Specific use of Pandas Pivot Table

Preface

Pivot Table is used to reshape and summarize data during data analysis and processing.

What is a pivot table?

PivotTables are data aggregation tools that rearrange raw data in a user-defined way to make it easier to analyze and visualize. Typically, the goal of a pivot table is to aggregate, summarize, and cross-analyze the data to gain insights into the dataset.

Steps to use

1. Introduce necessary libraries

import pandas as pd
import  as plt

2. Read data

# Read the datasetdata = pd.read_csv('your_dataset.csv')

3. Create a Pivot Table

Using Pandaspivot_table()Functions create pivot tables. This function accepts multiple parameters, including the dataset, columns to be analyzed, row indexes, column indexes, and summary methods.

# Create a Pivot Tablepivot_table = pd.pivot_table(data, values='value_to_summarize', 
                             index='row_index_column', 
                             columns='column_index_column', 
                             aggfunc='sum')

in:

• valuesIt is a column that needs to be summarized.
• indexIt is the row index, which determines the rows of the pivot table.
• columnsIt is the column index, which determines the columns of the pivot table.
• aggfuncIt is a function used for summary, which can be ‘sum’, ‘mean’, ‘count’, etc.

4. View the Pivot Table

print(pivot_table)

Sample code

import pandas as pd

# Read sample datasetdata = pd.read_csv('/datasciencedojo/datasets/master/')

# Create a Pivot Tablepivot_table = pd.pivot_table(data, values='Fare', 
                             index='Pclass', 
                             columns='Sex', 
                             aggfunc='mean')

# Print Pivot Tableprint(pivot_table)

Summarize

By rationally setting row indexes, column indexes and summary methods, you can quickly generate pivot tables suitable for data analysis based on different needs.

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