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

DataFrame.drop_duplicates(subset=None, *, keep='first', inplace=False, ignore_index=False) [source] #. Return DataFrame with duplicate rows removed. Considering certain columns is optional. Indexes, including time indexes are ignored. Parameters:

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://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.

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

Series.drop_duplicates(*, keep='first', inplace=False, ignore_index=False) [source] #. Return Series with duplicate values removed. Parameters: keep{‘first’, ‘last’, False}, default ‘first’. Method to handle dropping duplicates: ‘first’ : Drop duplicates except for the first occurrence.

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://stackoverflow.com › questions › 26744826

If you want to drop any duplicates, this should work. The sort will place all valid entries after NAs, so they will have preference in the drop_duplicate logic. df.loc[df['B'] == 'none', 'B'] = np.nan df = df.sort(['A','B']).drop_duplicates(subset='A')

https://www.statology.org › pandas-drop-duplicates

The easiest way to drop duplicate rows in a pandas DataFrame is by using the drop_duplicates () function, which uses the following syntax: df.drop_duplicates (subset=None, keep=’first’, inplace=False) where: subset: Which columns to consider for identifying duplicates. Default is all columns.

https://pandas.pydata.org › ... › 1.3 › reference › api › pandas.DataFrame.drop_duplicates.html

pandas.DataFrame.drop_duplicates. ¶. DataFrame.drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False) [source] ¶. Return DataFrame with duplicate rows removed. Considering certain columns is optional. Indexes, including time indexes are ignored. Parameters.