Pandas is an opensource library in Python that provides easytouse data structures and data analysis tools. It is widely used by data scientists for data manipulation, data analysis, and data visualization tasks.
One of the key data structures provided by Pandas is the DataFrame. In this article, we will cover the basics of creating pivot tables in Pandas and using Pandas DataFrames.
A DataFrame in Pandas is a twodimensional, sizemutable, and tabular data structure. It is similar to a spreadsheet or a SQL table. It has rows and columns, and each column can have a different data type.
You can think of a DataFrame as a collection of Series objects, where each row represents a unique record, and each column represents a specific attribute or feature of that record.
Creating a Pivot Table in Pandas
A pivot table is a powerful feature in Pandas that allows you to summarize and analyze data in a tabular format. It is especially useful when you have a large dataset and want to extract meaningful insights from it.
The syntax for creating a pivot table in Pandas is straightforward. You first create a Pandas DataFrame, and then you call the pivot_table()
function on it, passing the appropriate parameters.
Example of Creating a Pivot Table with Sum of Values
Let’s say we have a sales dataset that contains information about sales made by different salespeople in a company. We want to create a pivot table that shows the total sales made by each salesperson across different regions and products.
To do this, we can first create a Pandas DataFrame from our dataset, and then call the pivot_table()
function on it, passing the necessary parameters. Here’s an example:
import pandas as pd
# create a sample sales dataset
sales_data = {
'Salesperson': ['Alice', 'Alice', 'Bob', 'Bob', 'Charlie', 'Charlie'],
'Region': ['East', 'West', 'East', 'West', 'East', 'West'],
'Product': ['A', 'B', 'A', 'B', 'A', 'B'],
'Revenue': [1000, 1500, 2000, 2500, 3000, 3500]
}
df = pd.DataFrame(sales_data)
# create a pivot table with sum of revenue
pivot_table = df.pivot_table(values='Revenue', index='Salesperson', columns=['Region', 'Product'], aggfunc=sum)
print(pivot_table)
The output of the above code will be a pivot table that summarizes the sales data by salesperson, region, and product, with the sum of revenue as the metric.
Adding Margins to the Pivot Table
Margins are a useful feature in pivot tables that allow you to see the total for each row or column in addition to the values that are already displayed. In Pandas, you can add margins to your pivot table by passing the margins=True
parameter to the pivot_table()
function.
Here’s an example:
import pandas as pd
# create a sample sales dataset
sales_data = {
'Salesperson': ['Alice', 'Alice', 'Bob', 'Bob', 'Charlie', 'Charlie'],
'Region': ['East', 'West', 'East', 'West', 'East', 'West'],
'Product': ['A', 'B', 'A', 'B', 'A', 'B'],
'Revenue': [1000, 1500, 2000, 2500, 3000, 3500]
}
df = pd.DataFrame(sales_data)
# create a pivot table with sum of revenue and margins
pivot_table = df.pivot_table(values='Revenue', index='Salesperson', columns=['Region', 'Product'], aggfunc=sum, margins=True)
print(pivot_table)
The output of the above code will be a pivot table that includes the total for each row and column, in addition to the values already displayed.
Example of a Pandas DataFrame with Basketball Player Information
Let’s say we have a Pandas DataFrame that contains information about basketball players. The DataFrame has the following columns: Name, Age, Height, Weight, Position, and Team.
Here’s an example of what the DataFrame might look like:
import pandas as pd
# create a sample basketball player DataFrame
basketball_data = {
'Name': ['LeBron James', 'Stephen Curry', 'Kevin Durant', 'Kawhi Leonard', 'James Harden'],
'Age': [36, 32, 32, 29, 31],
'Height': ['6'9"', '6'3"', '6'10"', '6'7"', '6'5"'],
'Weight': [250, 185, 240, 225, 220],
'Position': ['SF', 'PG', 'SF', 'SF', 'SG'],
'Team': ['Los Angeles Lakers', 'Golden State Warriors', 'Brooklyn Nets', 'Los Angeles Clippers', 'Houston Rockets']
}
df = pd.DataFrame(basketball_data)
Accessing and Manipulating Pandas DataFrame
Once you have created a Pandas DataFrame, you can access and manipulate the data in a variety of ways. For example, you can use the loc[]
and iloc[]
methods to access specific rows and columns of the DataFrame.
