Limited rows selection with given column in Pandas | Python (original) (raw)

Last Updated : 24 Oct, 2019

Methods in Pandas like iloc[], iat[] are generally used to select the data from a given dataframe. In this article, we will learn how to select the limited rows with given columns with the help of these methods.

Example 1: Select two columns

import pandas as pd

data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],

`` 'Age' :[ 27 , 24 , 22 , 32 ],

`` 'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ],

`` 'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]}

df = pd.DataFrame(data)

print (df.loc[ 1 : 3 , [ 'Name' , 'Qualification' ]])

Output:

 Name Qualification

1 Princi MA 2 Gaurav MCA 3 Anuj Phd

Example 2: First filtering rows and selecting columns by label format and then Select all columns.

import pandas as pd

data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],

`` 'Age' :[ 27 , 24 , 22 , 32 ],

`` 'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ],

`` 'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]

`` }

df = pd.DataFrame(data)

print (df.loc[ 0 , :] )

Output:

Address Delhi Age 27 Name Jai Qualification Msc Name: 0, dtype: object

Example 3: Select all or some columns, one to another using .iloc.

import pandas as pd

data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],

`` 'Age' :[ 27 , 24 , 22 , 32 ],

`` 'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ],

`` 'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]}

df = pd.DataFrame(data)

print (df.iloc [ 0 : 2 , 1 : 3 ] )

Output:

Age Name 0 27 Jai 1 24 Princi

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