Best Data Cleaning Tools to Buy in October 2025
Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools
Ordilend for iPhone Cleaning Kit for Charging Port Cleaner, Cleaner Kit for AirPod Multi-Tool iPhone Cleaner Repair Lightning Cable for iPad Connector Airpod Speaker Compact Portable with Storage Case
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Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights
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Python Data Cleaning Cookbook: Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
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Cleaner Kit for AirPod, Multi-Tool iPhone Cleaning Kit, Cell Phone Cleaning Repair & Recovery iPhone and iPad (Type C) Charging Port, Lightning Cables, and Connectors, Easy to Store and Carry Design
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SMALLRIG x Andyax Creator Toolbox - Hard Case with Assembly, Lens Cleaning, Data Storage & Adhesive Labeling Sets
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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.