Using Pandas Pivot Table to Analyze Data: A Guide for Beginners

Understanding the Error in Pandas Pivot Table

When working with data analysis, using pandas can simplify tasks significantly. One common operation is creating a pivot table to summarize data from multiple sources into one table. In this case, we’re trying to create a new DataFrame that has the total number of athletes and the total number of medals won by type for each country.

The Problem

The problem arises when we try to use pandas pivot_table() function in an unexpected way. Specifically, we are trying to use it with multiple columns as both index and values.

out = olymp.pivot_table(index='NOC', values=['ID','Medal'],
                        aggfunc={'ID':pd.Series.nunique, 'Medal':'count'}) \
           .sort_values('Medal', ascending=False)

What’s Wrong?

The issue here is that we can’t use the same column for both columns and values. When you specify values in pandas pivot_table(), it expects a single list or array of column names to be summed. In our case, this results in an error: “ValueError: Grouper for ‘ID’ not 1-dimensional”.

Understanding Aggregation Functions

To solve this problem, we need to understand how aggregation functions work in pandas. When you use the agg() function with a dictionary, where each key is a column name and the value is an aggregation function, it will apply that aggregation function to each group.

out = df.pivot_table('ID', 'NOC', 'Medal', aggfunc='count', fill_value=0)

However, in our case, we need to handle both ID and Medal columns differently. We want to count the unique number of athletes (nunique() function) for each country, while counting the total number of medals.

A Correct Approach

We can achieve this by using two separate pivot tables. However, since you mentioned that you would like to use a single pivot table statement, we need to rethink our approach.

One way to do it is by applying aggregate functions separately and then combining the results:

out['ID'] = df[df['Medal'].notna()].groupby('NOC')['ID'].nunique()

out = olymp.pivot_table('Medal', 'NOC', aggfunc='count', fill_value=0)

However, this approach still doesn’t give us the total number of athletes for each country. To fix that, we need to use the groupby() function again.

out['ID'] = df[df['Medal'].notna()].groupby('NOC')['ID'].nunique()

out = olymp.groupby('NOC').agg({'ID': 'count', 'Medal': 'sum'})

Conclusion

Creating a pivot table with multiple columns as both index and values can lead to unexpected errors. By understanding how aggregation functions work in pandas, we can find alternative ways to achieve our goals.

When working with data analysis, it’s always a good idea to experiment with different approaches until you find the one that works best for your specific problem. With practice and patience, you’ll become more proficient at using pandas and other tools to analyze and manipulate data.

Example Use Cases

Here are some example use cases where this concept can be applied:

Creating a summary of sales by region and product
Calculating the average salary by department and location
Analyzing website traffic by page type and country

These scenarios often involve grouping data by multiple columns, which is exactly what we’ve discussed in this article.

Advanced Concepts

In more advanced cases, you might need to use groupby() with custom aggregation functions or even implement your own pivot table using loops. Here’s an example of how you can do it:

import pandas as pd

# Assuming df is a DataFrame with columns 'NOC', 'ID', and 'Medal'
def pivot_table(df):
    # Create an empty list to store the results
    out = []

    # Loop over each country in the data
    for nof, group in df.groupby('NOC'):
        # Calculate the total number of athletes
        athletes = group['ID'].nunique()

        # Calculate the total number of medals
        medals = group['Medal'].sum()

        # Append the results to the list
        out.append({'NOC': nof, 'Athletes': athletes, 'Medals': medals})

    # Convert the list to a DataFrame and return it
    return pd.DataFrame(out)

# Call the function with the data
df = pivot_table(olymp)
print(df)

This code creates an empty list to store the results and then loops over each country in the data, calculating the total number of athletes and medals. The results are then appended to the list, which is converted to a DataFrame at the end.

While this approach might be more verbose than using pandas built-in functions, it can be useful when you need to implement a custom pivot table or handle complex grouping scenarios.

Last modified on 2024-08-05