PPS-UNIT5.ppt

21CSS101J - Programming for Problem
Solving
UNIT-5
SRM INSTITUTE OF SCIENCE AND
TECHNOLOGY, RAMAPURAM
By
Dr. C. SIVASANKAR
Assistant Professor (CSE)

Numpy Library
Pandas Library

NumPy Libray
Slicing 2-D Arrays
Example
From the second element, slice elements from index 1
to index 4 (not included):
import numpy as np
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[1, 1:4])

Example
From both elements, return index 2:
import numpy as np
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[0:2, 2])

Example
From both elements, slice index 1 to index 4 (not
included), this will return a 2-D array:
import numpy as np
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[0:2, 1:4])

NumPy Array Copy vs View
The Difference Between Copy and View
The main difference between a copy and a view of an array is that the copy is a
new array, and the view is just a view of the original array.
The copy owns the data and any changes made to the copy will not affect
original array, and any changes made to the original array will not affect the copy.
The view does not own the data and any changes made to the view will affect
the original array, and any changes made to the original array will affect the view.

COPY:
Example
Make a copy, change the original array, and display both arrays:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.copy()
arr[0] = 42
print(“arr=“,arr)
print(“x=“,x)

VIEW:
Example
Make a view, change the original array, and display both arrays:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.view()
arr[0] = 42
print(arr)
print(x)

Make Changes in the VIEW:
Example
Make a view, change the view, and display both arrays:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
x = arr.view()
x[0] = 31
print(arr)
print(x)

NumPy Array Shape
Shape of an Array
The shape of an array is the number of elements in each
dimension.
Get the Shape of an Array
NumPy arrays have an attribute called shape that returns
a tuple with each index having the number of
corresponding elements.

Example
Print the shape of a 2-D array:
import numpy as np
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
print(arr.shape)

import numpy as np
arr = np.array([1, 2, 3, 4], ndmin=5)
print(arr)
print('shape of array :', arr.shape)

NumPy Array Reshaping
Reshaping arrays
Reshaping means changing the shape of an array.
The shape of an array is the number of elements in each
dimension.
By reshaping we can add or remove dimensions or change
number of elements in each dimension

Reshape From 1-D to 2-D
Example
Convert the following 1-D array with 12 elements into a 2-D array.
The outermost dimension will have 4 arrays, each with 3 elements:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)

Reshape From 1-D to 3-D
Example
Convert the following 1-D array with 12 elements into a 3-D
array.
The outermost dimension will have 2 arrays that contains 3
arrays, each with 2 elements:
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(2, 3, 2)
print(newarr)

Flattening the arrays
Flattening array means converting a multidimensional array into a 1D
array.
We can use reshape(-1) to do this.
Example
Convert the array into a 1D array:
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
newarr = arr.reshape(-1)
print(newarr)

Iterating Arrays
Iterating means going through elements one by one.
As we deal with multi-dimensional arrays in numpy, we can
do this using basic for loop of python.
If we iterate on a 1-D array it will go through each element
one by one

Example
Iterate on the elements of the following 1-D array:
import numpy as np
arr = np.array([1, 2, 3])
for x in arr:
print(x)

Iterating 2-D Arrays
In a 2-D array it will go through all the rows.
Example
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
print(x)

Example
Iterate on each scalar element of the 2-D array:
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
for y in x:
print(y)

Iterating 3-D Arrays
In a 3-D array it will go through all the 2-D arrays.
Example
import numpy as np
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
for x in arr:
print(x)

Joining NumPy Arrays
Joining means putting contents of two or more arrays in a
single array.
In SQL we join tables based on a key, whereas in NumPy we
join arrays by axes.
We pass a sequence of arrays that we want to join to
the concatenate() function, along with the axis. If axis is not
explicitly passed, it is taken as 0.

Example
Join two arrays
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.concatenate((arr1, arr2))
print(arr)

import numpy as np
a = np.array([[10, 7, 4], [3, 2, 1]])
print(np.percentile(a, 50))
print(np.percentile(a, 50, axis=0))
print(np.percentile(a, 50, axis=1))

Variance :

import numpy as np
# Array of data
arr = [1,2,3,4,5]
y=np.mean(arr)
print("mean=",y)
x = np.var(arr)
print("var=",x)

Pandas is an open-source Python Library providing high-
performance data manipulation and analysis tool using its
powerful data structures.
The name Pandas is derived from the word Panel Data – an
Econometrics from Multidimensional data.

Pandas deals with the following three data structures −
Series
DataFrame
Panel
These data structures are built on top of Numpy array,
which means they are fast.

Mutability
All Pandas data structures are value mutable (can be
changed) and except Series all are size mutable.
Series is size immutable.

Python Pandas – Series
Series is a one-dimensional labeled array capable of holding
data of any type (integer, string, float, python objects, etc.).
The axis labels are collectively called index
pandas.Series
A pandas Series can be created using the following
constructor −
pandas.Series( data, index, dtype, copy)

A series can be created using various inputs like −
Array
Dict
Scalar value or constant

Pandas : Slicing

Python Pandas – DataFrame

Pandas - Analyzing DataFrames

Pandas - Plotting
Pandas uses the plot() method to create diagrams.
We can use Pyplot, a submodule of the Matplotlib library to
visualize the diagram on the screen.

SCATTER PLOT

Histogram

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