Concatenate Strings from Multiple Rows using Pandas Groupby
When working with dataframes, there may be situations where you need to consolidate strings from multiple rows while grouping them by specific criteria. Pandas offers a convenient solution for this through its groupby and transform functions.
Problem Statement
Given a dataframe with columns 'name,' 'text,' and 'month,' the goal is to concatenate the strings in the 'text' column for each unique combination of 'name' and 'month.' The desired output is a dataframe with unique 'name' and 'month' combinations and the concatenated 'text' values.
Solution
To achieve this, you can utilize the following steps:
Here's an example code:
import pandas as pd from io import StringIO data = StringIO(""" "name1","hej","2014-11-01" "name1","du","2014-11-02" "name1","aj","2014-12-01" "name1","oj","2014-12-02" "name2","fin","2014-11-01" "name2","katt","2014-11-02" "name2","mycket","2014-12-01" "name2","lite","2014-12-01" """) # load string as stream into dataframe df = pd.read_csv(data, header=0, names=["name", "text", "date"], parse_dates=[2]) # add column with month df["month"] = df["date"].apply(lambda x: x.month) df['text'] = df[['name','text','month']].groupby(['name','month'])['text'].transform(lambda x: ','.join(x)) df[['name','text','month']].drop_duplicates()
The above code generates a dataframe with the desired result:
name text month 0 name1 hej,du 11 2 name1 aj,oj 12 4 name2 fin,katt 11 6 name2 mycket,lite 12
Alternative Solution
Instead of using transform(), you can also utilize apply() and then reset_index() to achieve the same result. The updated code would be:
df.groupby(['name','month'])['text'].apply(','.join).reset_index()
This simplified version eliminates the lambda expression and provides a more concise solution.
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