Grouping data in Pandas is a powerful operation that allows us to split our data into groups based on one or more criteria and then perform aggregate operations on each group. We can use the groupby() method to group data in Pandas. Here’s how to do it:

Syntax of the groupby() method:

grouped = df.groupby(by)
  • df: The DataFrame we want to group.
  • by: The column or columns by which we want to group the data. It can be a single column name or a list of column names.

Once we have a grouped object, we can perform various aggregation operations on it. Here are some common examples:

  1. Basic Grouping: Group data by a single column:
   import pandas as pd

   data = {
       'Category': ['A', 'B', 'A', 'B', 'A', 'B'],
       'Value': [10, 20, 30, 40, 50, 60]

   df = pd.DataFrame(data)
   grouped = df.groupby('Category')

   # Calculate the mean value for each group
   mean_values = grouped['Value'].mean()
  1. Grouping by Multiple Columns: We can group by multiple columns by passing a list of column names to groupby():
   grouped = df.groupby(['Category', 'Subcategory'])

   # Calculate the sum of values for each group
   sum_values = grouped['Value'].sum()
  1. Aggregation Functions: We can use various aggregation functions like mean(), sum(), max(), min(), count(), etc., to perform calculations on each group:
   # Calculate the total count of each group
   count_values = grouped['Value'].count()

   # Calculate the maximum value for each group
   max_values = grouped['Value'].max()
  1. Custom Aggregation Functions: We can also apply custom aggregation functions using the agg() method:
   # Define a custom aggregation function
   def custom_agg(series):
       return series.mean() - series.min()

   # Apply the custom aggregation function
   custom_result = grouped['Value'].agg(custom_agg)
  1. Iterating Over Groups: We can iterate over the groups and access each group’s data:
   for group_name, group_data in grouped:
       print(f"Group: {group_name}")
  1. Grouping with as_index=False: By default, groupby() makes the grouping columns the index of the resulting DataFrame. We can use as_index=False to keep them as regular columns:
   grouped = df.groupby('Category', as_index=False)

These are some of the common techniques for grouping data in Pandas. Grouping is often followed by aggregation operations, allowing us to summarize and analyze data within each group effectively.

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