pandas - Merge nearly duplicate rows based on column value

Question

I have a pandas dataframe with several rows that are near duplicates of each other, except for one value. My goal is to merge or "coalesce" these rows into a single row, without summing the numerical values.

Here is an example of what I'm working with:

Name   Sid   Use_Case  Revenue
A      xx01  Voice     $10.00
A      xx01  SMS       $10.00
B      xx02  Voice     $5.00
C      xx03  Voice     $15.00
C      xx03  SMS       $15.00
C      xx03  Video     $15.00

And here is what I would like:

Name   Sid   Use_Case            Revenue
A      xx01  Voice, SMS          $10.00
B      xx02  Voice               $5.00
C      xx03  Voice, SMS, Video   $15.00

The reason I don't want to sum the "Revenue" column is because my table is the result of doing a pivot over several time periods where "Revenue" simply ends up getting listed multiple times instead of having a different value per "Use_Case".

What would be the best way to tackle this issue? I've looked into the groupby() function but I still don't understand it very well.

Answer 1

You can groupby and apply the list function:

>>> df['Use_Case'].groupby([df.Name, df.Sid, df.Revenue]).apply(list).reset_index()
    Name    Sid     Revenue     0
0   A   xx01    $10.00  [Voice, SMS]
1   B   xx02    $5.00   [Voice]
2   C   xx03    $15.00  [Voice, SMS, Video]

(In case you are concerned about duplicates, use set instead of list.)

Answer 2

I was using some code that I didn't think was optimal and eventually found jezrael's answer. But after using it and running a timeit test, I actually went back to what I was doing, which was:

cmnts = {}
for i, row in df.iterrows():
    while True:
        try:
            if row['Use_Case']:
                cmnts[row['Name']].append(row['Use_Case'])

            else:
                cmnts[row['Name']].append('n/a')

            break

        except KeyError:
            cmnts[row['Name']] = []

df.drop_duplicates('Name', inplace=True)
df['Use_Case'] = ['; '.join(v) for v in cmnts.values()]

According to my 100 run timeit test, the iterate and replace method is an order of magnitude faster than the groupby method.

import pandas as pd
from my_stuff import time_something

df = pd.DataFrame({'a': [i / (i % 4 + 1) for i in range(1, 10001)],
                   'b': [i for i in range(1, 10001)]})

runs = 100

interim_dict = 'txt = {}\n' \
               'for i, row in df.iterrows():\n' \
               '    try:\n' \
               "        txt[row['a']].append(row['b'])\n\n" \
               '    except KeyError:\n' \
               "        txt[row['a']] = []\n" \
               "df.drop_duplicates('a', inplace=True)\n" \
               "df['b'] = ['; '.join(v) for v in txt.values()]"

grouping = "new_df = df.groupby('a')['b'].apply(str).apply('; '.join).reset_index()"

print(time_something(interim_dict, runs, beg_string='Interim Dict', glbls=globals()))
print(time_something(grouping, runs, beg_string='Group By', glbls=globals()))

yields:

Interim Dict
  Total: 59.1164s
  Avg: 591163748.5887ns

Group By
  Total: 430.6203s
  Avg: 4306203366.1827ns

where time_something is a function which times a snippet with timeit and returns the result in the above format.

Answer 3

Following @jezrael and @leoschet answers, I would like to provide a more general example in case there are many more columns in the dataframe, something I had to do recently.

Specifically, my dataframe had a total of 184 columns.

The column REF is the one that should be used as a reference for the groupby and only another one, called IDS, of the remaining 182, was different and I wanted to collapse its elements into a list id1, id2, id3...

So:

# Create a dictionary {df_all_columns_name : 'first', 'IDS': join} for agg
# Also avoid REF column in dictionary (inserted after aggregation)
columns_collapse = {c: 'first' if c != 'IDS' else ', '.join for c in my_df.columns.tolist() if c != 'REF'}
my_df = my_df.groupby('REF').agg(columns_collapse).reset_index()

I hope this is also useful to someone!

Regards!