Concatenate strings from several rows using Pandas groupby

Advanced aggregation techniques

Stringing along multiple columns

More hands means lighter work. When dealing with multiple columns, agg() can ensure each gets the customised operation it deserves:

result = df.groupby(['Group', 'Column2']).agg({'Data': ' '.join, 'NumericColumn': 'sum'}).reset_index()
# Sure, we're good at stringing along. But we can also sum! See?
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Tidying up the joint

No one likes a messy joint (concatenated string!). Use str.replace() to tidy up unwanted characters:

result['Data'] = result['Data'].str.replace('[^\w\s]', '', regex=True)
# Back in my day, strings were well-behaved...
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Stay in formation, pandas!

Original dataframe formation is important! Make sure to call transform() to keep things tidy:

df['Concatenated'] = df.groupby('Group')['Data'].transform(lambda x: ' '.join(x))
# Transform and roll out, pandas!
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Let's get statistical

When you have multiple statistics to calculate, let agg() work its magic with your groupby:

df.groupby('Group').agg({'Data': ' '.join, 'Numerical': ['mean', 'sum']})
# Who knew pandas could also be statisticians
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Date-time groupings

Working with time-series data? Transform dates to datetime to group them in periods like month, quarter, or year:

df['Month'] = pd.to_datetime(df['Month'])
grouped = df.groupby([df.Month.dt.to_period('M'), 'Group'])
# Time-travel with pandas!
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Handling numeric data within groups

When the group has non-text data, use the correct aggregation function such as mean, sum, min, max along with string concatenation:

result = df.groupby('Group').agg({'Data': ' '.join, 'Value': 'mean'}).reset_index()
# Because pandas aren't just about texts. Numbers are friends, not food!
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Keeping an eye on the final formation

Ensure the output structure aligns with your objective. Use df.drop_duplicates() and df.set_index() to get it just right:

result.drop_duplicates(inplace=True)
# or
result.set_index('Group', inplace=True)
# Be like a panda: unique and organized!
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