Understanding the Problem

In this article, we will explore how to get a distribution of posts per subreddit for each author in a pandas DataFrame. The problem arises when trying to compare distributions across authors, as they may have posted in different subreddits.

We’ll break down the solution step by step and discuss the concepts involved in achieving this goal efficiently.

Introduction to Pandas

Pandas is a powerful Python library used for data manipulation and analysis. It provides data structures such as Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types).

In this article, we’ll focus on the DataFrame data structure, which is ideal for handling tabular data.

The Problem - Getting Distribution Per Group

The problem at hand involves getting a distribution of posts per subreddit for each author. We start by grouping our DataFrame ‘df’ by the ‘author’ column and calculating the value counts of the ‘subreddit’ column using value_counts(). We then divide this count by the total number of posts made by each author in all subreddits using count().

# Grouping by author and calculating subreddit value counts and total posts
sub_visits = df.groupby('author').subreddit.value_counts()
sub_visits = sub_visits.div(df.groupby('author').subreddit.count(), axis=0)

This gives us a multi-indexed pandas DataFrame with the author as the first index, all unique subreddits in the second index, and the fraction of posts in each subreddit as values.

The Current Solution

The user then includes all subreddits from the entire df in a new DataFrame ‘pdf’ with authors as rows. They fill this DataFrame with zeros and then fill it with the values from sub_visits. This is done using a for loop to iterate over the groups of sub_visits and assign the corresponding subreddit fraction to the DataFrame.

# Creating a new DataFrame with all subreddits and filling with zeros
pdf = pd.DataFrame(index=df.author.unique(), columns=all_subs)
pdf = pdf.fillna(0)

# Filling values from sub_visits into pdf
for idx, df_select in sub_visits.groupby(level=[0, 1]):
    pdf.loc[idx[0],idx[1]] = df_select[0]

An Efficient Solution Using Pandas Operations

However, this approach can be slow due to the use of a for loop. A more efficient way to achieve this is by using pandas operations directly on sub_visits.

First, we unstack the DataFrame along the last axis (-1) to convert it into a matrix-like structure with subreddits as columns.

# Unstacking sub_visits along the last axis to get a matrix-like structure
sub_visits = sub_visits.unstack(-1)

This allows us to access each subreddit fraction for an author by its index in the DataFrame. We can then fill this DataFrame with zeros and assign values from sub_visits using vectorized operations.

# Filling values from sub_visits into a new DataFrame filled with zeros
sub Visits = sub_visits.fillna(0)

Conclusion

In conclusion, getting a distribution of posts per subreddit for each author in a pandas DataFrame can be achieved efficiently by leveraging pandas operations. The key concepts involved here are data grouping, value counts, and unstacking. By using these techniques, we can create a distribution matrix that allows us to compare the posting frequency across authors.

# Getting the distribution of posts per subreddit for each author
sub_visits = df.groupby('author').subreddit.value_counts() / df.groupby('author').subreddit.count()
sub_visits = sub_visits.unstack(-1)
sub_visits = sub_visits.fillna(0)

# Resulting DataFrame with distribution of posts per subreddit for each author
print(sub_visits)

Last modified on 2023-08-30