DataFrame Data structure are the heart of Pandas library. DataFrames are basically two dimension Series object. They have rows and columns with rows representing the index and columns representing the content. Now, let’s see how to Select all columns, except one given column in Pandas DataFrame in Python.
Creating a DataFrame:
Python3
# import pandas libraryimport pandas as pd# create a Dataframedata = pd.DataFrame({ 'course_name': ['Data Structures', 'Python', 'Machine Learning'], 'student_name': ['A', 'B', 'C'], 'student_city': ['Chennai', 'Pune', 'Delhi'], 'student_gender': ['M', 'F', 'M'] })# show the Dataframedata |
Output:
DataFrame
Using loc[] to select all columns, except one given column
This GeeksForGeeks Dataframe is just a two dimension array with numerical index. Therefore, to except only one column we could use the columns methods to get all columns and use a not operator to exclude the columns which are not needed. This method works only when the Dataframe is not multi indexed (did not have more than one index).
Example 1: Using Dataframe.loc[ ] with ‘!=’
Python3
df = data.loc[ : , data.columns != 'student_gender']# show the dataframedf |
Output:
filtered student_gender column
Example 2: Using Dataframe.loc[ ] with isin
Python3
df= data.loc[:, ~data.columns.isin(['student_gender'])]# showing the dataframedf |
Output:
filtered student_gender column
Using drop() Method to select all columns, except one given column
Dataframe supports drop() method to drop a particular column. It accepts two arguments, column/row name and axis
Example: Select all columns, except one ‘student_city’ column in Pandas Dataframe.
Python3
# drop methoddf = data.drop('student_city', axis = 1) # show the dataframedf |
Output:
student_city column removed
Using Series.difference() to select all columns, except one given column
Using Series.difference() method and [ ] operator together. Series.difference() Method returns a new Index with elements from the index that are not in other.
Example: Select all columns, except one ‘student_name’ column in Pandas Dataframe.
Python3
df = data[data.columns.difference(['student_name'])]# show the dataframedf |
Output:
filtered student_name column
