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How do I go about resetting the index of my dataframe columns to 0,1,2,3,4?
(How come doing df.reset_index() doesn't reset the column index?)
>>> data = data.drop(data.columns[[1,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]], axis=1) >>> data = data.drop(data.index[[0,1]],axis = 0) >>> print(data.head()) 0 2 3 4 20 2 500292014600 .00 .00 .00 NaN 3 500292014600 100.00 .00 .00 NaN 4 500292014600 11202.00 .00 .00 NaN >>> data = data.reset_index(drop = True) >>> print(data.head()) 0 2 3 4 20 0 500292014600 .00 .00 .00 NaN 1 500292014600 100.00 .00 .00 NaN 2 500292014600 11202.00 .00 .00 NaN 
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Pandas DataFrame reset_index() Method The reset_index() method allows you reset the index back to the default 0, 1, 2 etc indexes. By default this method will keep the "old" idexes in a column named "index", to avoid this, use the drop parameter.
Use DataFrame.reset_index() function We can use DataFrame. reset_index() to reset the index of the updated DataFrame. By default, it adds the current row index as a new column called 'index' in DataFrame, and it will create a new row index as a range of numbers starting at 0.
Try the following:
df = df.T.reset_index(drop=True).T 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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