the original array itself. For example, given the array named a
:
a
array([ 3, 8, 2, 43, 12, 1, 8])
The expression a>10
creates a mask, referring to those elements of a
which are greater than 10
:
a > 10
array([False, False, False, True, True, False, False])
Using masks for subsetting#
When passed as an index inside square brackets ([
), a mask can be used to filter the array, returning a subset with only those elements that correspond to True
. For example:
a[a > 10]
array([43, 12])
Note that when subsetting two or threedimensional arrays using a mask, the original shape is not preserved. Instead, the selected values are “collected” into a onedimensional array. For example:
b[b > 1]
array([2., 2., 2., 2.])
Using masks for assignment#
Assignment to a “masked” subset can be used to modify the corresponding subset in the original array. For example, the following expression replaces those values of a
which are greater than 10
, with a new value 100
:
a[a > 10] = 100
a
array([ 3, 8, 2, 100, 100, 1, 8])
The following expression replaces values which are negative or equal to 3
with a new value 99
. Again, note the mandatory parentheses.
a[(a < 0)  (a == 3)] = 99
a
array([99, 8, 99, 100, 100, 1, 8])
Before we move on, let us get back to the original values in array a
:
a = np.array([3, 8, 2, 43, 12, 1, 8])
a
array([ 3, 8, 2, 43, 12, 1, 8])
Summarizing array values#
Overview#
In addition to vectorized, or elementbyelement, operations such as np.abs
(see Vectorized operations), numpy
also accommodates functions to summarize array values, such as np.sum
to calculate the array sum. As we will see, there are several modes of operations for summary functions:
A “global” summary (the default), such as
np.sum(x)
for the sum of all values in arrayx
A summary for subsets along one or more dimensions—using
axis=A
oraxis=(A,B,...)
, whereA
,B
, etc. are the dimensions being summarized (or “eliminated”), such asnp.sum(x,axis=0)
for column sums of a twodimensional arrayx
The most useful function for summarizing array values, for the purposes of this book, are given in Table 14. Note that some of the functions are defined in two ways:
as standalone functions (e.g.,
np.sum
)as array methods (e.g.,
.sum
)
Both ways are identical, and it is just a matter of preference which one to use. However, standalone functions are accessible more universally:
Some of the functions only have a standalone version (such as
np.median
)“No Data”safe functions (such as
np.nanmean
, see Operations with “No Data”) are only available through standalone functions
Operation 
Function 
Method 
“No Data”safe version 

Sum 



Minimum 



Maximum 



Mean 



Median 

 

