Search results
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.
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.
Simply do df3 = df3[~df3.index.duplicated(keep='first')], which will drop all rows with duplicate index except the first occurrence. –
3 sie 2022 · Pandas drop_duplicates () Function Syntax. Pandas drop_duplicates () function removes duplicate rows from the DataFrame. Its syntax is: drop_duplicates(self, subset=None, keep="first", inplace=False) subset: column label or sequence of labels to consider for identifying duplicate rows.
18 gru 2020 · 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)
20 lut 2024 · Removing Duplicate Rows. Once you’ve identified duplicates, removing them is straightforward with the drop_duplicates() method. By default, this method keeps the first occurrence of the duplicate row and removes subsequent duplicates.
5 sie 2022 · You can use the following methods to remove duplicates in a pandas DataFrame but keep the row that contains the max value in a particular column: Method 1: Remove Duplicates in One Column and Keep Row with Max. df.sort_values('var2', ascending=False).drop_duplicates('var1').sort_index()
