Answered by : sachin-verma
# Below are quick example
# keep first duplicate row
df2 = df.drop_duplicates()
# Using DataFrame.drop_duplicates() to keep first duplicate row
df2 = df.drop_duplicates(keep='first')
# keep last duplicate row
df2 = df.drop_duplicates( keep='last')
# Remove all duplicate rows
df2 = df.drop_duplicates(keep=False)
# Delete duplicate rows based on specific columns
df2 = df.drop_duplicates(subset=["Courses", "Fee"], keep=False)
# Drop duplicate rows in place
df.drop_duplicates(inplace=True)
# Using DataFrame.apply() and lambda function
df2 = df.apply(lambda x: x.astype(str).str.lower()).drop_duplicates(subset=['Courses', 'Fee'], keep='first')
Source : | Last Update : Wed, 26 Aug 20
Answered by : clean-chimpanzee-x9xh2muu92w1
data = data.drop_duplicates(subset=['City'], keep='first')
Source : https://duckduckgo.com/?q=pandas+drop+duplicates+from+column&t=brave&ia=web | Last Update : Sun, 22 May 22
Answered by : ben-levitas
# For dropping duplicate columns:
df = df.loc[:,~df.columns.duplicated()]
Source : | Last Update : Tue, 06 Dec 22
Answered by : attractive-alpaca-9p5g9fusgcy5
df3 = df3[~df3.index.duplicated(keep='first')]
Source : https://stackoverflow.com/questions/13035764/remove-pandas-rows-with-duplicate-indices | Last Update : Fri, 04 Feb 22
Answered by : athul-mathew
df.drop_duplicates(keep=False, inplace=True)
Source : https://stackoverflow.com/questions/23667369/drop-all-duplicate-rows-across-multiple-columns-in-python-pandas | Last Update : Tue, 02 Aug 22
Answered by : athul-mathew
df.drop_duplicates(subset=['brand', 'style'], keep='last')
Source : https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html | Last Update : Mon, 29 Aug 22
Answered by : lazy-lark-jenyffc7wa94
df.drop_duplicates()
Source : | Last Update : Mon, 30 May 22