# Pandas: How to create a pivot table with count/sum/average of values

Pandas, a powerful and flexible Python library, is synonymous with data manipulation and analysis. Among its numerous features, the pivot table function stands out for its ability to summarize and analyze large datasets in a simple manner. In this tutorial, we will dive deep into how to create pivot tables in Pandas to count, sum, and average values, complete with step-by-step examples.

## Introduction to Pivot Tables in Pandas

Pivot tables are a quintessential tool for data analysis, allowing for the summarization of data. They enable the aggregation of data points based on columns and calculation of statistics such as counts, sums, and averages. Pandasâ€™ pivot table functionality brings this power into the Python environment, enhancing data analysis workflows.

### Getting Started

First, ensure you have Pandas installed:

``pip install pandas``

Then, import Pandas in your Python script:

``import pandas as pd``

### Basic Pivot Table

To create a basic pivot table, we start with a simple DataFrame:

``````data = {
'Product': ['Apples', 'Bananas', 'Oranges', 'Apples', 'Oranges'],
'Sales': [20, 30, 40, 50, 60],
'Region': ['East', 'West', 'East', 'West', 'East']
}
df = pd.DataFrame(data)
``````

Creating a pivot table to count the number of sales per product:

``````pd.pivot_table(df, values='Sales', index='Product', aggfunc='count')
``````

### Sum and Average

For summarizing sales, you can also calculate the sum and average:

``````pd.pivot_table(df, values='Sales', index='Product', aggfunc=['sum', 'mean'])
``````

Moving towards more complex scenarios, we consider another DataFrame:

``````data = {
'Product': ['Apples', 'Bananas', 'Oranges', 'Apples', 'Oranges', 'Bananas'],
'Sales': [20, 30, 40, 50, 60, 70],
'Region': ['East', 'West', 'East', 'West', 'East', 'West'],
'Month': ['January', 'February', 'January', 'February', 'January', 'February']
}
df = pd.DataFrame(data)
``````

We can use the pivot table to analyze sales data across regions and months:

``````pd.pivot_table(df, values='Sales', index=['Region', 'Month'], columns='Product', aggfunc={'Sales': ['sum', 'mean']})
``````

Further exploring the capabilities of Pandasâ€™ pivot table, we delve into multi-level indexing, conditional aggregations, and incorporating multiple aggregation functions.

``````pd.pivot_table(df, values='Sales', index=['Region', 'Month'], aggfunc={'Sales': 'sum', 'Product': lambda x: x.mode()[0]})
``````

### Handling Missing Data

In cases where your dataset includes missing values, Pandas pivot tables offer parameters like `fill_value` and `dropna` to handle them effectively:

``````pd.pivot_table(df, values='Sales', index=['Region', 'Month'], columns='Product', fill_value=0, dropna=False)
``````

### Visualization

Understanding data through visualization is a powerful technique. Pandas pivot tables can be directly used with libraries like Matplotlib and Seaborn, for graphical representation:

``````import matplotlib.pyplot as plt
import seaborn as sns

# Assuming `pivot` is a previously created pivot table
sns.heatmap(pivot)
plt.show()
``````

## Conclusion

In conclusion, the Pandasâ€™ pivot table function is a versatile and powerful tool in the data analystâ€™s arsenal, enabling effective summarization and analysis of data through counting, summing, and averaging values. Through this tutorial, you should now have a solid understanding of creating basic to advanced pivot tables, expanding your data analysis capabilities.

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