How to Obtain a Union of Strings with Pandas GroupBy?

Pandas GroupBy: Obtaining a Union of Strings

In the context of Pandas, the groupby function offers a convenient way to group data based on specific columns and perform computations on the resulting groups. However, when dealing with string columns, the default aggregation functions like sum() may not always yield the desired results.

Suppose we have a DataFrame with columns 'A', 'B', and 'C', where 'C' contains string values. We can use groupby("A")["C"].sum() to get a concatenated string for each group:

<code class="python">print(df.groupby("A")["C"].sum())

# Output:
# A
# 1    Thisstring
# 2           is!
# 3             a
# 4        random
# Name: C, dtype: object</code>

To obtain a union of strings (i.e., the unique strings in each group), we can utilize a custom function that iterates over the elements of the 'C' column and creates a comma-separated string surrounded by braces.

<code class="python">def get_string_union(group):
    return "{%s}" % ', '.join(group['C'].unique())

df.groupby('A')['C'].apply(get_string_union)

# Output:
# A
# 1    {This, string}
# 2           {is, !}
# 3               {a}
# 4          {random}
# Name: C, dtype: object</code>

Another approach involves using the apply function along with a lambda expression:

<code class="python">df.groupby('A')['C'].apply(lambda x: "{%s}" % ', '.join(x))

# Output:
# A
# 1    {This, string}
# 2           {is, !}
# 3               {a}
# 4          {random}
# Name: C, dtype: object</code>

When applied to a larger DataFrame, the custom function can be utilized to return a Series containing the desired union of strings for each group:

<code class="python">def f(x):
     return Series(dict(A = x['A'].sum(), 
                        B = x['B'].sum(), 
                        C = "{%s}" % ', '.join(x['C'])))

df.groupby('A').apply(f)

# Output:
#   A         B               C
# A                             
# 1  2  1.615586  {This, string}
# 2  4  0.421821         {is, !}
# 3  3  0.463468             {a}
# 4  4  0.643961        {random}</code>

By utilizing custom functions or the apply function with a lambda expression, Pandas allows us to manipulate and obtain specific results from data containing string columns. The aforementioned methods provide convenient ways to combine the unique strings in each group and return them in a desired format.

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