Index of (first) minimum 



Index of (first) maximum 



Is at least one element 


 
Are all elements 


 
Global summaries#
Unlike vectorized operations (see Vectorized operations), which are performed elementbyelement, summary operations return a single result for the entire array (the default), or an array with results for subsets such as the rows and columns. In this section we discuss the former, known as global summaries.
For example, the np.sum
function can be used to calculate the global sum of all elements in an array:
np.sum(a)
73
The np.min
and np.max
functions return the minimum and maximum element, respectively:
np.min(a)
2
np.max(a)
43
As mentioned above, most numpy
summary functionshave “method” shortcuts to do the same using shorter code (Table 14). For example, there is a .max
method which does the same thing as the np.max
function. For example:
a.max() ## Shortcut to 'np.max(a)'
43
Note
Python has a builtin function called sum
, which works on lists as well as numpy
arrays. However, sum
is much slower, and it is not tailored to numpy
arrays, so it is not aware of array dimensions. The same applies to min
, max
, any
, all
, and other “base” Python functions. Make sure you are using the specialized numpy
functions (such as np.sum
) or methods (such as .sum
) when working with numpy
, to avoid unexpected results!
A common use case of global summary functions is to count True
values in a boolean array, typically to find out the number of elements satisfying a given condition. Recall that the arithmetic operators, when applied on a boolean array, treats True
values as 1
and False
values as 0
(see numpy conditional operators). Therefore, it can be used for “counting” the number of True
values. For example:
np.sum(a > 5) ## How many elements of 'a' are greater than 5?
4
np.sum(a < 15) ## How many elements of 'a' are smaller than 15?
6
Sometimes we are not interested in the exact number of True
values, but rather to figure out if we have at least one True
value, or if all values are True
. The .any
and .all
methods used to reveal whether the array contains at least one True
, or whether all of its elements are True
, respectively. For example:
np.any(a > 5) ## Is *any* of the elements of 'a' greater than 5?
True
np.all(a > 5) ## Are *all* elements of 'a' greater than 5?
False
Exercise 04c
The
np.diff
function calculates consecutive differences between elements in an array.Write an expression that, using
np.diff
, checks whether the elements of an array are in increasing order.Test your expression using two arrays that are in increasing and nonincreasing oderer, such as
np.array([2,5,8,9])
andnp.array([21,8,9])
. The returned values need to beTrue
, andFalse
, respectively.
Summaries per dimension (2D)#
As shown above, the default behavior of summary functions, such as np.sum
, is to operate on all elements combined, resulting in a “global” summary (see Global summaries). Using the axis
parameter, however, we can also apply the function on different dimensions, separately.
When referring to twodimensional arrays, summary functions can be applied separately:
on each column (
axis=0
), oron each row (
axis=1
) (Fig. 22).
Let us demonstrate on the twodimensional array b
:
np.sum(b, axis=0) ## Column sums
array([ 7., nan, 3.])
np.sum(b, axis=1) ## Row sums
array([ 1., 5., 3., nan])
Note that whenever at least one “No Data” (np.nan
) value is passed to np.sum
, the result is also “No Data” (Fig. 22). To ignore np.nan
values we may use the “No Data”insensitive version of .sum
, namely np.nansum
(see Table 14). We elaborate on dealing with “No Data” later on (see Working with missing data (numpy)).
The meaning of the axis
parameter can be confusing. To get rowwise summaries (i.e., dimension 0
) we are actuially specifying the column dimension (axis=1
), and vice versa. Here is a how you can remember the meaning of the axis
parameter. With the axis
parameter, we specify which dimension gets “eliminated” from the resulting array. Accordingly,
axis=0
“eleminates” dimension0
(rows), resulting in columnwise summariesaxis=1
“eliminates” dimension1
(columns), resulting in rowwise summaries
Summaries per dimension (3D)#
Let us now move on to the more complex case of summaries per dimension of threedimensional arrays. Before we begin, recall that the order of dimensions in a threedimensional array is (layers,rows,columns)
(see Subsetting arrays).
For example, in array c
, we have:
Two “layers”
Three rows
Four columns
c.shape
(2, 3, 4)
First, we can get rowwise, columnwise, and “layer”wise summaries, according to the same principle as with a twodimensional array (see Summaries per dimension (2D)). We just need to “eliminate” two out of three dimensions, rather than one of two dimensions. In this case, the axis
argument is a tuple
of length two:
np.sum(c, axis=(1, 2)) ## "Layer" sums
array([ 78, 222])
np.sum(c, axis=(0, 2)) ## Row sums
array([ 68, 100, 132])
np.sum(c, axis=(0, 1)) ## Column sums
array([66, 72, 78, 84])
Second, eliminating one out of three dimensions results in summaries of the two remaining dimensions, which is a little more confusing. Overall, there are three possible combinations of dimension “pairs”:
axis=0
—rowcolumn summariesaxis=1
—columnlayer summariesaxis=2
—rowlayer summaries
For our purposes in this book, the only useful combination is rowcolumn summaries. In raster terminology, rowcolumn summaries refer to “cell”, or “pixel”, summaries (see Summarizing values). To get rowcolumn summaries, we eliminate the layer dimension:
np.sum(c, axis=0) ## Row and column (i.e., "pixel") sums
array([[14, 16, 18, 20],
[22, 24, 26, 28],
[30, 32, 34, 36]])
Working with missing data (numpy
)#
“No Data” representation#
Missing values in a numpy
array are typically represented using np.nan
(“not a number”), which is a special type of a float value in numpy
. For example:
