Menu
I have the following df:
code . role . persons 123 . Janitor . 3 123 . Analyst . 2 321 . Vallet . 2 321 . Auditor . 5 The first line means that I have 3 persons with the role Janitors. My problem is that I would need to have one line for each person. My df should look like this:
df: code . role . persons 123 . Janitor . 3 123 . Janitor . 3 123 . Janitor . 3 123 . Analyst . 2 123 . Analyst . 2 321 . Vallet . 2 321 . Vallet . 2 321 . Auditor . 5 321 . Auditor . 5 321 . Auditor . 5 321 . Auditor . 5 321 . Auditor . 5 How could I do that using pandas?
792
Use DataFrame. drop_duplicates() to Drop Duplicate and Keep First Rows. You can use DataFrame. drop_duplicates() without any arguments to drop rows with the same values on all columns.
The pandas. DataFrame. duplicated() method is used to find duplicate rows in a DataFrame. It returns a boolean series which identifies whether a row is duplicate or unique.
repeat(3) will create a list where each index value will be repeated 3 times and df. iloc[df. index. repeat(3),:] will help generate a dataframe with the rows as exactly returned by this list.
You can loop over a pandas dataframe, for each column row by row.
reindex+ repeat
df.reindex(df.index.repeat(df.persons)) Out[951]: code . role ..1 persons 0 123 . Janitor . 3 0 123 . Janitor . 3 0 123 . Janitor . 3 1 123 . Analyst . 2 1 123 . Analyst . 2 2 321 . Vallet . 2 2 321 . Vallet . 2 3 321 . Auditor . 5 3 321 . Auditor . 5 3 321 . Auditor . 5 3 321 . Auditor . 5 3 321 . Auditor . 5 PS: you can add.reset_index(drop=True) to get the new index
151
Wen's solution is really nice and intuitive. Here's an alternative, calling repeat on df.values.
df code role persons 0 123 Janitor 3 1 123 Analyst 2 2 321 Vallet 2 3 321 Auditor 5 pd.DataFrame(df.values.repeat(df.persons, axis=0), columns=df.columns) code role persons 0 123 Janitor 3 1 123 Janitor 3 2 123 Janitor 3 3 123 Analyst 2 4 123 Analyst 2 5 321 Vallet 2 6 321 Vallet 2 7 321 Auditor 5 8 321 Auditor 5 9 321 Auditor 5 10 321 Auditor 5 11 321 Auditor 5 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
