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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:

  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 pandas as pd  # Import Panda Library
  
# 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 
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) 

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:



  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
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



This article is attributed to GeeksforGeeks.org

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