In Python, you can use the pandas library to work with tabular data, and the core data type in pandas is the DataFrame. Sometimes, when working with DataFrame data, you may need to convert rows to columns or columns to rows. Here is a simple example demonstrating how to achieve this using the pandas library.
Create Table
At first, lets’ create a new example DataFrame.
import pandas as pd # create a new DataFrame df = pd.DataFrame({ 'name': ['John', 'Mary', 'Peter'], 'math': [80, 90, 70], 'english': [70, 85, 90], 'science': [75, 95, 80] }) print(df) # output: name math english science 0 John 80 70 75 1 Mary 90 85 95 2 Peter 70 90 80 Convert Rows to Columns
# Use the melt function to convert rows to columns df_melt = pd.melt(df, id_vars=['name'], var_name='subject', value_name='score') print(df_melt) In the code above, we simply use the melt function to transform each student's subject grades into a column, which gives us the following output.
name subject score 0 John math 80 1 Mary math 90 2 Peter math 70 3 John english 70 4 Mary english 85 5 Peter english 90 6 John science 75 7 Mary science 95 8 Peter science 80 Convert Columns to Rows
Column to row transformation, also known as data unpivoting, can be achieved using the pivot function in the pandas library. Here is an example code:
# Use the pivot function to pivot rows to columns. df_pivot = df_meld.pivot(index='name', columns='subject', values='score') print(df_pivot) subject english math science name John 70 80 75 Mary 85 90 95 Peter 90 70 80 Explore more
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