https://pandas.pydata.org › ... › stable › reference › api › pandas.DataFrame.drop_duplicates.html

Learn how to remove duplicate rows from a DataFrame based on certain columns or all columns. See parameters, return value and examples of using subset, keep, inplace and ignore_index options.

https://stackoverflow.com › questions › 50885093

Use drop_duplicates() by using column name. import pandas as pd data = pd.read_excel('your_excel_path_goes_here.xlsx') #print(data) data.drop_duplicates(subset=["Column1"], keep="first") keep=first to instruct Python to keep the first value and remove other columns duplicate values.

https://datatofish.com › remove-duplicates-pandas-dataframe

Learn how to use df.drop_duplicates() method to remove duplicates across the entire DataFrame or under a specific column. See examples of data, code and output with explanations.

https://www.w3schools.com › python › pandas › ref_df_drop_duplicates.asp

The drop_duplicates() method removes duplicate rows. Use the subset parameter if only some specified columns should be considered when looking for duplicates. Syntax. dataframe.drop_duplicates (subset, keep, inplace, ignore_index) Parameters. The parameters are keyword arguments. Return Value.

https://www.slingacademy.com › article › pandas-removing-duplicate-rows-from-a-dataframe

Duplicates can skew your data, give you misleading results, and overall, just get in the way of clean, interpretable data. This tutorial will guide you through various methods of identifying and removing duplicate rows using Pandas, applicable from basic to advanced use cases.

https://note.nkmk.me › en › python-pandas-duplicated-drop-duplicates

Learn how to use duplicated() and drop_duplicates() methods to handle duplicate rows in pandas DataFrame or Series. See examples, arguments, and alternatives such as groupby().

https://towardsdatascience.com › finding-and-removing-duplicate-rows-in-pandas-dataframe...

1. Finding duplicate rows. To find duplicates on a specific column, we can simply call duplicated() method on the column. >>> df.Cabin.duplicated() 0 False. 1 False. 9 False. 10 False. 14 False. ...