Pandas
Pandas offer tools for cleaning and process your data. It is the most popular Python library that is used for data analysis. In pandas, a data table is called a dataframe.
So, let’s start with creating Pandas data frame:
Example 1:
Python3
# Python code demonstrate creating import pandas as pd # initialise data of lists.data = {'Name':[ 'Mohe' , 'Karnal' , 'Yrik' , 'jack' ], 'Age':[ 30 , 21 , 29 , 28 ]} # Create DataFramedf = pd.DataFrame( data ) # Print the output.df |
Output:
Example 2: load the CSV data from the system and display it through pandas.
Python3
# import moduleimport pandas# load the csvdata = pandas.read_csv("nba.csv")# show first 5 columndata.head() |
Output:
Data Visualization with Python Seaborn
Data Visualization is the presentation of data in pictorial format. It is extremely important for Data Analysis, primarily because of the fantastic ecosystem of data-centric Python packages. And it helps to understand the data, however, complex it is, the significance of data by summarizing and presenting a huge amount of data in a simple and easy-to-understand format and helps communicate information clearly and effectively.
Pandas and Seaborn is one of those packages and makes importing and analyzing data much easier. In this article, we will use Pandas and Seaborn to analyze data.