How to Delete A Column In Pandas?

To delete a column in pandas, you can use the drop() method along with the axis parameter set to 1. This will drop the specified column from the DataFrame. For example, if you have a DataFrame named df and you want to delete a column named column_name, you can use the following code:

1

df.drop('column_name', axis=1, inplace=True)

This will remove the specified column from the DataFrame df. The inplace=True parameter is used to modify the original DataFrame instead of creating a new copy.

How to drop a column based on condition in pandas?

To drop a column based on a condition in pandas, you can use the drop() method along with boolean indexing. Here is an example of how you can drop a column based on a condition:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3, 4],
        'B': [5, 6, 7, 8],
        'C': [9, 10, 11, 12]}
df = pd.DataFrame(data)

# Drop the column 'B' if all values are greater than 5
if all(df['B'] > 5):
    df = df.drop('B', axis=1)

print(df)

In this example, the column 'B' will be dropped only if all the values in the column are greater than 5. You can modify the condition based on your specific requirement.

How to delete multiple columns in pandas DataFrame?

To delete multiple columns in a pandas DataFrame, you can use the drop() method by passing a list of column names you want to delete. Here is an example:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6],
        'C': [7, 8, 9],
        'D': [10, 11, 12]}
df = pd.DataFrame(data)

# List of column names to delete
cols_to_delete = ['B', 'C']

# Delete multiple columns
df.drop(cols_to_delete, axis=1, inplace=True)

print(df)

In this example, we first create a DataFrame df with columns A, B, C, and D. We then define a list cols_to_delete with column names 'B' and 'C' that we want to delete. Finally, we use the drop() method to delete the specified columns along the columns axis (axis=1) and set inplace=True to modify the original DataFrame in place.

What is the function to drop a column in pandas DataFrame?

The function to drop a column in pandas DataFrame is drop().

You can drop a column by providing the column name as an argument to the drop() function. For example:

1

df.drop('column_name', axis=1, inplace=True)

In this example, 'column_name' is the name of the column you want to drop, axis=1 specifies that you want to drop a column (as opposed to a row), and inplace=True specifies that you want to drop the column in place (i.e., modify the original DataFrame).

How to drop a column by label in pandas DataFrame?

You can drop a column by label in a pandas DataFrame using the drop() method. Here's an example of how to do this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

# Display the original DataFrame
print("Original DataFrame:")
print(df)

# Drop the column 'B' by label
df.drop('B', axis=1, inplace=True)

# Display the modified DataFrame
print("\nDataFrame after dropping column 'B':")
print(df)

In the above example, the drop() method is used to drop the column 'B' by label. The axis=1 parameter specifies that we want to drop a column (as opposed to a row) and inplace=True ensures that the DataFrame is modified in place (i.e., the change is applied directly to the original DataFrame).

What is the effect of dropping a column on the memory usage of DataFrame?

Dropping a column from a DataFrame can reduce the memory usage as it will eliminate the storage space used by that column. By removing a column, the DataFrame will have fewer columns and hence require less memory to store the data. This can be particularly beneficial if the column that is being dropped is not necessary for further analysis or calculations. Dropping unnecessary columns can help optimize memory usage and improve the performance of the DataFrame.