How to Perform Data Aggregation with Pandas?

Aggregation in Pandas

With Pandas, you can perform various aggregation operations to reduce the dimensionality and summarize data.

Question 1: How can I perform aggregation with Pandas?

Pandas provides many aggregating functions, including mean(), sum(), count(), min(), and max(). You can use these functions to calculate summary statistics for each group. For example:

# Calculate mean of each group based on 'A' and 'B' columns
df1 = df.groupby(['A', 'B']).mean()

# Print the results
print(df1)

Question 2: No DataFrame after aggregation! What happened?

When you apply aggregation to multiple columns, the resulting object can be a Series or DataFrame depending on the number of columns grouped.

• Series: If you group by one or more columns, the result is a Series with an index corresponding to the groups.
• DataFrame: If you group by only one column, the result is a DataFrame with columns corresponding to the original columns.

To get a DataFrame with all the columns, use as_index=False in the groupby function.

Question 3: How can I aggregate mainly strings columns (to lists, tuples, strings with separator)?

To aggregate strings columns, you can use list, tuple, or join operations.

• List: Convert the column to a list using list() or GroupBy.apply(list).
• Tuple: Convert the column to a tuple using tuple() or GroupBy.apply(tuple).
• String with separator: Combine the strings with a separator using str.join().

For example:

# Convert 'B' column values to a list for each group
df1 = df.groupby('A')['B'].agg(list).reset_index()

# Combine 'B' column values into a string with separator for each group
df2 = df.groupby('A')['B'].agg(','.join).reset_index()

Question 4: How can I aggregate counts?

To count non-missing values in each group, use GroupBy.count(). To count all values, including missing ones, use GroupBy.size().

For example:

# Count non-missing values in 'C' column for each group
df1 = df.groupby('A')['C'].count().reset_index(name='COUNT')

# Count all values in 'A' column for each group
df2 = df.groupby('A').size().reset_index(name='COUNT')

Question 5: How can I create a new column filled by aggregated values?

You can add a new column containing the aggregated values using the transform() method. The transform() function applies the specified operation to each group and returns a new object with the same size as the original one.

For example:

# Create a new 'C1' column with the sum of 'C' grouped by 'A'
df['C1'] = df.groupby('A')['C'].transform('sum')

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