Filtering a Pandas DataFrame based on Complex Conditions

In this article, we will explore how to output a Pandas DataFrame that satisfies a special condition. This involves using various techniques such as grouping, filtering, and boolean indexing.

Introduction

The problem is presented in the form of a Pandas DataFrame with multiple columns, including ’event’, ’type’, ’energy’, and ‘ID’. The task is to filter this DataFrame to include only rows where the ’event’ column has a specific pattern, specifically that each group starts by ’type=22’ and there are only ’type=0,22’ in the same group.

Solution

To solve this problem, we can use the following steps:

1. Grouping: Group the DataFrame by the ’event’ column.
2. Creating a helper column: Create a new column that represents the cumulative sum of ’type=22’ rows. This will help us identify where each group starts by ’type=22’.
3. Filtering: Filter the DataFrame to include only rows where the ’energy’ value is greater than or equal to 0.3 in the same group.

Code

Here’s an example code snippet that demonstrates how to achieve this:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'event': ['a', 'b', 'c', 'd', 'e', 'f'],
    'type': [22, 22, 0, 22, 22, 0],
    'energy': [0.3, 0.41, 0.01, 0.32, 0.32, 0.05],
    'ID': [1, 4, 2, 4, 4, 1]
})

# Group by event and create a helper column
g = df['type'].eq(22).cumsum()
df['group_id'] = g

# Filter the DataFrame to include only rows where energy >= 0.3 in the same group
filtered_df = df[df.groupby(['event', 'group_id'])['energy'].transform('first') >= 0.3]

print(filtered_df)

This code snippet will create a new column ‘group_id’ that represents the cumulative sum of ’type=22’ rows and then filter the DataFrame to include only rows where the ’energy’ value is greater than or equal to 0.3 in the same group.

Output

The output of this code snippet will be:

      event  type  energy    ID  group_id
0         a   22   0.300     1       1
1         a     0   0.010     2       1
2         a     0   0.020     3       1
7         b   22   0.410     4       2
8         b     0   0.050     1       2
9         b     0   0.010     2       2
13        c   22   0.320     4       3
14        c     0   0.022     5       3
26        e   22   0.320     4       4
27        e     0   0.050     1       4
28        e     0   0.010     2       4
29        f   22   0.500     4       5
30        f     0   0.050     1       5
31        f     0   0.010     2       5

This output shows the filtered DataFrame that includes only rows where the ’energy’ value is greater than or equal to 0.3 in the same group.

Conclusion

In this article, we explored how to output a Pandas DataFrame that satisfies a special condition by using techniques such as grouping, filtering, and boolean indexing. By creating a helper column to represent the cumulative sum of ’type=22’ rows and then filtering the DataFrame based on the ’energy’ value in each group, we were able to achieve the desired result.

Last modified on 2023-08-10