Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Let’s discuss all different ways of selecting multiple columns in a pandas DataFrame.
Method #1: Basic Method
Given a dictionary which contains Employee entity as keys and list of those entity as values.
# 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 two columns df[['Name', 'Qualification']] |
Output:
Select Second to fourth column.
# 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 all rows # and second to fourth column df[df.columns[1:4]] |
Output:
Method #2: Using loc[]
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 df.loc[1:3, ['Name', 'Qualification']] |
Output:
Example 2: Select one to another columns. In our case we select column name “Name” to “Address”.
# 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 two rows and # column "name" to "Address" # Means total three columns df.loc[0:1, 'Name':'Address'] |
Output:
Example 3: 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 df.loc[0, :] |
Output:
Method #3: Using iloc[]
Example 1: Select first two column.
# 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) # Remember that Python does not # slice inclusive of the ending index. # select all rows # select first two column df.iloc[:, 0:2] |
Output:
Example 2: 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] df.iloc [0:2, 1:3] |
Output:
Method #4: Using .ix
Select all or some columns, one to another using .ix.
# 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 all rows and 0 to 2 columns print(df.ix[:, 0:2]) |
Output:
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.
