Pandas reshaping data with pivot pivot_table melt unstack stack

This is a Summary and Visual Explanation based on this page

References

• https://pandas.pydata.org/docs/user_guide/reshaping.html

name	description	example
pivot	Return reshaped DataFrame organized by given index / column values.	df.pivot(index='foo', columns='bar', values='baz')
pivot_table	Create a spreadsheet-style pivot table as a DataFrame.	pd.pivot_table(df, values='D', index=[‘A', ‘B'], columns=[‘C'], aggfunc=np.sum)
melt	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.	df.melt(id_vars=[‘A'], value_vars=[‘B', ‘C'])
unstack	Pivot a level of the (necessarily hierarchical) index labels.	df.unstack()
stack	Stack the prescribed level(s) from columns to index.	df.stack()
explode	Transform each element of a list-like to a row, replicating index values.	df.explode(‘A')

█ pivot and pivot_table

• pivot does not do data aggregation such as min/max/mean
• pivot_table does data aggregation; when aggregation part is left empty in pivot_table, it is same as pivot

pivot

pivot and pivot_table: same results

pivot_table with aggregation

█ melt

█ stack and unstack

█ explode