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.
#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.
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.
#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:
df.values.flatten() |
Output: