Splitting a Pandas DataFrame Based on Number of Rows with a Column Value

When working with large datasets, it’s common to need to split data into smaller subsets based on certain criteria. In this article, we’ll explore how to achieve this using the Pandas library in Python.

Understanding the Problem

The problem at hand involves splitting a pandas DataFrame into two separate DataFrames. The first DataFrame should contain a specified number of rows for each unique value in a particular column, and the second DataFrame should contain the remaining rows.

For example, let’s consider a DataFrame df with an “animal” column containing different animals as rows and a corresponding value. We want to split this DataFrame into two DataFrames: one containing 40 rows for each animal and another containing 10 rows for each animal.

| Animal    | Value |
|-----------|-------|
| dog       | 12    |
| cat       | 14    |
| dog       | 10    |
| ...       | ...   |

We can achieve this by using the groupby method in combination with the sample function.

Using GroupBy and Sample

The groupby method groups the DataFrame based on the values in the specified column. In our case, we’re grouping by the “animal” column.

df.groupby('animal', group_keys=False).apply(lambda x: x.sample(frac=0.2))

This code groups the DataFrame and then applies a lambda function to each group. The frac parameter specifies the proportion of rows to sample from each group (in this case, 20% or 0.2).

However, simply applying the sample function doesn’t achieve our desired result. We need to adjust the sampling fraction so that we end up with the correct number of rows for each animal.

Adjusting the Sampling Fraction

To calculate the optimal sampling fraction, let’s first count the total number of rows in the original DataFrame and then divide this by the desired number of rows per animal (in our case, 40).

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'animal': ['cat', 'dog', 'lion'] * 10,
    'value': [12, 13, 14] * 10
})

# Calculate the total number of rows and desired number of rows per animal
total_rows = len(df)
desired_rows_per_animal = 40

# Calculate the optimal sampling fraction
optimal_fraction = (desired_rows_per_animal / total_rows) / 2.0

With this adjustment, we can now apply the sample function with the correct sampling fraction to achieve our desired result.

Final Solution

Here’s the complete code:

df_sixty = df.groupby('animal', group_keys=False).apply(lambda x: x.sample(frac=optimal_fraction))

We’ll then remove these rows from the original DataFrame using drop to create the second DataFrame with 10 rows per animal.

Code Example

Here’s a code example that demonstrates this approach:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'animal': ['cat', 'dog', 'lion'] * 10,
    'value': [12, 13, 14] * 10
})

# Calculate the total number of rows and desired number of rows per animal
total_rows = len(df)
desired_rows_per_animal = 40

# Calculate the optimal sampling fraction
optimal_fraction = (desired_rows_per_animal / total_rows) / 2.0

# Split the DataFrame using GroupBy and Sample
df_sixty = df.groupby('animal', group_keys=False).apply(lambda x: x.sample(frac=optimal_fraction))

# Remove rows from original DataFrame to create second DataFrame with 10 rows per animal
df_tens = df.drop(df_sixty.index)

print("DataFrame with 40 rows per animal:")
print(df_sixty.head())

print("\nDataFrame with 10 rows per animal:")
print(df_tens.head())

Conclusion

In this article, we demonstrated how to split a pandas DataFrame into two separate DataFrames based on the number of rows with a particular column value. We used the groupby method in combination with the sample function to achieve this result.

By understanding how to calculate the optimal sampling fraction and adjust it accordingly, we can create custom splitting schemes for our data as needed.

Last modified on 2023-07-28