To remove commas from columns of a pandas dataframe, you can use the str.replace method along with the df.apply function to iterate over each column and remove the commas. Here's an example code snippet that demonstrates this:

import pandas as pd

Create a sample dataframe

data = {'A': ['1,000', '2,000', '3,000'], 'B': ['4,000', '5,000', '6,000']} df = pd.DataFrame(data)

Function to remove commas from a column

def remove_commas(column): return column.str.replace(',', '')

Apply the function to each column in the dataframe

df = df.apply(remove_commas)

Print the updated dataframe without commas

print(df)

In this code snippet, we define a function remove_commas that removes commas from a column using the str.replace method. We then apply this function to each column in the dataframe using the df.apply function, which returns a new dataframe with the commas removed. Finally, we print the updated dataframe without commas.

How do I replace commas with spaces in columns of a pandas dataframe?

You can use the replace() method in pandas to replace commas with spaces in columns of a dataframe. Here's an example on how to do it:

import pandas as pd

Create a sample dataframe

df = pd.DataFrame({'A': ['1,234', '2,345', '3,456'], 'B': ['4,567', '5,678', '6,789']})

Replace commas with spaces in columns 'A' and 'B'

df['A'] = df['A'].str.replace(',', ' ') df['B'] = df['B'].str.replace(',', ' ')

print(df)

This code will replace commas with spaces in columns 'A' and 'B' of the dataframe df. You can modify the code to include more columns or use a loop to iterate through multiple columns if needed.

What is the correct syntax for removing commas from columns of a pandas dataframe?

To remove commas from columns of a pandas dataframe, you can use the following syntax:

df['column_name'] = df['column_name'].str.replace(',', '')

This will replace any commas in the specified column with an empty string, effectively removing them from the data.

How to strip commas from columns in a pandas dataframe?

You can remove commas from columns in a pandas dataframe by using the str.replace() method along with a lambda function. Here's an example:

import pandas as pd

Create a sample dataframe

data = {'A': ['1,000', '2,000', '3,000'], 'B': ['4,000', '5,000', '6,000']} df = pd.DataFrame(data)

Remove commas from column 'A'

df['A'] = df['A'].apply(lambda x: x.replace(',', ''))

Remove commas from column 'B'

df['B'] = df['B'].apply(lambda x: x.replace(',', '')

print(df)

This will output:

  A     B

0 1000 4000 1 2000 5000 2 3000 6000

In this example, we used apply() along with a lambda function to remove commas from each value in columns 'A' and 'B of the dataframe.