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.
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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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