Python | Data analysis using Pandas

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:

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

Here, Data can be:

A Scalar value which can be integerValue, string
A Python Dictionary which can be Key, Value pair
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  
Data =[1, 3, 4, 5, 6, 2, 9]  # Numeric data 
  
# 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)  

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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 
Data =[[2, 3, 4], [5, 6, 7]]  # Defining 2darray 
  
# 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 pandas as pd   # Import Library 
  
a = pd.DataFrame(Data)  # Create DataFrame with Data 

Here, Data can be:

One or more dictionaries
One or more Series
2D-numpy Ndarray

Code #2: When Data is Dictionaries

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

Output:

DataFrame with two dictionaries

Code #3: When Data is Series

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

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

Output:

DataFrame with 2d ndarray

Python | Data analysis using Pandas

Series:

DataFrames:

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