You can also use various aggregation functions like sum()
and mean()
to calculate summary statistics of the data. Here are some examples:
# select a specific row using loc[]
row = df.loc[0]
print(row)
# select a specific column using loc[]
column = df['Name']
print(column)
# select multiple columns using loc[]
columns = df.loc[:, ['Name', 'Position']]
print(columns)
# select a specific row and column using loc[]
cell = df.loc[0, 'Name']
print(cell)
# select a subset of rows using boolean indexing
subset = df[df['Age'] > 30]
print(subset)
# calculate the mean and standard deviation of the height column
mean_height = df['Height'].mean()
std_height = df['Height'].std()
print(mean_height, std_height)
Conclusion
In this article, we have covered the basics of creating pivot tables in Pandas and using Pandas DataFrames. We have looked at the syntax for creating pivot tables, adding margins to pivot tables, and accessing and manipulating Pandas DataFrames.
Armed with this knowledge, you should be able to start using Pandas for your own data analysis tasks.
Values and Aggregation
In Pivot Tables, values and aggregation are two important concepts.
Values represent the data that we want to analyze, while aggregation refers to the statistical functions that we can use to calculate summary statistics of the data. In Pandas, we can use the pivot_table()
function to create a Pivot Table with values and aggregation.
Aggregation Functions Available in Pandas
Pandas comes with a wide range of aggregation functions to perform statistical analysis on data. These functions can be used to calculate summary statistics like mean, median, sum, count, etc.
Here are some of the most commonly used aggregation functions in Pandas:
mean()
: Calculates the arithmetic mean of the data.sum()
: Calculates the sum of the data.count()
: Counts the number of values in the data.min()
: Returns the minimum value in the data.max()
: Returns the maximum value in the data.median()
: Calculates the median of the data.std()
: Calculates the standard deviation of the data.
Examples of Using Different Aggregation Functions in Pandas Pivot Tables
Let’s go through some examples to see how different aggregation functions can be used in Pivot Tables.
Example 1: Calculating the Mean of Values in a Pivot Table
To calculate the mean of values in a Pivot Table, we can pass the ‘mean’ function as the ‘aggfunc’ parameter in the pivot_table()
function.
Here’s an example:
import pandas as pd
# load a sample dataset
df = pd.read_csv('sales.csv')
# create a pivot table with mean of sales by month
pivot_table = df.pivot_table(values='sales', index='month', aggfunc='mean')
print(pivot_table)
The output of the above code will be a Pivot Table that summarizes the monthly sales data with the mean of sales as the metric.
Example 2: Calculating the Sum of Values in a Pivot Table
To calculate the sum of values in a Pivot Table, we can pass the ‘sum’ function as the ‘aggfunc’ parameter in the pivot_table()
function.
Here’s an example:
import pandas as pd
# load a sample dataset
df = pd.read_csv('sales.csv')
# create a pivot table with sum of sales by month
pivot_table = df.pivot_table(values='sales', index='month', aggfunc='sum')
print(pivot_table)
The output of the above code will be a Pivot Table that summarizes the monthly sales data with the sum of sales as the metric.
Indexing and Columns
In Pivot Tables, Indexing and Columns are two important concepts.
Indexing refers to the values that are used to group and organize the data in a Pivot Table. Columns refer to the metric or attribute that we want to analyze in the Pivot Table.
In Pandas, we can use the pivot_table()
function to create a Pivot Table with indexing and columns.
Example of Using Indexing and Multiple Columns in Pandas Pivot Table
Let’s go through an example to see how to use indexing and multiple columns in a Pivot Table.
import pandas as pd
# load a sample dataset
df = pd.read_csv('sales.csv')
# create a pivot table with sum of sales by month and city
pivot_table = df.pivot_table(values='sales', index='month', columns='city', aggfunc='sum')
print(pivot_table)
The output of the above code will be a Pivot Table that summarizes the monthly sales data by city, with the sum of sales as the metric.
Subtotal and Totals When Using Indexing and Columns in Pandas Pivot Table
Subtotal and Totals are useful features in Pivot Tables that allow us to see the sum or mean of the data across rows and columns. In Pandas, we can add subtotals and totals to a Pivot Table by using the ‘margins’ parameter in the pivot_table()
function.
import pandas as pd
# load a sample dataset
df = pd.read_csv('sales.csv')
# create a pivot table with sum of sales by month and city, and subtotal and totals
pivot_table = df.pivot_table(values='sales', index='month', columns='city', aggfunc='sum', margins=True)
print(pivot_table)
The output of the above code will be a Pivot Table that summarizes the monthly sales data by city, with the total and subtotal of sales at the end of each row and column.