x = np.array([1, np.nan, 3])
x
array([ 1., nan, 3.])
Due to the way that numbers are stored in computer memory, np.nan
can only be accommodated in float
arrays. This leads to the inconvenience that int
arrays cannot store “No Data” values in any straightforward way. Accordingly, for example, an integer
array that contains np.nan
is automatically “promoted” to a float
array, which is why, for instance, x
turns out to be a float
array:
x.dtype
dtype('float64')
Operations with “No Data”#
np.nan
values are “contagious” in arithmetic operations. Namely, when one or more of the inputs is missing, the result of the operation is also going to be np.nan
. For example, adding any number to np.nan
results in np.nan
, reflecting the fact that the sum is unknown:
np.nan + 4
nan
Similarly, a global sum of an array that contains one (or more) np.nan
is also np.nan
:
np.sum(x)
nan
We can use the special “No Data”safe versions of summary functions (Table 14) to apply the respective function on the nonmissing elements of the array. For example, the following expression uses np.nansum
to calculate the sum of nonmissing elements in x
, which is equal to 4
:
np.nansum(x)
4.0
Exercise 04e
How can you insert a “No Data” (i.e.,
np.nan
) value into the 1^{st} layer, 2^{nd} row, 3^{rd} column ofc
?Keep in mind that
int
arrays cannot containnp.nan
!
Perhaps unintuitively, conditional operators with np.nan
always return False
(even though the result is actually unknown):
np.nan > 0
False
x > 0
array([ True, False, True])
The exception is inequality (!=
), which combined with np.nan
always returns True
:
np.nan != 10
True
The implication is that operations such as array subsetting based on conditions (see Using masks for subsetting), or counting the elements which satisfy a particular condition (see Global summaries), typically ignore np.nan
values:
x[x > 0]
array([1., 3.])
np.sum(x > 0)
2
Exercise 04f
Calculte “cell” sums in the modified array
c
, with and without ignoring “No Data” values.
Detecting “No Data” (np.isnan
)#
“No Data” values can be detected using the np.isnan
function. Given an input array, np.isnan
returns a corresponding boolean array, where np.nan
are marked as True
, and valid values are marked as False
. For example:
np.isnan(x)
array([False, True, False])
To detect values that are not missing, we can reverse the output of np.isnan
using the negation operator ~
(see Table 10):
~np.isnan(x)
array([ True, False, True])
Reading array from file#
In addition to creating an array from an existing Python object such as a list
(see From list), or creating an array with consecutive or sequential values (see, for example, Using np.arange), we can read array data from files on disk. For example, the numpy
package contains a function named np.genfromtxt
, which can be used to read an array from a plain text file, such as a CSV file.
The first argument of np.genfromtxt
is the text file path, which is mandatory. The np.genfromtxt
function has many other, optional, parameters, to accommodate reading from various text file formats, such as specifying the delimiting character, i.e., the character which is used to separate columns (delimiter
), whether there are header rows, such as column names, to be skipped (skip_header
), and so on.
The following expression demonstrates reading array data from a CSV file without a header:
m = np.genfromtxt('data/carmel.csv', delimiter=',')
m
array([[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]])
The carmel.csv
matrix represents a Digital Elevation Model (DEM) of the area around Haifa. Note that:
"data/carmel.csv"
is the file path to thecarmel.csv
file, which is included in the book sample data (see Sample data). Recall that this is a relative path (see File paths) to the subdirectory nameddata
. This means that, for the expression to work, you need to have thecarmel.csv
file inside a subdirectory nameddata
, relative to the directory where the notebook is.This is a CSV file, which is why we use
delimiter=","
(open the file in a spreadsheet software such as Excel to get a sense of its contents).
Later on, we will learn how to turn this array into a spatial raster (see Creating raster from array).
To keep track of the calculations, it may be easier to start with a smaller array. The carmel_lowres.csv
file also contains a DEM of the area around Haifa, but at much coarser resolution and therefore much smaller dimensions:
m = np.genfromtxt('data/carmel_lowres.csv', delimiter=',')
m
array([[ nan, nan, nan, nan, nan, 3., 3.],
[ nan, nan, nan, nan, 4., 6., 4.],
[ nan, nan, nan, nan, 6., 9., 7.],
[ nan, 61., 9., 4., 9., 10., 16.],
[ nan, 106., 132., 11., 6., 6., 27.],
[ nan, 47., 254., 146., 6., 6., 12.],
[ nan, 31., 233., 340., 163., 13., 64.],
[ 3., 39., 253., 383., 448., 152., 39.],
[ 5., 32., 199., 357., 414., 360., 48.],
[ 7., 49., 179., 307., 403., 370., 55.]])
We will use the smaller array to demonstrate array plotting (see Plotting (numpy)). Afterwards, you can try the same operations with the larger array.
Note
Unlike in the examples in this section, the first row in CSV files is usually the header, specifying the column names. However, the header is irrelevant when importing the CSV file into a numpy
array, as a numpy
array does not have column (or row) names. Therefore, when reading a CSV file with a header, it should be skipped using skip_header=1
(i.e., skip the first line) in np.genfromtxt
.
Note
To write a numpy
array to a CSV file, you can use the np.savetxt
function, in an expression such as np.savetxt("m.csv", m, fmt="%.6f", delimiter=",", header="x,y,z", comments='')
, where:
"m.csv"
is the CSV file namem
is the array object to be writtedfmt="%.6f"
specifies the format, such as"%.6f"
for floats with 6 digitsdelimiter=","
specifies the CSV delimiter, i.e., a commaheader="x,y,z"
andcomments=''
