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Data analysis using Pandas
  • Difficulty Level : Easy
  • Last Updated : 15 Oct, 2020
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Pandas is the most popular python library that is used for data analysis. It provides highly optimized performance with back-end source code is purely written in C or Python.

We can analyze data in pandas with:

  1. Series
  2. DataFrames

Series:

Series is one dimensional(1-D) array defined in pandas that can be used to store any data type.

Code #1: Creating Series




# Program to create series
  
# Import Panda Library
import pandas as pd  
  
# Create series with Data, and Index
a = pd.Series(Data, index = Index)  

Here, Data can be:

  1. A Scalar value which can be integerValue, string
  2. A Python Dictionary which can be Key, Value pair
  3. A Ndarray

Note: Index by default is from 0, 1, 2, …(n-1) where n is length of data.
 
Code #2: When Data contains scalar values






# Program to Create series with scalar values 
  
# Numeric data
Data =[1, 3, 4, 5, 6, 2, 9]  
  
# Creating series with default index values
s = pd.Series(Data)    
  
# predefined index values
Index =['a', 'b', 'c', 'd', 'e', 'f', 'g'
  
# Creating series with predefined index values
si = pd.Series(Data, Index) 

Output:

Scalar Data with default Index

Scalar Data with Index

 
Code #3: When Data contains Dictionary




# Program to Create Dictionary series
dictionary ={'a':1, 'b':2, 'c':3, 'd':4, 'e':5
  
# Creating series of Dictionary type
sd = pd.Series(dictionary) 

Output:

Dictionary type data

 

Code #4:When Data contains Ndarray




# Program to Create ndarray series
  
# Defining 2darray
Data =[[2, 3, 4], [5, 6, 7]]  
  
# Creating series of 2darray
snd = pd.Series(Data)    

Output:

Data as Ndarray

 

DataFrames:

DataFrames is two-dimensional(2-D) data structure defined in pandas which consists of rows and columns.



Code #1: Creation of DataFrame




# Program to Create DataFrame
  
# Import Library
import pandas as pd   
  
# Create DataFrame with Data
a = pd.DataFrame(Data)  

Here, Data can be:

  1. One or more dictionaries
  2. One or more Series
  3. 2D-numpy Ndarray

 
Code #2: When Data is Dictionaries




# Program to Create Data Frame with two dictionaries
  
# Define Dictionary 1
dict1 ={'a':1, 'b':2, 'c':3, 'd':4}   
  
# Define Dictionary 2     
dict2 ={'a':5, 'b':6, 'c':7, 'd':8, 'e':9
  
# Define Data with dict1 and dict2
Data = {'first':dict1, 'second':dict2} 
  
# Create DataFrame 
df = pd.DataFrame(Data)  

Output:

DataFrame with two dictionaries

 
Code #3: When Data is Series




# Program to create Dataframe of three series 
import pandas as pd
  
# Define series 1
s1 = pd.Series([1, 3, 4, 5, 6, 2, 9])   
  
# Define series 2       
s2 = pd.Series([1.1, 3.5, 4.7, 5.8, 2.9, 9.3]) 
  
# Define series 3
s3 = pd.Series(['a', 'b', 'c', 'd', 'e'])     
  
# Define Data
Data ={'first':s1, 'second':s2, 'third':s3} 
  
# Create DataFrame
dfseries = pd.DataFrame(Data)              

Output:

DataFrame with three series

 
Code #4: When Data is 2D-numpy ndarray
Note: One constraint has to be maintained while creating DataFrame of 2D arrays – Dimensions of 2D array must be same.




# Program to create DataFrame from 2D array
  
# Import Library
import pandas as pd 
  
# Define 2d array 1
d1 =[[2, 3, 4], [5, 6, 7]] 
  
# Define 2d array 2
d2 =[[2, 4, 8], [1, 3, 9]] 
  
# Define Data
Data ={'first': d1, 'second': d2}  
  
# Create DataFrame
df2d = pd.DataFrame(Data)    

Output:

DataFrame with 2d ndarray

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