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:
