pivot in Pandas to summerize DataFrame data

Pivot are summarized data without any aggregate functions used.

pivot(index=None, columns=None, values=None)

index: str or object or a list of str, optional
columns: list or Column to use to make new frame’s columns.
values Column(s) to use for populating new frame’s values (optional ).
This is our sample DataFrame ( created by using sales.csv file after removing duplicate column/ rows )

import pandas as pd 
my_dict={'sale_id':[1,2,4,6,7],
         'c_id':[2,2,4,3,2],
         'p_id':[3,4,2,3,2],
	 'product':['Monitor','CPU','RAM','Monitor','RAM'],
         'qty':[2,1,2,2,3],
         'store':['ABC','DEF','DEF','DEF','ABC']}
sales = pd.DataFrame(data=my_dict)
print(sales)

	sale_id	c_id	p_id	product	qty	store
0	1	2	3	Monitor	2	ABC
1	2	2	4	CPU	1	DEF
2	4	4	2	RAM	2	DEF
3	6	3	3	Monitor	2	DEF
4	7	2	2	RAM	3	ABC

Using this DataFrame we will use the method pivot()

pvt=pd.pivot(sales,index=['product'],columns=['store'])

	sale_id		c_id		p_id		qty
store	ABC	DEF	ABC	DEF	ABC	DEF	ABC	DEF
product
CPU	NaN	2.0	NaN	2.0	NaN	4.0	NaN	1.0
Monitor	1.0	6.0	2.0	3.0	3.0	3.0	2.0	2.0
RAM	7.0	4.0	2.0	4.0	2.0	2.0	3.0	2.0

values

pvt=sales.pivot(index=['product'],columns=['store'],values='qty')

store	ABC	DEF
product
CPU	NaN	1.0
Monitor	2.0	2.0
RAM	3.0	2.0

Difference between pivot_table and pivot

pivot_table() : Index column pair need not be unique and it can be used with aggragrate function list sum , average etc
pivot() :If the index column pair has multiple values then it will generate value error. We can't use aggragrate functions.
Pandas pivot_table() Pandas DataFrame
contains() to display and delete row based on Conditions

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