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Pandas is an advanced data analysis tool or package extension in Python. It is highly recommended to use Pandas when we have data in SQL tables, spreadsheets, or heterogeneous columns.

This article discusses the basic concepts of stacking and unstacking in Pandas. In Pandas, stacking and unstacking are widely used to change the shape of the DataFrame under consideration.

Let's see this method in action. First, we'll create a dummy DataFrame dates_datawith a few rows.

import pandas as pd

index = pd.date_range("2013-1-1", periods=100, freq="30Min")
dates_data = pd.DataFrame(data=list(range(100)), columns=["value"], index=index)
dates_data["value2"] = "Alpha"
dates_data["value2"].loc[0:10] = "Beta"

The above code block creates a DataFrame dates_datacontaining the date and two columns named valueand value2. To view the entries in the data, we use the following code:

print(dates_data)

Output:

                     value value2
2013-01-01 00:00:00      0   Beta
2013-01-01 00:30:00      1   Beta
2013-01-01 01:00:00      2   Beta
2013-01-01 01:30:00      3   Beta
2013-01-01 02:00:00      4   Beta
...                    ...    ...
2013-01-02 23:30:00     95  Alpha
2013-01-03 00:00:00     96  Alpha
2013-01-03 00:30:00     97  Alpha
2013-01-03 01:00:00     98  Alpha
2013-01-03 01:30:00     99  Alpha

As we can see, we have 100 different entries, each with the same time set after an interval of 30 minutes.

valueAdditionally, two additional columns named and are created value2where we set some values ​​to numbers and other values ​​to Alphaor Beta.

stack()Sum unstack()Function in Pandas

We can change the DataFrame named with the help of two functions in Pandas named stack()and . This function helps us to change the direction of the DataFrame so that the rows become columns and the columns become rows accordingly.unstack()dates_data

valueWe will try to convert the and in the DataFrame value2to rows and the values ​​in it to entries in the row.

Use unstack()the function to transform our DataFrame

Order:

dates_data = dates_data.unstack()
print(dates_data)

Output:

value   2013-01-01 00:00:00        0
        2013-01-01 00:30:00        1
        2013-01-01 01:00:00        2
        2013-01-01 01:30:00        3
        2013-01-01 02:00:00        4
                               ...
value2  2013-01-02 23:30:00    Alpha
        2013-01-03 00:00:00    Alpha
        2013-01-03 00:30:00    Alpha
        2013-01-03 01:00:00    Alpha
        2013-01-03 01:30:00    Alpha
Length: 200, dtype: object

Now that we have successfully transformed our data, we now have the columns as row entries in our data.

Use unstack()the function to transform our DataFrame

Order:

dates_data = dates_data.stack()
print(dates_data)

Output:

2013-01-01 00:00:00  value         0
                     value2     Beta
2013-01-01 00:30:00  value         1
                     value2     Beta
2013-01-01 01:00:00  value         2
                               ...
2013-01-03 00:30:00  value2    Alpha
2013-01-03 01:00:00  value        98
                     value2    Alpha
2013-01-03 01:30:00  value        99
                     value2    Alpha
Length: 200, dtype: object

The column values ​​are now stacked as rows in our DataFrame.

So, with the help of in Pandas unstacking 技术, we can effectively filter the data as and when required and transform the appearance of the DataFrame to visualize the data in a better way.