How to Calculate the Percentage of Total Sales per Office Using Pandas Groupby?

Calculating Percentage of Total Sales with Pandas Groupby

To calculate the percentage of total sales for each office within a given state, you can leverage the transformative power of Pandas' groupby operation. Let's dive into the details.

Consider a CSV file with three columns: state, office_id, and sales. To group data by state and office_id and summarize sales, you can utilize df.groupby(['state', 'office_id']).agg({'sales': 'sum'}).

df = pd.DataFrame({'state': ['CA', 'WA', 'CO', 'AZ'] * 3,
                   'office_id': list(range(1, 7)) * 2,
                   'sales': [np.random.randint(100000, 999999)
                             for _ in range(12)]})

df.groupby(['state', 'office_id']).agg({'sales': 'sum'})

This operation yields a grouped DataFrame where the sum of sales is calculated for each unique (state, office_id) combination.

To determine the percentage of total sales for each office, you can implement the following strategies:

Transform Approach

Introducing the transformative power of transform! Apply it to the sales column and divide by the sum of sales within each state.

df['sales'] / df.groupby('state')['sales'].transform('sum')

This transformation yields a DataFrame with percentages, listing the fraction of each office's sales relative to the total sales within their respective states.

Multi-Groupby Approach

Alternatively, you can create a new grouped object based on the sales column within each (state, office_id) group. Then, divide by the sum of the newly grouped column.

state_office = df.groupby(['state', 'office_id']).agg({'sales': 'sum'})
state_pcts = state_office.groupby(level=0).apply(lambda x:
                                                 100 * x / float(x.sum()))

This approach gives you a similar DataFrame with percentage values, but it requires an additional level of grouping.

Both methods effectively calculate the percentage contribution of each office to the total sales within their respective states. By understanding these techniques, you can unlock new insights from your grouped data!

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