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 package import pandas as pd
# Define a dictionary containing employee data data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],
'Age' :[ 27 , 24 , 22 , 32 ],
'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ],
'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]}
# Convert the dictionary into DataFrame df = pd.DataFrame(data)
# select three rows and two columns 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 package import pandas as pd
# Define a dictionary containing employee data data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],
'Age' :[ 27 , 24 , 22 , 32 ],
'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ],
'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]
}
# Convert the dictionary into DataFrame df = pd.DataFrame(data)
# .loc DataFrame method # filtering rows and selecting columns by label format # df.loc[rows, columns] # row 1, all columns 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 package import pandas as pd
# Define a dictionary containing employee data data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ],
'Age' :[ 27 , 24 , 22 , 32 ],
'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ],
'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]}
# Convert the dictionary into DataFrame df = pd.DataFrame(data)
# iloc[row slicing, column slicing] print (df.iloc [ 0 : 2 , 1 : 3 ] )
|
Output:
Age Name 0 27 Jai 1 24 Princi