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I have the following large dataframe (df) that looks like this:
ID date PRICE 1 10001 19920103 14.500 2 10001 19920106 14.500 3 10001 19920107 14.500 4 10002 19920108 15.125 5 10002 19920109 14.500 6 10002 19920110 14.500 7 10003 19920113 14.500 8 10003 19920114 14.500 9 10003 19920115 15.000 Question: What's the most efficient way to delete (or remove) the first row of each ID? I want this:
ID date PRICE 2 10001 19920106 14.500 3 10001 19920107 14.500 5 10002 19920109 14.500 6 10002 19920110 14.500 8 10003 19920114 14.500 9 10003 19920115 15.000 I can do a loop over each unique ID and remove the first row but I believe this is not very efficient.
815
Use iloc to drop first row of pandas dataframe. Use drop() to remove first row of pandas dataframe. Use tail() function to remove first row of pandas dataframe.
Remove First N Rows of Pandas DataFrame Using tail()tail(df. shape[0] -n) to remove the top/first n rows of pandas DataFrame. Generally, DataFrame. tail() function is used to show the last n rows of a pandas DataFrame but you can pass a negative value to skip the rows from the beginning.
To delete the first three rows of a DataFrame in Pandas, we can use the iloc() method.
Use pandas. DataFrame. drop() method to delete/remove rows with condition(s).
Another one line code is df.groupby('ID').apply(lambda group: group.iloc[1:, 1:])
Out[100]: date PRICE ID 10001 2 19920106 14.5 3 19920107 14.5 10002 5 19920109 14.5 6 19920110 14.5 10003 8 19920114 14.5 9 19920115 15.0 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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