Select Rows and Columns by Name or Index in Pandas DataFrame using loc and iloc

import pandas as pd

amazon_2 = pd.read_csv(r"C:\Users\User\Desktop\archive\amazon.csv",index_col=0)

amazon_2.head()

	Category	Product Link	No of Sellers	Rank	Rating	Reviews Count	Price
ASIN
B079QHML21	Electronics	https://www.amazon.com/gp/offer-listing/B079QH...	1 Sellers	#1	4.7	640,721	$39.99
B07FZ8S74R	Electronics	https://www.amazon.com/gp/offer-listing/B07FZ8...	1 Sellers	#2	4.7	854,114	$34.99
B07XJ8C8F5	Electronics	https://www.amazon.com/gp/offer-listing/B07XJ8...	1 Sellers	#3	4.7	267,821	$44.99
B07WVFCVJN	Electronics	https://www.amazon.com/gp/offer-listing/B07WVF...	27 Sellers	#4	4.8	114,267	$28.48
B08YT2N5SX	Electronics	https://www.amazon.com/gp/offer-listing/B08YT2...	1 Sellers	#5	4.7	267,821	$49.99

amazon_2.head()

	Category	Product Link	No of Sellers	Rank	Rating	Reviews Count	Price
ASIN
B079QHML21	Electronics	https://www.amazon.com/gp/offer-listing/B079QH...	1 Sellers	#1	4.7	640,721	$39.99
B07FZ8S74R	Electronics	https://www.amazon.com/gp/offer-listing/B07FZ8...	1 Sellers	#2	4.7	854,114	$34.99
B07XJ8C8F5	Electronics	https://www.amazon.com/gp/offer-listing/B07XJ8...	1 Sellers	#3	4.7	267,821	$44.99
B07WVFCVJN	Electronics	https://www.amazon.com/gp/offer-listing/B07WVF...	27 Sellers	#4	4.8	114,267	$28.48
B08YT2N5SX	Electronics	https://www.amazon.com/gp/offer-listing/B08YT2...	1 Sellers	#5	4.7	267,821	$49.99

Print out Specified column & All Rows as Pandas Series

productLink_series =amazon_2['Product Link']

productLink_series

ASIN
B079QHML21    https://www.amazon.com/gp/offer-listing/B079QH...
B07FZ8S74R    https://www.amazon.com/gp/offer-listing/B07FZ8...
B07XJ8C8F5    https://www.amazon.com/gp/offer-listing/B07XJ8...
B07WVFCVJN    https://www.amazon.com/gp/offer-listing/B07WVF...
B08YT2N5SX    https://www.amazon.com/gp/offer-listing/B08YT2...
                                    ...                        
B007DW6F34    https://www.amazon.com/gp/offer-listing/B007DW...
B01N16VX79    https://www.amazon.com/gp/offer-listing/B01N16...
B09197N995    https://www.amazon.com/gp/offer-listing/B09197...
B015CCR1FW    https://www.amazon.com/gp/offer-listing/B015CC...
B07TS96J7Q    https://www.amazon.com/gp/offer-listing/B07TS9...
Name: Product Link, Length: 707, dtype: object

Print Out Specifed Column & All Rows as Pandas Dataframe

productLink_DataFrame = amazon_2[['Product Link']]

productLink_DataFrame

	Product Link
ASIN
B079QHML21	https://www.amazon.com/gp/offer-listing/B079QH...
B07FZ8S74R	https://www.amazon.com/gp/offer-listing/B07FZ8...
B07XJ8C8F5	https://www.amazon.com/gp/offer-listing/B07XJ8...
B07WVFCVJN	https://www.amazon.com/gp/offer-listing/B07WVF...
B08YT2N5SX	https://www.amazon.com/gp/offer-listing/B08YT2...
...	...
B007DW6F34	https://www.amazon.com/gp/offer-listing/B007DW...
B01N16VX79	https://www.amazon.com/gp/offer-listing/B01N16...
B09197N995	https://www.amazon.com/gp/offer-listing/B09197...
B015CCR1FW	https://www.amazon.com/gp/offer-listing/B015CC...
B07TS96J7Q	https://www.amazon.com/gp/offer-listing/B07TS9...

707 rows × 1 columns

Specified Row ( First 3 Rows) & all Column Print Out

0: 3 means frpm 0 to 3 row

first_3_row = amazon_2[0:3] 

first_3_row 

	Category	Product Link	No of Sellers	Rank	Rating	Reviews Count	Price
ASIN
B079QHML21	Electronics	https://www.amazon.com/gp/offer-listing/B079QH...	1 Sellers	#1	4.7	640,721	$39.99
B07FZ8S74R	Electronics	https://www.amazon.com/gp/offer-listing/B07FZ8...	1 Sellers	#2	4.7	854,114	$34.99
B07XJ8C8F5	Electronics	https://www.amazon.com/gp/offer-listing/B07XJ8...	1 Sellers	#3	4.7	267,821	$44.99

Select First 3 Rows and Specified Column

select_3_row_and_1_column = amazon_2[0:3][['Rank']]

select_3_row_and_1_column

	Rank
ASIN
B079QHML21	#1
B07FZ8S74R	#2
B07XJ8C8F5	#3

Select First 2 Rows and 2 Specified Column

select_2_row_and_2_column = amazon_2[0:2][['Rank','Rating']]

select_2_row_and_2_column

	Rank	Rating
ASIN
B079QHML21	#1	4.7
B07FZ8S74R	#2	4.7

Select one Specified Row and one Specified Column Using loc

spRow_spCol_loc=amazon_2.loc[["B07FZ8S74R"],["Rank"]]
          
