Removing columns from a pandas DataFrame that contain certain names can be accomplished using the drop() function. The drop() function takes two arguments: the labels of the columns to be removed, and the axis (0 for rows, 1 for columns).

Here is an example of how to use the drop() function to remove columns with specific names from a DataFrame:

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Mike', 'Sara'],
        'Age': [25, 30, 35],
        'Address': ['New York', 'Chicago', 'Los Angeles'],
        'Phone': [123, 456, 789]}
df = pd.DataFrame(data)

# Print the original DataFrame
print(df)

# Remove the 'Address' and 'Phone' columns
df = df.drop(columns=['Address', 'Phone'])

# Print the modified DataFrame
print(df)

The output of the first print(df) statement will be:

   Name  Age    Address  Phone
0  John   25     New York    123
1  Mike   30    Chicago    456
2  Sara   35  Los Angeles    789

And the output of the second print(df) statement will be:

   Name  Age
0  John   25
1  Mike   30
2  Sara   35

As you can see, the 'Address' and 'Phone' columns have been removed from the DataFrame.

Another way to remove a column is by using the del keyword on the DataFrame.

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Mike', 'Sara'],
        'Age': [25, 30, 35],
        'Address': ['New York', 'Chicago', 'Los Angeles'],
        'Phone': [123, 456, 789]}
df = pd.DataFrame(data)

# Print the original DataFrame
print(df)

# Remove the 'Address' column
del df['Address']

# Print the modified DataFrame
print(df)

This method also will remove the 'Address' column from the DataFrame

You can also remove multiple columns by passing a list of column names to the del keyword.

del df[['Address','Phone']]

It's also possible to remove columns based on certain conditions. For example, you can remove all columns that contain a specific string in their name.

# Create a sample DataFrame
data = {'Name': ['John', 'Mike', 'Sara'],
        'Age': [25, 30, 35],
        'Address_1': ['New York', 'Chicago', 'Los Angeles'],
        'Phone_1': [123, 456, 789],
        'Address_2': ['New York', 'Chicago', 'Los Angeles'],
        'Phone_2': [123, 456, 789]}
df = pd.DataFrame(data)

# Print the original DataFrame
print(df)

# Remove all columns that contain the string '_1'
df = df
Sure, in addition to removing columns based on specific names, you can also remove columns based on certain conditions. For example, you can remove all columns that contain a specific string in their name.

import pandas as pd

Create a sample DataFrame

data = {'Name': ['John', 'Mike', 'Sara'],
'Age': [25, 30, 35],
'Address_1': ['New York', 'Chicago', 'Los Angeles'],
'Phone_1': [123, 456, 789],
'Address_2': ['New York', 'Chicago', 'Los Angeles'],
'Phone_2': [123, 456, 789]}
df = pd.DataFrame(data)

Print the original DataFrame

print(df)

Remove all columns that contain the string '_1'

df = df.drop(columns=[col for col in df.columns if '_1' in col])

Print the modified DataFrame

print(df)

The output of the first `print(df)` statement will be:

Name  Age  Address_1  Phone_1  Address_2  Phone_2

0 John 25 New York 123 New York 123
1 Mike 30 Chicago 456 Chicago 456
2 Sara 35 Los Angeles 789 Los Angeles 789

And the output of the second `print(df)` statement will be:

Name Age Address_2 Phone_2
0 John 25 New York 123
1 Mike 30 Chicago 456
2 Sara 35 Los Angeles 789

You can also remove columns based on their data type. For example, you can remove all columns that have a numeric data type.

Create a sample DataFrame

data = {'Name': ['John', 'Mike', 'Sara'],
'Age': [25, 30, 35],
'Address': ['New York', 'Chicago', 'Los Angeles'],
'Phone': [123, 456, 789],
'Salary':[2000,3000,4000]
}
df = pd.DataFrame(data)

Print the original DataFrame

print(df)

Remove all numeric columns

df = df.drop(columns=df.select_dtypes(['int64','float64']).columns)

Print the modified DataFrame

print(df)

The output of the first `print(df)` statement will be:

Name Age Address Phone Salary
0 John 25 New York 123 2000
1 Mike 30 Chicago 456 3000
2 Sara 35 Los Angeles 789 4000

And the output of the second `print(df)` statement will be:

Name Address
0 John New York
1 Mike Chicago
2 Sara Los Angeles

It's also possible to remove a column by its index. For example, you can remove the first column of a DataFrame by passing the index 0 to the `drop()` function.

Create a sample DataFrame

Popular questions

1. How can I remove a specific column from a DataFrame in pandas?

• You can remove a specific column from a DataFrame in pandas by using the drop() function and passing the column name as an argument. For example:

df = df.drop(columns='column_name')

2. How can I remove multiple columns from a DataFrame in pandas?

• You can remove multiple columns from a DataFrame in pandas by using the drop() function and passing a list of column names as an argument. For example:

df = df.drop(columns=['column_1', 'column_2', 'column_3'])

3. How can I remove all columns that contain a specific string in their name from a DataFrame in pandas?

• You can remove all columns that contain a specific string in their name from a DataFrame in pandas by using a list comprehension to create a list of columns that match the condition and passing that list to the drop() function. For example:

df = df.drop(columns=[col for col in df.columns if 'specific_string' in col])

4. How can I remove all columns with a specific data type from a DataFrame in pandas?

• You can remove all columns with a specific data type from a DataFrame in pandas by using the select_dtypes() function to select columns of the desired data type, then passing the resulting list of column names to the drop() function. For example:

df = df.drop(columns=df.select_dtypes(['int', 'float']).columns)

5. Can we remove a column by its index in DataFrame?

• Yes, it is possible to remove a column by its index in a DataFrame by passing the index to the drop() function. For example:

df = df.drop(df.columns[index], axis=1)

Please note that it will drop the column at specific index and axis=1 is for column.

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