should be used in case you want the first row in the CSV to contain a header with commaseparated column names (such asx,y,z
for a CSV file with three columns namedx
,y
, andz
)
Plotting (numpy
)#
The matplotlib
package#
Sometimes visualizing array values can be more informative and instructive than any numeric summary. For example, we may wish to:
Summarize array values in the form of Histograms (numpy)
Display one or twodimensional array values in the form of Line plots (numpy)
Display twodimensional array values in the form of Images (numpy)
The most established and widely used thirdparty Python package for data visualization, of arrays or other types of data, is matplotlib
. To use matplotlib
, we need to import it. More specifically, we import the pyplot
subpackage (see Loading packages), which is the simpler interface of matplotlib
, renaming it to plt
for convenience:
import matplotlib.pyplot as plt
Visualization in Python is a large topic. Hereby we only introduce the most basic methods for a “first impression” of the data. Keep in mind that matplotlib
is can be used in more advanced ways to produce customized publicationquality figures (Fig. 23).
Note
Scientific Visualization: Python + Matplotlib is a recommended book about advanced plotting methods using the matplotlib
package (Fig. 23). It is can be downloaded as PDF. The official web page is a good starting point to learn about matplotlib
.
Histograms (numpy
)#
To make a histogram showing the distribution of a numpy
array, we first need to “collect” them into one long ondimensional array using the flatten
method (see To onedimensional (.flatten)):
v = m.flatten()
v
array([ nan, nan, nan, nan, nan, 3., 3., nan, nan, nan, nan,
4., 6., 4., nan, nan, nan, nan, 6., 9., 7., nan,
61., 9., 4., 9., 10., 16., nan, 106., 132., 11., 6.,
6., 27., nan, 47., 254., 146., 6., 6., 12., nan, 31.,
233., 340., 163., 13., 64., 3., 39., 253., 383., 448., 152.,
39., 5., 32., 199., 357., 414., 360., 48., 7., 49., 179.,
307., 403., 370., 55.])
Then, the onedimensional array can be passed to the plt.hist
function to produce a histogram:
plt.hist(v);
Note
The ;
at the end of a matplotlib
expression is an IPython trick to avoid printing textual data along with the plot. (Try removing it and rerunning the cell to see the unwanted textual output.)
Line plots (numpy
)#
A onedimensional array can be passed to the plt.plot
function to display the array values in a line plot, where the xaxis values are array indices, and yaxis values are the array values. For example, the plt.plot
can be used to display a “profile” or “slice” of a given array, along a specific row:
plt.plot(m[7, :]);
or along a specific column:
plt.plot(m[:, 4]);
A twodimensional array can also be passed to plt.plot
, in which case each array column is plotted as a separate series. For example, the following plot displays the seven columns in m
, i.e., seven northsouth elevation profiles:
plt.plot(m);
Images (numpy
)#
To display the information of a twodimensional array all at once, we can to encode the array values into a visual property, such as color. The result is an image, also known as a heatmap. The plt.imshow
(short for “image show”) function can be used to produce an image of a twodimensional array:
plt.imshow(m);
Since m
is an array representing a Digital Elevation Model, this image is actually an elevation map of the area that this array represents.
Beyond numpy
#
numpy
is a fundamental Python package, with many other packages for data analysis, scientific computing, and statistics depending on it. Notable packages depending on numpy
include:
pandas
—For working with tablesscipy
—For scientific computationsscikitlearn
—For statistical analysis and machine learning
We are going to cover pandas
in the next two chapters (see Tables (pandas) and Table reshaping and joins). Towards the end of the book we are going to breifly use scipy
to apply a focal filter over a raster (see Focal filtering).
The scikitlearn
package is beyond the scope of this book, however there are many resources online (https://scikitlearn.org/stable/) and in print []. In case you are going to do statistical analysis or machine learning in Python, scikitlearn
is the recommended starting point.
More exercises#
Exercise 04g
Calculate a \(10\times 10\) array which contains the multiplication table (as shown below). Hint: create a “template” array of the right shape (with uniform values such as
0
), then use a nestedfor
loop (see for loops) to populate it with the correct values.Next, make your code general enough so that it can produce a multiplication table of any size, specified by a parameter named
s
in the beginning of the code. For example,s=10
should result in the \(10\times 10\) multiplication table shown below,s=5
should result in a \(5\times 5\) multiplication table, and so on.
array([[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20],
[ 3, 6, 9, 12, 15, 18, 21, 24, 27, 30],
[ 4, 8, 12, 16, 20, 24, 28, 32, 36, 40],
[ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50],
[ 6, 12, 18, 24, 30, 36, 42, 48, 54, 60],
[ 7, 14, 21, 28, 35, 42, 49, 56, 63, 70],
[ 8, 16, 24, 32, 40, 48, 56, 64, 72, 80],
[ 9, 18, 27, 36, 45, 54, 63, 72, 81, 90],
[ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]])
Exercise 04h
Read the
carmel.csv
file into anumpy
array (see Reading array from file).Find the minimum, maximum, and average elevation value in the Carmel area, according to the
carmel.csv
array, excluding “No Data” values (answer:14.0
,541.0
, and124.06
, respectively).Calculate and plot a new array where “No Data” values are flagged by
1
, and any other value is flagged by0
(exercise04h1
).Calculate the proportion of missing values in the array (answer:
0.53
).Calculate and plot the average column and row “profiles” of the Carmel DEM, by calculating a 1D array of column means, and a 1D array of row means, respectively (
exercise04h2
).