In this pandas drop columns by index article, I will explain how to drop columns by index with several DataFrame examples. You can drop column by index in pandas by using DataFrame.drop() method and by using DataFrame.iloc[].columns property to get the column names by index.
drop()method is used to remove columns or rows from DataFrame.- Use
axisparam to specify what axis you would like to remove. By default axis = 0 meaning to remove rows. Use axis=1 or columns param to remove columns. - Use
inplace=Trueto remove row/column in place meaning on existing DataFrame with out creating copy.
1. Quick Examples of Drop Columns by Index in Pandas DataFrame
If you are in hurry, below are some quick examples to drop column(s) by an index of pandas DataFrame.
# Below are some quick examples.
# Using DataFrame.drop() method.
df2=df.drop(df.columns[1], axis=1)
# Drop columns based on column index.
df2 = df.drop(df.columns[[0, 1, 2]],axis = 1)
# Drop column of index using DataFrame.iloc[] and drop() methods.
df2 = df.drop(df.iloc[:, 1:3],axis = 1)
# Drop columns by labels using DataFrame.loc[] and drop() methods.
df2 = df.drop(df.loc[:, 'Courses':'Fee'].columns,axis = 1)
Now, let’s create a Pandas DataFrame with a few rows and columns and execute some examples and validate results. Our DataFrame contains column names Courses, Fee and Discount.
# Create a Pandas DataFrame.
import pandas as pd
import numpy as np
technologies= {
'Courses':["Spark","Spark","PySpark","JAVA","Hadoop",".Net","Python","AEM","Oracle","SQL DBA","C","WebTechnologies"],
'Fee' :[22000,25000,23000,24000,26000,30000,27000,28000,35000,32000,20000,15000],
'Duration':['30days','35days','40days','45days','50days','55days','60days','35days','30days','40days','50days','55days']
}
df = pd.DataFrame(technologies)
print(df)
Yields below output.
Courses Fee Duration
0 Spark 22000 30days
1 Spark 25000 35days
2 PySpark 23000 40days
3 JAVA 24000 45days
4 Hadoop 26000 50days
5 .Net 30000 55days
6 Python 27000 60days
7 AEM 28000 35days
8 Oracle 35000 30days
9 SQL DBA 32000 40days
10 C 20000 50days
11 WebTechnologies 15000 55days
2. Using DataFrame.drop() Column by Index
you can use DataFrame.drop() function to remove the column by index. The drop()function is used to drop specified labels from rows or columns. Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index of columns. When using a multi-index, labels on different levels can be removed by specifying the level.
Note that an index is 0 based. Use 0 to delete the first column and 1 to delete the second column and so on.
# Using DataFrame.drop() method.
df2=df.drop(df.columns[1], axis=1)
print(df2)
Yields below output. This deletes the second column as the index starts from 0.
Courses Duration
0 Spark 30days
1 Spark 35days
2 PySpark 40days
3 JAVA 45days
4 Hadoop 50days
5 .Net 55days
6 Python 60days
7 AEM 35days
8 Oracle 30days
9 SQL DBA 40days
10 C 50days
11 WebTechnologies 55days
3. Drop Multiple Columns By Index
In this section, you’ll learn how to drop multiple columns by index. You can use df.columns[[index1, index2, indexn]] to identify the list of column names in that index position and pass that list to the drop method.
# Drop columns based on column index.
df2 = df.drop(df.columns[[1, 2]],axis = 1)
print(df2)
Yields below output.
Courses
0 Spark
1 Spark
2 PySpark
3 JAVA
4 Hadoop
5 .Net
6 Python
7 AEM
8 Oracle
9 SQL DBA
10 C
11 WebTechnologies
4. Drop Columns by Index Using DataFrame.iloc[] and drop() Methods
If you wanted to drop columns from starting and ending index ranges, you can do so by using iloc[] property. This property returns all column names between specified indexes and ass these column names to drop() method.
# Drop column of index using DataFrame.iloc[] and drop() methods.
df2 = df.drop(df.iloc[:, 1:3],axis = 1)
print(df2)
Yields below output.
Courses
0 Spark
1 Spark
2 PySpark
3 JAVA
4 Hadoop
5 .Net
6 Python
7 AEM
8 Oracle
9 SQL DBA
10 C
11 WebTechnologies
5. Drop Columns of Index Using DataFrame.loc[] and drop() Methods
Similarly, you can drop columns by the range of labels using DataFrame.loc[] and DataFrame.drop() methods. Here the loc[] property is used to access a group of rows and columns by label(s) or a boolean array.
# Drop columns of index using DataFrame.loc[] and drop() methods.
df2 = df.drop(df.loc[:, 'Courses':'Fee'].columns,axis = 1)
print(df2)
Yields below output.
Duration
0 30days
1 35days
2 40days
3 45days
4 50days
5 55days
6 60days
7 35days
8 30days
9 40days
10 50days
11 55days
6. Complete Examples of Drop Columns By Index
# Create a Pandas DataFrame.
import pandas as pd
import numpy as np
technologies= {
'Courses':["Spark","Spark","PySpark","JAVA","Hadoop",".Net","Python","AEM","Oracle","SQL DBA","C","WebTechnologies"],
'Fee' :[22000,25000,23000,24000,26000,30000,27000,28000,35000,32000,20000,15000],
'Duration':['30days','35days','40days','45days','50days','55days','60days','35days','30days','40days','50days','55days']
}
df = pd.DataFrame(technologies)
print(df)
# Using DataFrame.drop() method.
df2=df.drop(df.columns[1], axis=1)
print(df2)
# Drop Multiple Columns by labels.
df2 = df.drop(['Courses', 'Duration'],axis = 1)
print(df2)
# Drop columns based on column index.
df2 = df.drop(df.columns[[0, 1, 2]],axis = 1)
print(df2)
# Drop column by index using DataFrame.iloc[] and drop() methods.
df2 = df.drop(df.iloc[:, 1:3],axis = 1)
print(df2)
# Drop columns by labels using DataFrame.loc[] and drop() methods.
df2 = df.drop(df.loc[:, 'Courses':'Fee'].columns,axis = 1)
print(df2)
Conclusion
In this article, You have learned how to drop columns by index using DataFrame.drop() Method and DataFrame.iloc[] and DataFrame.loc[] properties with some examples.
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