This content originally appeared on CodeSource.io and was authored by Md Niaz Rahman Khan
In this article, you will learn how to stack Pandas DataFrame.
A Pandas DataFrame is nothing but a two-dimensional data structure or two-dimensional array that represents the data in rows and columns. In other words, it is compared to rectangular grids used to store data. It is open-source and potent, fast, and easy to use. Basically, while working with big data we need to analyze, manipulate and update them and the pandas’ library plays a lead role there.
Sometimes, We need to reshape the Pandas DataFrame into a table. The easiest way to perform this action is to use the stack() method. We can stack a Pandas DataFrame in a single level or multi-level. This method accepts two parameters first one is level which is required and the second one is dropna with a value of True and False. This parameter is optional. In this article, we will explore this method and see how we can stack Pandas DataFrame. First, let’s create a simple Pandas DataFrame in the below section:
import pandas as pd
student_df = pd.DataFrame([['Alex', 19], ['Deven', 21]],
['Alex_Details', 'Deven_details'],
columns=['Name', 'Age'])
print(student_df)
# Output:
# Name Age
# Alex_Details Alex 19
# Deven_details Deven 21Here, we have created a simple Pandas DataFrame that represents two students’ details. This DataFrame consists of single-level columns with the details of Name and Marks. We will reshape this DataFrame by using the stack() method in the next section.
Example One: stack Pandas DataFrame in single-level
We can stack Pandas DataFrame in single level column. This is the easiest way to perform this action. All we need to do is to mention the DataFrame name and then access the stack() method. Follow the below code example:
import pandas as pd
student_df = pd.DataFrame([['Alex', 19], ['Deven', 21]],
['Alex_Details', 'Deven_details'],
columns=['Name', 'Age'])
single_stack_df = student_df.stack()
print(single_stack_df)
# Output:
# Alex_Details Name Alex
# Age 19
# Deven_details Name Deven
# Age 21
# dtype: objectHere, you can see that we use the stack method to reshape the DataFrame. We store the result into a new variable. Finally, you can see the changes in the output.
Example Two: stack Pandas DataFrame in multi-level
In the previous example, we have seen how we can reshape the DataFrame that consists of single level column. What if we need to reshape a multi-level columns DataFrame? How can we perform this action? To do so, follow the below code example:
import pandas as pd
multi_column = pd.MultiIndex.from_tuples([('student_details', 'Name'),
('student_details', 'Age')])
student_df = pd.DataFrame([['Alex', 19], ['Deven', 21]],
['Alex_Details', 'Deven_details'],
columns=multi_column)
multi_stack_df = student_df.stack()
print(multi_stack_df)
# Output:
# student_details
# Alex_Details Age 19
# Name Alex
# Deven_details Age 21
# Name DevenHere, you can see that our DataFrame has a common column name and each column consists of two more columns. We simply, reshape this DataFrame by using the stack() method. If you need to perform this action in your program, you can simply follow these approaches.
This content originally appeared on CodeSource.io and was authored by Md Niaz Rahman Khan
Md Niaz Rahman Khan | Sciencx (2022-10-01T16:27:00+00:00) How to stack Pandas DataFrame. Retrieved from https://www.scien.cx/2022/10/01/how-to-stack-pandas-dataframe/
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