Array indexing
NumPy mainly offers three ways to index into arrays.
Slicing Based Indexing
Integer Based Indexing
Boolean Based Indexing
Accessing a single element
Use [] for accessing the elements inside the array
Example 1
*** index starts from 0
Slicing Based Indexing
Similar to Python lists, numpy arrays can be sliced. As elements inside the arras are indexed
Since arrays may be multidimensional, you must specify a slice for each dimension of the array
A slice of an array is a view into the same data, so modifying it will modify the original array
When you index into numpy arrays using slicing, the resulting array view will always be a subarray of the original array.
Accessing multiple element from 2d array
In 2 dimensional we have rows as one dimension and columns as other dimension (Matrix)
Rows and Columns index starts from 0
So whenever we are doing slicing we have to provide slicing for both rows as well as for columns
*** ending index not included
Example 2
1 ROW AND 1 COLUMN
Example 3
"N" ROWS AND "M" COLUMNS
Integer array indexing
Integer array indexing allows you to construct arbitrary arrays using the data from another array
Means no change in the original array if we do any change in the sub array
yield an array of lower rank than the original array
Since arrays may be multidimensional, you must specify an Integer array indexing for each dimension of the array
Example 4
Boolean Based indexing
Boolean array indexing lets you pick out arbitrary elements of an array
Means no change in the original array if we do any change in the extracted array
This type of indexing is used to select the elements of an array that satisfy some condition
It can also be called masking
Example 5
Example 6
Mathematical Operations on Arrays
Basic mathematical functions can be applied on arrays
This mathematical functions operate elementwise on arrays
This mathematical operation are available as both as operators and function in numpy module
Addition (+ or add() )
We can use + operator to do elementwise addition
Or we can use add() functions in the NumPy module
Example 7
Subtraction (- or subtract() )
We can use - operator to do elementwise subtraction
Or we can use subtract() functions in the NumPy module
Example 8
Multiplication (* or multiply() )
We can use * operator to do elementwise multiplication
Or we can use multiply() functions in the NumPy module
Example 9
dot() (to get inner product)
* is elementwise multiplication, not matrix multiplication
To do matrix multiplication we have to use dot() function provided by numpy module
dot() functions computes the inner products
Whenever we are using this dot() function we have to follow matrix multiplication to rule
For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix.
The resulting matrix, has the number of rows of the first and the number of columns of the second matrix as its final shape
So if you don't follow the above rule then we will get an error
Example 10
Example 11
Broadcasting
Broadcasting is a powerful mechanism that allows numpy to work with arrays of different shapes when performing arithmetic operations.
Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array
whenever we are doing computation on different shapes of array then numpy automatically uses this broadcasting technique
Broadcasting in a simple way we can understand it as duplicating the rows or column to match the shape of the larger array
In the below figure we have an array having a shape (3,3) means 3 rows and 3 columns and other array having a shape of (3,) means a single row having 3 elements so when we do multiplication on both the arrays the array with shape (3,) will duplicate the rows 2 times to match the larger array shaped as (3,3)
Example 11
Example 12
Example 13
Division (/ or divide() )
We can use / operator to do elementwise division
Or we can use divide() functions in the NumPy module
Example 14
Square Root (sqrt() )
We can use sqrt() function to get square root of each element in an array
Example 15
Different Functions For Performing Computations On Arrays
sum(array object , axis)
We can use sum() function to get sum of all elements
axis (its and optional parameter) parameters has two values 0 (column wise sum) , 1 (rows wise sum)
If we don't use axis parameter then the sum function will return sum of all the elements in the array
Example 16
prod(array object , axis)
We can use prod() function to get product of all elements
axis (its and optional parameter) parameters has two values 0 (column wise product) , 1 (rows wise product)
If we don't use axis parameter then the prod function will return product of all the elements in the array
Example 17
cumsum(array object , axis)
We can use cumsum() function to get the cumulative sum of the elements .
axis (its and optional parameter) parameters has two values 0 (column wise cumulative sum ) , 1 (rows wise cumulative sum )
Example 18
cumprod(array object , axis)
We can use cumprod() function to get the cumulative product of the elements .
axis (its and optional parameter) parameters has two values 0 (column wise cumulative product ) , 1 (rows wise cumulative product )
Example 19
round(array object , decimals)
Evenly round to the given number of decimals
decimal parameter is nothing but the Number of decimal places to round to (default: 0)
Example 20
Descriptive statistics on the ndarray
mean()
Computes the mean of ndarray
axis parameter can be used to compute mean row wise(1) or column wise(0)
If axis parameter is not used while computing the mean then mean will be computed for total elements inside the array
Example 21
median()
Computes the median of ndarray
axis parameter can be used to compute mean row wise(1) or column wise(0)
If axis parameter is not used while computing the median then median will be computed for total elements inside the array
Example 22
std() Standard Deviation
Computes the standard deviation of ndarray
axis parameter can be used to compute mean row wise(1) or column wise(0)
If axis parameter is not used while computing the standard deviation then standard deviation will be computed for total elements inside the array
Example 23
min()
Computes the minimum value of ndarray
axis parameter can be used to compute mean row wise(1) or column wise(0)
If axis parameter is not used while computing the minimum value then minimum value will be computed from the total elements inside the array
Example 24
argmin()
Gets the index of minimum value in the ndarray
Example 24
max()
Computes the maximum value of ndarray
axis parameter can be used to compute mean row wise(1) or column wise(0)
If axis parameter is not used while computing the maximum value then maximum value will be computed from the total elements inside the array
Example 25
argmax()
Gets the index of maximum value in the ndarray
Example 26
Extras:-
Transpose A Matrix
We can transpose a matrix(2d) by using T
It can be also used as a reshape function
taking the transpose of a rank 1 array does nothing
Example 27
Tile Function
Tile function is used to create a new array by repeating the array column wise or row wise
Tile function takes two important arguments
array
repetitions along axis (should be given in a tuple format)
(row wise repetitions ,column wise repetitions)
(2,3) -- repeat 2 times row wise and three times column wise
Example 28
Create An Empty Array With The Same Shape Of Other Array
We can create an empty array with the same shape of other array by using empty_like()
Example 29
