Flatten a list of DataFrames

In this article, we are going to see how to flatten a list of DataFrames. Flattening is defined as Converting or Changing data format to a narrow format. The advantage of the flattened list is Increases the computing speed and Good understanding of data.

Example:

Let consider, the data frame that contains values like payments in four months. Actually, the data is stored in a list format.

Note: 0,1,2 are the indices of the records  

Flattening means assigning lists separately for each author.

We are going to perform flatten operations on the list using data frames.

Method 1:

Step 1: Create a simple data frame.

Python3

#importing pandas module
import pandas as pd

#creating dataframe with 2 columns
df = pd.DataFrame(data=[[[ 300, 400, 500, 600], 'sravan_payment'],
                        [[ 300, 322, 333, 233], 'bobby_payment']],
                  index=[ 0, 1], columns=[ 'A', 'B'])

display(df)

Output:

Step 2: iterate each row with a specific column.

Python3

flatdata = pd.DataFrame([( index, value) for ( index, values)
                         in df[ 'A' ].iteritems() for value in values],
                             columns = [ 'index', 'A']).set_index( 'index' )

df = df.drop( 'A', axis = 1 ).join( flatdata )
display(df)

Output:

Methods 2: Using the flatten methods.

We are going to apply the flatten function for the above code.

Python3

#importing pandas module for dataframe.
import pandas as pd

df = pd.DataFrame(data=[[[ 300, 400, 500, 600], 'sravan_payment'],
                        [[ 300, 322, 333, 233], 'bobby_payment']],
                  index = [ 0, 1], columns = [ 'A', 'B'])
display(df)

Output:

Python3

df.values.flatten()

Output: