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
