Unaggregating a Pandas DataFrame
In this article, we will explore the process of unaggregating a pandas DataFrame that has been aggregated by location. We will start with an example DataFrame and walk through the steps to achieve the desired output.
Introduction
When working with DataFrames in pandas, it’s often necessary to perform aggregations based on certain criteria. However, sometimes we need to “un-aggregate” this data to get back to a more detailed level. In this article, we will focus on unaggregating a DataFrame that has been aggregated by location.
The Aggregated DataFrame
Let’s start with an example of what the aggregated DataFrame might look like:
location_id | score | number_of_males | number_of_females
1 | 20 | 2 | 1
2 | 45 | 1 | 2
As you can see, this DataFrame has been aggregated by location. The location_id column represents the group or category, while the score, number_of_males, and number_of_females columns represent the aggregated values for each location.
Creating a New DataFrame with Unaggregated Data
We want to create a new DataFrame that unaggregates this data. We’ll aim for an output like this:
location_id | score | sex
1 | 20 | male
1 | 20 | female
2 | 45 | male
2 | 45 | female
2 | 45 | female
Notice that each row in the original aggregated DataFrame is now expanded into multiple rows in the new unaggregated DataFrame.
Using a Loop to Append Rows
One way to achieve this is by using a loop to append rows to the new DataFrame. Here’s an example:
import pandas as pd
# Create the original DataFrame
original_df = pd.DataFrame({
'location_id': [1, 2],
'score': [20, 45],
'number_of_males': [2, 1],
'number_of_females': [1, 2]
})
# Create a new DataFrame with columns for the unaggregated data
unaggregated_df = pd.DataFrame(columns=['location_id', 'score', 'sex'])
# Loop through each row in the original DataFrame
for index, row in original_df.iterrows():
# Calculate the number of males and females
num_males = row['number_of_males']
num_females = row['number_of_females']
# Create a list to store the unaggregated rows
unaggregated_rows = []
# Loop through each possible value for males
for i in range(num_males + 1):
male_row = {
'location_id': row['location_id'],
'score': row['score'],
'sex': 'male' if i < num_males else None
}
unaggregated_rows.append(male_row)
# Loop through each possible value for females
for j in range(num_females + 1):
female_row = {
'location_id': row['location_id'],
'score': row['score'],
'sex': 'female' if j < num_females else None
}
unaggregated_rows.append(female_row)
# Append the unaggregated rows to the DataFrame
for row in unaggregated_rows:
unaggregated_df = pd.concat([unaggregated_df, pd.DataFrame([row])], ignore_index=True)
However, this approach is not very pandas-like. It’s repetitive and can lead to issues with data integrity.
Using groupby and melt
A better way to achieve this is by using the groupby and melt functions from pandas:
import pandas as pd
# Create the original DataFrame
original_df = pd.DataFrame({
'location_id': [1, 2],
'score': [20, 45],
'number_of_males': [2, 1],
'number_of_females': [1, 2]
})
# Define the IDs for grouping and melting
ids = ['location_id', 'score']
# Define a function to create the unaggregated rows
def foo(d):
return pd.Series(d['number_of_males'].values*['male'] +
d['number_of_females'].values*['female'])
# Apply the function to each group and melt the result
unaggregated_df = pd.melt(pd.groupby(original_df[ids].apply(foo).reset_index(), id_vars=ids).drop('variable', 1))
print(unaggregated_df)
This approach is more pandas-like. It takes advantage of the groupby function to group the data by location and score, then applies a function to each group to create the unaggregated rows.
Conclusion
In this article, we explored the process of unaggregating a pandas DataFrame that has been aggregated by location. We started with an example DataFrame and walked through the steps to achieve the desired output using both a loop-based approach and the groupby and melt functions from pandas.
Last modified on 2024-10-21