In this tutorial, we will cover how to drop or remove one or multiple columns from pandas dataframe.
![drop columns python]()
What is pandas in Python?
pandas is a python package for data manipulation. It has several functions for the following data tasks: - Drop or Keep rows and columns
- Aggregate data by one or more columns
- Sort or reorder data
- Merge or append multiple dataframes
- String Functions to handle text data
- DateTime Functions to handle date or time format columns
Import or Load Pandas library
To make use of any python library, we first need to load them up by using import command. import pandas as pd
import numpy as np
Let's create a fake dataframe for illustration
The code below creates 4 columns named A through D. df = pd.DataFrame(np.random.randn(6, 4), columns=list('ABCD'))
A B C D
0 -1.236438 -1.656038 1.655995 -1.413243
1 0.507747 0.710933 -1.335381 0.832619
2 0.280036 -0.411327 0.098119 0.768447
3 0.858730 -0.093217 1.077528 0.196891
4 -0.905991 0.302687 0.125881 -0.665159
5 -2.012745 -0.692847 -1.463154 -0.707779
Drop a column in python
In pandas,drop( ) function is used to remove column(s).axis=1 tells Python that you want to apply function on columns instead of rows. df.drop(['A'], axis=1)Column A has been removed. See the output shown below.
B C DIn order to create a new dataframe
0 -1.656038 1.655995 -1.413243
1 0.710933 -1.335381 0.832619
2 -0.411327 0.098119 0.768447
3 -0.093217 1.077528 0.196891
4 0.302687 0.125881 -0.665159
5 -0.692847 -1.463154 -0.707779
newdf storing remaining columns, you can use the command below. newdf = df.drop(['A'], axis=1)To delete the column permanently from original dataframe
df, you can use the option inplace=Truedf.drop(['A'], axis=1, inplace=True)
#Check columns in df after dropping column A
df.columns
Output
Index(['B', 'C', 'D'], dtype='object')
The parameter
If you want to change the existing dataframe, try this
inplace= can be deprecated (removed) in future which means you might not see it working in the upcoming release of pandas package. You should avoid using this parameter if you are not already habitual of using it. Instead you can store your data after removing columns in a new dataframe (as explained in the above section). If you want to change the existing dataframe, try this
df = df.drop(['A'], axis=1) Remove Multiple Columns in Python
You can specify all the columns you want to remove in a list and pass it indrop( ) function. Method I
df2 = df.drop(['B','C'], axis=1)
Method II
cols = ['B','C']
df2 = df.drop(cols, axis=1)
Select or Keep Columns
If you wish to select a column (instead of drop), you can use the command df['A']To select multiple columns, you can submit the following code.
df[['A','B']]
How to drop column by position number from pandas Dataframe?
You can find out name of first column by using this commanddf.columns[0]. Indexing in python starts from 0. df.drop(df.columns[0], axis =1)To drop multiple columns by position (first and third columns), you can specify the position in list
[0,2]. cols = [0,2]
df.drop(df.columns[cols], axis =1)
Drop columns by name pattern
df = pd.DataFrame({"X1":range(1,6),"X_2":range(2,7),"YX":range(3,8),"Y_1":range(2,7),"Z":range(5,10)})X1 X_2 YX Y_1 Z
0 1 2 3 2 5
1 2 3 4 3 6
2 3 4 5 4 7
3 4 5 6 5 8
4 5 6 7 6 9
Drop column whose name starts with letter 'X'
df.loc[:,~df.columns.str.contains('^X')]
How it works?
^Xis a expression of regex language which refers to beginning of letter 'X'df.columns.str.contains('^X')returns array [True, True, False, False, False].
True where condition meets. Otherwise False- Sign
~refers to negate the condition. df.loc[ ]is used to select columns
df.drop(df.columns[df.columns.str.contains('^X')], axis=1)Other Examples
#Removing columns whose name contains string 'X'
df.loc[:,~df.columns.str.contains('X')]
#Removing columns whose name contains string either 'X' or 'Y'
df.loc[:,~df.columns.str.contains('X|Y')]
#Removing columns whose name ends with string 'X'
df.loc[:,~df.columns.str.contains('X$')]
Drop columns where percentage of missing values is greater than 50%
df = pd.DataFrame({'A':[1,3,np.nan,5,np.nan],
'B':[4,np.nan,np.nan,5,np.nan]
})
% of missing values can be calculated by mean of NAs in each column. cols = df.columns[df.isnull().mean()>0.5]
df.drop(cols, axis=1)