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

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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

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