python pandas tutorial 10 pivot table

python pandas tutorial: pivot tables

pivot tables are a powerful feature in pandas that allow you to summarize and reorganize data for better analysis. they enable you to aggregate data based on one or more keys, which is particularly useful for data analysis, reporting, and visualizations.

what is a pivot table?

a pivot table is a data processing technique that summarizes and reorganizes data from a dataframe. it allows you to group data based on certain columns and perform calculations (like sum, mean, count, etc.) on other columns.

creating a pivot table with pandas

to create a pivot table in pandas, you can use the `pivot_table()` function. here's the basic syntax:

parameters:
- **values**: the column(s) to aggregate.
- **index**: column(s) to group by on the rows.
- **columns**: column(s) to group by on the columns.
- **aggfunc**: function to aggregate the values; can be a string (like 'mean', 'sum', etc.) or a list of functions.
- **fill_value**: value to replace missing values.
- **margins**: if true, adds all-row and all-column subtotals.
- **margins_name**: name for the all-row and all-column subtotals.
- **dropna**: if true, it excludes columns that contain only missing values.

example: creating a pivot table

let's consider an example using a sample dataset that represents sales data.

sample dataframe

dataframe preview

output:

creating a pivot table

now, we will create a pivot table to summarize the total sales and quantity by region for each date.

pivot table output

the output will look like this:

explanation

- **values**: we are aggregating both 'sales' and 'quantity'.
- **index**: we are grouping by 'date'.
- **columns**: we are splitting the data by 'region'.
- **aggfunc**: we are using the sum function to aggregate the values.
- **fill_value**: we replace missing values with 0.

additional features

adding margins

you can also add margins to get subtotals for each row and column.

this will add an additional ...

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