Adding Rows to Groups in Pandas DataFrame

In this article, we’ll explore how to add rows to specific groups within a Pandas DataFrame. We’ll use two approaches: explicitly looping through each group and using the reindex method with a new index.

Introduction to Pandas DataFrames

A Pandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It’s similar to an Excel spreadsheet or a table in a relational database. The DataFrame class provides efficient data structures and operations for manipulating structured data, including tabular data such as spreadsheets and SQL tables.

In this article, we’ll focus on the groupby method, which allows us to split a DataFrame into groups based on one or more columns. We can then perform various operations on each group, such as aggregating values, calculating statistics, or adding new rows.

Looped Approach

The first approach involves explicitly looping through each group and appending dummy DataFrames while dropping duplicates. Here’s an example code snippet that demonstrates this method:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'start_timestamp_milli': [1555414708025, 1555414708025, 1555414708025, 1555414708025,
                                1555414813304, 1555414813304, 1555414813304, 1555414813304,
                                1555414921819, 1555414921819, 1555414921819],
    'end_timestamp_milli': [1555414723279, 1555414723279, 1555414723279, 1555414723279,
                             1555414831795, 1555414831795, 1555414831795, 1555414831795,
                             1555414931382, 1555414931382, 1555414931382],
    'name': ['Valence', 'Arousal', 'Dominance', 'Sadness', 'Valence', 'Arousal',
             'Dominance', 'Sadness', 'Valence', 'Arousal', 'Dominance', 'Sadness'],
    'rating': [2, 6, 2, 1, 3, 5, 2, 1, 1, 7, 2, 1]
})

# Define a dictionary to store dummy values
d = dict(name=['Anger', 'Happiness'], rating=0)

# Define columns for grouping and filtering
cols = ['start_timestamp_milli', 'end_timestamp_milli']

def f(d0, k):
    # Create a new DataFrame with dummy values
    d1 = pd.DataFrame({**dict(zip(cols, k)), **d})
    
    # Append the dummy DataFrame to the original group
    return d0.append(d1, ignore_index=True).drop_duplicates('name')

# Group by columns and append dummy DataFrames
pd.concat([f(df, k) for k, df in df.groupby(cols)], ignore_index=True)

This code snippet uses a function f that takes a group and dummy values as arguments. It creates a new DataFrame with the dummy values, appends it to the original group using append, and then drops duplicates using drop_duplicates. The resulting concatenated DataFrame is stored in the main DataFrame.

Reindexed Approach

The second approach involves creating a new index and reindexing the original DataFrame using the reindex method. Here’s an example code snippet that demonstrates this method:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'start_timestamp_milli': [1555414708025, 1555414708025, 1555414708025, 1555414708025,
                                1555414813304, 1555414813304, 1555414813304, 1555414813304,
                                1555414921819, 1555414921819, 1555414921819],
    'end_timestamp_milli': [1555414723279, 1555414723279, 1555414723279, 1555414723279,
                             1555414831795, 1555414831795, 1555414831795, 1555414831795,
                             1555414931382, 1555414931382, 1555414931382],
    'name': ['Valence', 'Arousal', 'Dominance', 'Sadness', 'Valence', 'Arousal',
             'Dominance', 'Sadness', 'Valence', 'Arousal', 'Dominance', 'Sadness'],
    'rating': [2, 6, 2, 1, 3, 5, 2, 1, 1, 7, 2, 1]
})

# Define columns for grouping and filtering
cols = ['start_timestamp_milli', 'end_timestamp_milli']

# Create a list of tuples with dummy values
cats = [('Anger', None), ('Happiness', None)]
i = pd.MultiIndex.from_tuples(cats, names=cols)

# Set the new index on the DataFrame
d = df.set_index(*cols).reindex(i, fill_value=0).reset_index()

This code snippet creates a list of tuples with dummy values using pd.MultiIndex.from_tuples. It then sets the new index on the original DataFrame using set_index and reindexes it using reindex, passing in the new index and filling missing values with 0. Finally, it resets the index using reset_index.

Conclusion

In this article, we explored two approaches to add rows to specific groups within a Pandas DataFrame: looping through each group and appending dummy DataFrames, and creating a new index and reindexing the original DataFrame. Both methods can be used to achieve the desired outcome, depending on the complexity of the problem and personal preference.

Example Use Case

Suppose we have a dataset with user ratings for movies, where each row represents a single rating. We want to add rows for missing ratings, such as “Anger” or “Happiness”, with a default value of 0. We can use the reindexed approach to achieve this by creating a new index and reindexing the original DataFrame.

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'user_id': [1, 2, 3],
    'movie_id': [101, 102, 103],
    'rating': [4, 5, 6]
})

# Define columns for grouping and filtering
cols = ['user_id', 'movie_id']

# Create a list of tuples with dummy values
cats = [('Anger', None), ('Happiness', None)]
i = pd.MultiIndex.from_tuples(cats, names=cols)

# Set the new index on the DataFrame
d = df.set_index(*cols).reindex(i, fill_value=0).reset_index()

print(d)

This code snippet creates a sample DataFrame with user ratings for movies and defines columns for grouping and filtering. It then creates a list of tuples with dummy values using pd.MultiIndex.from_tuples and sets the new index on the original DataFrame using set_index. Finally, it reindexes the DataFrame using reindex, passing in the new index and filling missing values with 0.

The resulting DataFrame has additional rows for missing ratings, such as “Anger” or “Happiness”, with a default value of 0.

Last modified on 2023-07-26