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16 cze 2018 · 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.
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:
20 lut 2013 · Here's a one line solution to remove columns based on duplicate column names: df = df.loc[:,~df.columns.duplicated()].copy() How it works: Suppose the columns of the data frame are ['alpha','beta','alpha'] df.columns.duplicated() returns a boolean array: a True or False for each column.
10 sie 2024 · How to Remove Duplicates in a DataFrame in Python? To remove duplicates from a DataFrame, you can use the drop_duplicates() method. This method allows you to specify whether to drop duplicates across all columns or just a subset. Example: # Remove duplicates considering only column 'A' clean_df = df.drop_duplicates(subset=['A']) print(clean_df)
26 sty 2024 · In pandas, the duplicated() method is used to find, extract, and count duplicate rows in a DataFrame, while drop_duplicates() is used to remove these duplicates. This article also briefly explains the groupby() method, which aggregates values based on duplicates.
9 mar 2023 · The DataFrame.drop_duplicates() function. This function is used to remove the duplicate rows from a DataFrame. DataFrame.drop_duplicates(subset= None, keep= 'first', inplace= False, ignore_index= False) Parameters: subset: By default, if the rows have the same values in all the columns, they are considered duplicates.
22 gru 2023 · Remove duplicate columns from a DataFrame using df.columns.duplicated() Pandas df .duplicated() method helps in analyzing duplicate values only. It returns a boolean series which is True only for Unique elements.
