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I have the following pandas dataframe:
token year uses books 386 xanthos 1830 3 3 387 xanthos 1840 1 1 388 xanthos 1840 2 2 389 xanthos 1868 2 2 390 xanthos 1875 1 1 I aggregate the rows with duplicate token and years like so:
dfalph = dfalph[['token','year','uses','books']].groupby(['token', 'year']).agg([np.sum]) dfalph.columns = dfalph.columns.droplevel(1) uses books token year xanthos 1830 3 3 1840 3 3 1867 2 2 1868 2 2 1875 1 1 Instead of having the 'token' and 'year' fields in the index, I would like to return them to columns and have an integer index.
990
To reset index after group by, at first group according to a column using groupby(). After that, use reset_index().
Overview: Create a dataframe using an dictionary. Group by item_id and find the max value. enumerate over the grouped dataframe and use the key which is an numeric value to return the alpha index value. Create an result_df dataframe if you desire.
pandas MultiIndex to ColumnsUse pandas DataFrame. reset_index() function to convert/transfer MultiIndex (multi-level index) indexes to columns. The default setting for the parameter is drop=False which will keep the index values as columns and set the new index to DataFrame starting from zero.
The Groupby Rolling function does not preserve the original index and so when dates are the same within the Group, it is impossible to know which index value it pertains to from the original dataframe.
Method #1: reset_index()
>>> g uses books sum sum token year xanthos 1830 3 3 1840 3 3 1868 2 2 1875 1 1 [4 rows x 2 columns] >>> g = g.reset_index() >>> g token year uses books sum sum 0 xanthos 1830 3 3 1 xanthos 1840 3 3 2 xanthos 1868 2 2 3 xanthos 1875 1 1 [4 rows x 4 columns] Method #2: don't make the index in the first place, using as_index=False
>>> g = dfalph[['token', 'year', 'uses', 'books']].groupby(['token', 'year'], as_index=False).sum() >>> g token year uses books 0 xanthos 1830 3 3 1 xanthos 1840 3 3 2 xanthos 1868 2 2 3 xanthos 1875 1 1 [4 rows x 4 columns] 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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