Conclusion
In this article, we have covered the basics of values, aggregation, indexing, and columns in Pandas Pivot Tables. We have seen how to use different aggregation functions in Pivot Tables and how to use indexing and columns to organize and analyze the data.
We have also looked at how to add subtotals and totals to a Pivot Table. With these concepts, you should be able to analyze your data more effectively and obtain meaningful insights from it.
Visualization of Pivot Tables
Pivot Tables are a powerful tool for analyzing data, but they can be even more effective when combined with visualization. Visualizing Pivot Tables allows you to explore the data in more detail and identify patterns or trends that may not be obvious from the bare numbers.
In this article, we will cover the basics of visualizing Pivot Tables in Pandas, using the popular plotting library – Matplotlib.
Visualizing Pandas Pivot Tables
Pandas provides excellent support for data visualization through its integration with the Matplotlib library. With Matplotlib, you can create a variety of plots, such as bar charts, line charts, scatter plots, and more, to visualize your Pivot Table data.
Visualization brings life to the data, and helps us to understand it better. Through visualization, we can analyze the data and identify any patterns or trends in them.
Examples of Visualizing Pivot Tables using Matplotlib
Let us see the example of visualizing a simple Pandas Pivot Table using Matplotlib. To create visualizations, we need to import Matplotlib library along with Pandas.
Let’s have a glance:
import pandas as pd
import matplotlib.pyplot as plt
# Create dataframe
df = pd.DataFrame({'Item': ['Apple', 'Grape', 'Banana', 'Orange'],
'Sales2019': [55, 70, 45, 80],
'Sales2020': [60, 65, 70, 85]})
# Create pivot table
pivot = df.pivot(index='Item', columns='Year', values='Sales')
# Create bar chart
pivot.plot.bar(rot=0)
# Show plot
plt.show()
The output of the code will be a bar chart of sales of items from 2019 to 2020.
Creating Different Types of Plots for Pandas Pivot Tables
We can create different types of plots for visualizing the Pivot Tables. Here are some of the commonly used plots:

Bar Charts
Bar charts are useful for comparing the values of different categories with each other. It is suitable when we have a few distinct categories with numerical values.
Copyimport matplotlib.pyplot as plt # create a pivot table with sum of sales by month pivot_table = df.pivot_table(values='sales', index='month', aggfunc='sum') # create a bar chart pivot_table.plot(kind='bar') # display the plot plt.show()
The code above will create a bar chart that summarizes the monthly sales data with the sum of sales as the metric.

Line Charts
Line charts are suitable for visualizing time series data or data that changes over time. It is best when we want to show the changes in values over a continuum, such as time or frequency.
Copyimport matplotlib.pyplot as plt # create a pivot table with mean of sales by month and city pivot_table = df.pivot_table(values='sales', index='month', columns='city', aggfunc='mean') # create a line chart pivot_table.plot(kind='line') # display the plot plt.show()
The code above will create a line chart that summarizes the monthly sales data by city, with the mean of sales as the metric.

Scatter Plots
Scatter plots are useful for visualizing the relationship between two variables. When two variables are correlated, a scatter plot can reveal any patterns or trends in the data.
Copyimport matplotlib.pyplot as plt # create a pivot table with sum of sales by month and city pivot_table = df.pivot_table(values='sales', index='month', columns='city', aggfunc='sum') # create a scatter plot pivot_table.plot(kind='scatter', x='city1', y='city2') # display the plot plt.show()
The code above will create a scatter plot that summarizes the monthly sales data by city, with the sum of sales as the metric.
Conclusion
In this article, we have covered the basics of visualizing Pivot Tables in Pandas using the Matplotlib library. We have seen how to create different types of plots, such as bar charts, line charts, and scatter plots, to visualize our Pivot Table data.
With the help of these plots, we can explore our data more effectively and gain valuable insights from it. Visualization is an essential tool in data analysis, and with Pandas and Matplotlib, it is now easier than ever to make sense of the data.
In conclusion, the article covered the fundamentals of creating pivot tables in Pandas and using Pandas DataFrames. We explored the concept of values and aggregation, indexing and columns, and visualization of pivot tables using Matplotlib.
We discussed the various aggregation functions, plotting options, and added subtotals and totals to pivot tables. Visualizing pivot tables allows us to explore data in more detail, identify trends and patterns that might not be obvious otherwise, and analyze data to gain insights.
By using pandas pivot tables effectively and visualizing data, we can make better datadriven decisions. In summary, the article provided an indepth overview of creating, manipulating, and visualizing pivot tables and their importance in data analysis and decisionmaking.