In Pandas, the groupby method is a powerful tool for aggregating and analyzing data based on specific criteria. When seeking divided values of two columns resulting from a groupby operation, you can use various techniques. In this article, we will explore three different methods/approaches to get the divided values of two columns that are a result of a groupby method.

Techniques for Getting Divided Values

Below are the possible approaches to get the divided values of two columns that are a result of a groupby method.

Method 1: Using Apply with a Custom Function

In this approach, we are using the apply method in Pandas along with a lambda function to divide the ‘Value1’ column by the ‘Value2’ column within each group defined by the ‘Category’ column. The groupby operation groups the DataFrame based on ‘Category’, and then the lambda function calculates the division for each group. Finally, reset_index(level=0, drop=True) is used to flatten the resulting Series, removing the grouped index level for a cleaner output.

Syntax:

result_series = df.apply(custom_function, axis=1)

• axis=1 specifies that the function should be applied to each row. Use axis=0 for applying the function to each column.

Example:

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B', 'A', 'B'],
        'Value1': [10, 20, 30, 40, 50, 60],
        'Value2': [5, 10, 15, 20, 25, 30]}
df = pd.DataFrame(data)

# Group by 'Category' and apply a custom function to divide 'Value1' by 'Value2'
result1 = df.groupby('Category').apply(lambda x: x['Value1'] / x['Value2']).reset_index(level=0, drop=True)
print(result1)

0    2.0
1    2.0
4    2.0
2    2.0
3    2.0
5    2.0
dtype: float64

Method 2: Using pivot_table

In this approach, we use the pivot_table function in Pandas to create a summary table where ‘Category’ serves as the index, and the values ‘Value1’ and ‘Value2’ are summed up based on each category. Then, we calculate the division directly between the summed ‘Value1’ and ‘Value2’ columns, resulting in a Series with the division results for each category. Sorting the resulting Series by the index ensures the output is in the desired order.

Syntax:

pd.pivot_table(data, values=None, index=None, columns=None, aggfunc=’mean’, fill_value=None, margins=False, dropna=True, margins_name=’All’)

• data: DataFrame to be used for pivoting.
• values: Column(s) to aggregate. Defaults to all numeric columns.
• index: Column(s) to group by on the rows (pivot index).
• aggfunc: Aggregation function to use. Default is ‘mean’. Can be a function, list of functions, or dictionary of column names and functions.

Example:

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B', 'A', 'B'],
        'Value1': [10, 20, 30, 40, 50, 60],
        'Value2': [5, 10, 15, 20, 25, 30]}
df = pd.DataFrame(data)

# Create a pivot table to calculate the division directly
pivot_result = df.pivot_table(index='Category', values=['Value1', 'Value2'], aggfunc='sum')
result2 = pivot_result['Value1'] / pivot_result['Value2']

# Sort the resulting Series by the index
result2_sorted = result2.sort_index()
print(result2_sorted)

Category
A    2.0
B    2.0
dtype: float64

Method 3: Using eval with String Expression

In this approach, we use the eval method in Pandas to directly compute the division of ‘Value1’ by ‘Value2’ within the DataFrame. The string expression ‘Value1 / Value2’ passed to eval specifies the division operation. The resulting Series contains the division results for each corresponding row in the DataFrame. Using reset_index(drop=True) ensures the Series is reset with a continuous index for a cleaner output.

Syntax:

DataFrame.eval(expr, inplace=False, **kwargs)

• expr: String expression to evaluate. This can be a simple mathematical expression, a logical expression, or a combination of both.
• inplace: Optional parameter to specify whether to modify the DataFrame in place. Default is False, which returns a new DataFrame.
• kwargs: Additional keyword arguments that can be passed to the expression, such as local variables.

Example:

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B', 'A', 'B'],
        'Value1': [10, 20, 30, 40, 50, 60],
        'Value2': [5, 10, 15, 20, 25, 30]}
df = pd.DataFrame(data)

# Group by 'Category' and use eval with a string expression to calculate the division
result3 = df.eval('Value1 / Value2').reset_index(drop=True)
print(result3)

0    2.0
1    2.0
2    2.0
3    2.0
4    2.0
5    2.0
dtype: float64

Conclusion

In conclusion, for obtaining the divided values of two columns after a groupby operation in Pandas, consider using the apply method with a custom function for detailed calculations, pivot_table for a summarized overview with division, or eval with a string expression for direct computation, ensuring efficient and proper data manipulation.