  

spRow_spCol_loc

	Rank
ASIN
B07FZ8S74R	#2

Select two Specified Row and two specified Column using loc

twoRow_twoCol_loc=amazon_2.loc[["B07FZ8S74R","B09197N995"],["Rank","Rating"]]

twoRow_twoCol_loc

	Rank	Rating
ASIN
B07FZ8S74R	#2	4.7
B09197N995	#97	4.8

Select one Specified Row and one Specified Column Using iloc

spRow_spCol_iloc=amazon_2.iloc[[1],[4]]

spRow_spCol_iloc

	Rating
ASIN
B07FZ8S74R	4.7

Select two Specified Row and two specified Column using iloc

twoRow_twoCol_iloc=amazon_2.iloc[[1,706],[4,5]]

twoRow_twoCol_iloc

	Rating	Reviews Count
ASIN
B07FZ8S74R	4.7	854,114
B07TS96J7Q	4.7	12,902

Build a DataFrame From Scratch ( Way 1)

movie ={  "Avenger":{'Type':"Scifi", 'Rating':"Public"}, 
          "Fast and Furious":{'Type':"Action",'Rating':"16 Above"},
          "Carrie":{'Type':"Horror",'Rating':"16 Above"},
          "Jumanji":{'Type':"Comedy",'Rating':"Public"},  
          "Ice Age":{'Type':"Cartoon",'Rating':"Public"},  
          "Resident Evil":{'Type':"Gore",'Rating':"16 Above"},  
          "Basic Instinct":{'Type':"Adult",'Rating':"18 Above"}
       
       }
  

movie_frame =pd.DataFrame(movie)

movie_frame

	Avenger	Fast and Furious	Carrie	Jumanji	Ice Age	Resident Evil	Basic Instinct
Type	Scifi	Action	Horror	Comedy	Cartoon	Gore	Adult
Rating	Public	16 Above	16 Above	Public	Public	16 Above	18 Above

Select 1 Specified Row and 2 Specified Column with loc

mf_1row_2col_loc =movie_frame.loc[["Type"],["Carrie","Jumanji"]]

mf_1row_2col_loc

	Carrie	Jumanji
Type	Horror	Comedy

Build a DataFrame From Scratch ( Way 2) Using Tupple and List

movie2= [   ('Avenger', 'Scifi', 'Public'),
            ('Fast and Furious', 'Action', '16 Above'),
            ('Carrie', 'Horror', '16 Above'),
            ('Jumanji', 'Comedy', 'Public'),
            ('Ice Age', 'Cartoon', 'Public'),
            ('Resident Evil', 'Gore', '16 Above'),
            ('Basic Instinct', 'Adult', '18 Above')
            
           
            ]

Since pandas DataFrames and Series always have an index, you can’t actually drop the index, but you can reset it by using the following bit of code:

movie_frame_2 = pd.DataFrame(movie2,  columns =['Name', 'Type', 'Rating'])
              

movie_frame_2.reset_index(drop=True, inplace=True)

movie_frame_2

	Name	Type	Rating
0	Avenger	Scifi	Public
1	Fast and Furious	Action	16 Above
2	Carrie	Horror	16 Above
3	Jumanji	Comedy	Public
4	Ice Age	Cartoon	Public
5	Resident Evil	Gore	16 Above
6	Basic Instinct	Adult	18 Above

Or you can Assign Value to Index

movie_frame_test = pd.DataFrame(movie2,  columns =['Name', 'Type', 'Rating'],index=['Movie_1','Movie_2','Movie_3','Movie_4'
                                                                                    ,'Movie_5','Movie_6','Movie_7'])

movie_frame_test

	Name	Type	Rating
Movie_1	Avenger	Scifi	Public
Movie_2	Fast and Furious	Action	16 Above
Movie_3	Carrie	Horror	16 Above
Movie_4	Jumanji	Comedy	Public
Movie_5	Ice Age	Cartoon	Public
Movie_6	Resident Evil	Gore	16 Above
Movie_7	Basic Instinct	Adult	18 Above

Hide the Index

movie_frame_2.style.hide_index()

Name	Type	Rating
Avenger	Scifi	Public
Fast and Furious	Action	16 Above
Carrie	Horror	16 Above
Jumanji	Comedy	Public
Ice Age	Cartoon	Public
Resident Evil	Gore	16 Above
Basic Instinct	Adult	18 Above

movie_frame_2

	Name	Type	Rating
0	Avenger	Scifi	Public
1	Fast and Furious	Action	16 Above
2	Carrie	Horror	16 Above
3	Jumanji	Comedy	Public
4	Ice Age	Cartoon	Public
5	Resident Evil	Gore	16 Above
6	Basic Instinct	Adult	18 Above

Select 1 Specified Row and 2 Specified Column with iloc¶

mf_1row_2col_iloc=movie_frame_2.iloc[[1],[1,2]]

mf_1row_2col_iloc

	Type	Rating
1	Action	16 Above

Build a DataFrame From Scratch ( Way 3)¶

name = ['Avenger','Fast and Furious','Carrie','Jumanji','Ice Age','Resident Evil','Basic Instinct']
types =['scifi','Action','Horor','Comedy','Cartoon','Gore','Adult']
rating=['Public','16 Above','16 Above','Public','Public','16 Above','18 Above']

movie3 ={'Name':name,'Type':types,'Rating':rating}

movie_frame_3 =pd.DataFrame(movie3)

movie_frame_3

	Name	Type	Rating
0	Avenger	scifi	Public
1	Fast and Furious	Action	16 Above
2	Carrie	Horor	16 Above
3	Jumanji	Comedy	Public
4	Ice Age	Cartoon	Public
5	Resident Evil	Gore	16 Above
6	Basic Instinct	Adult	18 Above