Limited rows selection with given column in Pandas | Python

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