Select Rows & Columns by Name or Index in DataFrame using loc & iloc | Python Pandas

How to select rows and columns by Name or Index in DataFrame using loc and iloc in Python ?

We will discuss several methods to select rows and columns in a dataframe. To select rows or columns we can use loc( ), iloc( ) or using the [ ] operator.

To demonstrate the various methods we will be using the following dataset :

      Name      Score        City
0     Jill           16.0       Tokyo
1     Rachel     38.0      Texas
2     Kirti         39.0       New York
3     Veena      40.0      Texas
4     Lucifer     NaN      Texas
5     Pablo       30.0       New York
6     Lionel      45.0       Colombia

Method-1 : DataFrame.loc | Select Column & Rows by Name

We can use the loc( ) function to select rows and columns.

Syntax :

dataFrame.loc[<ROWS RANGE> , <COLUMNS RANGE>]

We have to enter the range of rows or columns, and it will select the specified range.

If we don’t give a value and pass ‘:’ instead, it will select all the rows or columns.

Select a Column by Name in DataFrame using loc[ ] :

As we need to select a single column only, we have to pass ‘:’ in row range place.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'])
#Selecting the 'Score solumn'
columnD = dfObj.loc[:,'Score']
print(columnD)
Output :
0    16.0
1    38.0
2    39.0
3    40.0
4     NaN
5    30.0
6    45.0
Name: Score, dtype: float64

Select multiple Columns by Name in DataFrame using loc[ ] :

To select multiple columns, we have to pass the column names as a list into the function.

So, let’s see the implementation of it.

#Program

import pandas as pd
import numpy as np
#data
students = [('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple columns i.e 'Name' and 'Score' column
columnD = dfObj.loc[:,['Name','Score']]
print(columnD)
Output :
     Name      Score
a     Jill          16.0
b   Rachel     38.0
c    Kirti         39.0
d    Veena     40.0
e  Lucifer      NaN
f    Pablo       30.0
g   Lionel       45.0

Select a single row by Index Label in DataFrame using loc[ ] :

Just like the column, we can also select a single row by passing its name and in place of column range passing ‘:’.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting a single row i.e 'b' row
selectData = dfObj.loc['b',:]
print(selectData)

Output :
Name       Rachel
Score        38.0
City          Texas
Name: b, dtype: object

Select multiple rows by Index labels in DataFrame using loc[ ] :

To select multiple rows we have to pass the names as a list into the function.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple rows i.e 'd' and 'g'
selectData = dfObj.loc[['d','g'],:]
print(selectData)

Output :
      Name   Score      City
d    Veena   40.0       Texas
g    Lionel   45.0       Colombia

Select multiple row & columns by Labels in DataFrame using loc[ ] :

To select multiple rows and columns we have to pass the list of rows and columns we want to select into the function.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple rows and columns i.e 'd' and 'g' rows and 'Name' , 'City' column
selectData = dfObj.loc[['d','g'],['Name','City']]
print(selectData)

Output :
      Name      City
d   Veena      Texas
g  Lionel       Colombia

Method-2 : DataFrame.iloc | Select Column Indexes & Rows Index Positions

We can use the iloc( ) function to select rows and columns. It is quite similar to loc( ) function .

Syntax-

dataFrame.iloc

[<ROWS INDEX RANGE> , <COLUMNS INDEX RANGE>]

The function selects rows and columns in the dataframe by the index position we pass into the program. And just as like loc( ) if ‘:’ is passed into the function, all the rows/columns are selected.

Select a single column by Index position :

We have to pass the index of the column with ‘:’ in place of the row index.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting a single column at the index 2
selectData = dfObj.iloc[:,2]
print(selectData)
Output :
a       Tokyo
b       Texas
c        New York
d       Texas
e       Texas
f        New York
g       Colombia
Name: City, dtype: object

Select multiple columns by Indices in a list :

To select multiple columns by indices we just pass the indices as series into the column value.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple columns at the index 0 & 2
selectData = dfObj.iloc[:,[0,2]]
print(selectData)
Output :
        Name       City
a      Jill            Tokyo
b      Rachel     Texas
c      Kirti          New York
d      Veena      Texas
e      Lucifer     Texas
f      Pablo        New York
g     Lionel       Colombia

Select multiple columns by Index range :

To select multiple columns by index range we just pass the indices as series into the column value.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple columns from the index 1 to 3
selectData = dfObj.iloc[:,1:3]
print(selectData)
Output :
      Score      City
a    16.0         Tokyo
b    38.0         Texas
c    39.0         New York
d    40.0         Texas
e    NaN        Texas
f     30.0         New York
g    45.0         Colombia

Select single row by Index Position :

Just like columns we can pass the index and select the row.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting a single row with index 2
selectData = dfObj.iloc[2,:]
print(selectData)
Output :
Name        Kirti
Score        39.0
City     New York
Name: c, dtype: object

Select multiple rows by Index positions in a list :

To do this we can pass the indices of positions to select into the function.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple rows by passing alist i.e. 2 & 5
selectData = dfObj.iloc[[2,5],:]
print(selectData)
 Output :
     Name    Score      City
c   Kirti      39.0       New York
f    Pablo   30.0       New York

Select multiple rows by Index range :

To select a range of rows we pass the range separated by a ‘:’ into the function.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple rows by range i.e. 2 to 5
selectData = dfObj.iloc[2:5,:]
print(selectData)
Output :
      Name      Score      City
c     Kirti        39.0        New York
d     Veena     40.0       Texas
e     Lucifer    NaN       Texas

Select multiple rows & columns by Index positions :

To select multiple rows and columns at once, we pass the indices directly into function.

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Selecting multiple rows and columns
selectData = dfObj.iloc[[1,2],[1,2]]
print(selectData)
Output :
    Score      City
b   38.0      Texas
c   39.0       New York

Method-3 : Selecting Columns in DataFrame using [ ] operator

The [ ] operator selects the data according to the name provided to it. However, when a non-existent label is passed into it, it sends a KeyError.

Select a Column by Name :

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Select a single column name using [ ]
selectData = dfObj['Name']
print(selectData)
Output :
a       Jill
b     Rachel
c      Kirti
d      Veena
e    Lucifer
f      Pablo
g     Lionel
Name: Name, dtype: object

Select multiple columns by Name :

To select multiple columns we just pass a list of their names into [ ].

So, let’s see the implementation of it.

#Program :

import pandas as pd
import numpy as np
#data
students = [
('Jill',    16,     'Tokyo',),
('Rachel',  38,     'Texas',),
('Kirti',   39,     'New York'),
('Veena',   40,     'Texas',),
('Lucifer', np.NaN, 'Texas'),
('Pablo',   30,     'New York'),
('Lionel',  45,     'Colombia',)]
#Creating the dataframe object
dfObj = pd.DataFrame(students, columns=['Name','Score','City'], index=['a','b','c','d','e','f','g'])
#Select multiple columns using [ ]
selectData = dfObj[['Name','City']]
print(selectData)
Output :
       Name        City
a      Jill            Tokyo
b      Rachel     Texas
c      Kirti          New York
d      Veena       Texas
e      Lucifer      Texas
f       Pablo        New York
g      Lionel       Colombia

Want to expert in the python programming language? Exploring Python Data Analysis using Pandas tutorial changes your knowledge from basic to advance level in python concepts.

Read more Articles on Python Data Analysis Using Padas – Select items from a Dataframe