Drop_Duplicates Not Working in Pandas?
The Purpose of My Code Is to Import 2 Excel Files, Compare Them, and Print out the Differences to a New Excel File. However, After Concatenating All the Data...
The purpose of my code is to import 2 Excel files, compare them, and print out the differences to a new Excel file.
However, after concatenating all the data, and using the drop_duplicates function, the code is accepted by the console. But, when printed to the new excel file, duplicates still remain within the day.
Am I missing something? Is something nullifying the drop_duplicates function?
My code is as follows:
import datetime
import xlrd
import pandas as pd
#identify excel file paths
filepath = r"excel filepath"
filepath2 = r"excel filepath2"
#read relevant columns from the excel files
df1 = pd.read_excel(filepath, sheetname="Sheet1", parse_cols= "B, D, G, O")
df2 = pd.read_excel(filepath2, sheetname="Sheet1", parse_cols= "B, D, F, J")
#merge the columns from both excel files into one column each respectively
df4 = df1["Exchange Code"] + df1["Product Type"] + df1["Product Description"] + df1["Quantity"].apply(str)
df5 = df2["Exchange"] + df2["Product Type"] + df2["Product Description"] + df2["Quantity"].apply(str)
#concatenate both columns from each excel file, to make one big column containing all the data
df = pd.concat([df4, df5])
#remove all whitespace from each row of the column of data
df=df.str.strip()
df=["".join(x.split()) for x in df]
#convert the data to a dataframe from a series
df = pd.DataFrame({'Value': df})
#remove any duplicates
df.drop_duplicates(subset=None, keep="first", inplace=False)
#print to the console just as a visual aid
print(df)
#print the erroneous entries to an excel file
df.to_excel("Comparison19.xls")
5 Answers
You've got inplace=False so you're not modifying df. You want either
df.drop_duplicates(subset=None, keep="first", inplace=True)
or
df = df.drop_duplicates(subset=None, keep="first", inplace=False)
I have just had this issue, and this was not the solution.
It may be in the docs - I admittedly havent looked - and crucially this is only when dealing with date-based unique rows: the 'date' column must be formatted as such.
If the date data is a pandas object dtype, the drop_duplicates will not work - do a pd.to_datetime first.
Might help anyone in the future.
I had a column with dates, where I tried to remove duplicates without success. If it's not important to keep the column as a date for further operations, I converted the column from type object to string.
df = df.astype('str')
Then I performed @Keith answers
df = df.drop_duplicates(subset=None, keep="first", inplace=False)
If you are using a DatetimeIndex in your DataFrame this will not work
df.drop_duplicates(subset=None, keep="first", inplace=True)
Instead one can use:
df = df[~df.index.duplicated()]
The use of inplace=False tells pandas to return a new dataframe with duplicates dropped, so you need to assign that back to df:
df = df.drop_duplicates(subset=None, keep="first", inplace=False)
or inplace=True to tell pandas to drop duplicates in the current dataframe
df.drop_duplicates(subset=None, keep="first", inplace=True)
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