Python Pandas

Panda is an open source library of Python. This library is used to analyse and manipulate data using python data structure.

Pandas is used for the high performance of merging data,Python pandas is used in many fields like Finance,Statistic,Web Development,Artificial Intelligence and data preprocessing and much more. Pandas consist of lots of DataFrame objects with default customization indexes.

Key Feature of Pandas

• Pandas provide a large amount of libraries which are very fast and efficient.it provides the series and data frame.
• Lots of data makes confusion unless you don’t have any labeling method are an important factor,without the label data becoming more complex to understand pandas providing the indexing and alignment options for making the dataset easy.
• Lots of data makes confusion unless you don’t have any labeling method are an important factor,without the label data becoming more complex to understand pandas providing the indexing and alignment options for making the dataset easy.
• In any data there are lots of possibilities that lack value in the dataframe ,it makes more complex and confusing pandas help to find the missing value.
• In any data there are lots of possibilities that lack value in the dataframe ,it makes more complex and confusing pandas help to find the missing value.
• Before processing any dataset we would clean up data ,with the help of this you can clean data.
• Pandas provides a wide range of built-in data for read and write output.
• Now time data is found in multiple formats like JSON,CSV,HDFS.Pandas provide multiple ways to read the data.

Pandas provides the following type of data structure.

a. Series
b. Data Frame

Series :it is the homogeneous and one dimensional,immutable array.

DataFrame:It is the 2D label size mutable ,heterogeneous array.

DataFrame is widely used in the world and it is the most important structure.

Series

Its one dimension array previously we talk about and it’s the homogeneous array. Lets see the example for better understanding.

Data Frame

It is the two dimensional and it is the mutable data structure. Lets takes the example:-

This table representing the details of the student and provides useful information of these student,

Data type Column

Pandas Example

import pandas as pd
data = pd.Series([1, 2, 3, 4, 5, 6, 7,8,9,10])
print(data)

Output

0     1
1     2
2     3
3     4
4     5
5     6
6     7
7     8
8     9
9    10
dtype: int64

Question

import pandas as pd
data= {'Name' : 'Ankit','Address': 'Lucknow', 'Gender' : 'Male', 'Mobile': 234564445, 'Roll No': 54}
d= pd.Series(data)
print(d)

Output

Name           Ankit
Address      Lucknow
Gender          Male
Mobile     234564445
Roll No           54
dtype: object

Question 1

# importing pandas as pd 
import pandas as pd 

# Creating the Series 
data = pd.Series(['Football', 'Cricket', 'Hockey', 'Chess', 'Badminton']) 
  
# Create the Datetime Index 
index = ['Sports1','Sports2','Sports3','Sports4','Sports5'] 
  
# set the index 
data.index = index 
  
# Print the series 
print(data) 

Output

Sports1       Football
Sports2       Cricket
Sports3        Hockey
Sports4         Chess
Sports5    Badminton
dtype: object

Program 2

# importing pandas as pd 
import pandas as pd 
  
# Creating the Series 
food_price = pd.Series([250, 100, 300, 400,300,333,600]) 
  
# Create the Index 
recipe = ['Chow mein','Burger','HOT Dog','French Fry','Macaroni','Pasta','Pizza'] 
  
# set the index 
food_price.index = recipe 
  
# Print the series 
print(food_price)

Output

Chow mein     250
Burger        100
HOT Dog       300
French Fry    400
Macaroni      300
Pasta         333
Pizza         600
dtype: int64