How to Drop Multiple Columns by Index in pandas

In this pandas drop multiple columns by index article, I will explain how to drop multiple columns by index with several DataFrame examples. You can drop columns by index by using DataFrame.drop() method and by using DataFrame.iloc[].columns property to get the column names by index.

1. Quick Examples of Pands Drop Multiple Columns by Index

If you are in hurry, below are some quick examples of how to drop multiple columns by an index of DataFrame.


# Below are some quick examples.
                    
# 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)

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. Pandas Drop Multiple Columns By Index

In this section, you’ll learn how to drop multiple columns by index in pandas. 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.

Note that an index is 0 based. Use 0 to delete the first column and 1 to delete the second column and so on.


# 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

3. Drop Columns by Index Using iloc[] and drop() Methods

If you wanted to drop columns from starting and ending index ranges, you can do so by using DataFrame.iloc[] property. This property returns all column names between specified indexes and pass 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

4. Drop Columns by 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

5. Complete Examples Drop Multiple 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)

# 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 multiple columns by index using, DataFrame.drop() Method and DataFrame.iloc[] and DataFrame.loc[] properties with some examples.

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