How to delete a row in a Pandas DataFrame and rewrite the index?

Question

I am reading a file in a Pandas DataFrame that may have invalid strings (e.g. NaN). This is serial data, so I have row_id + 1 to reference row_id. When I use frame.dropna (), I get the structure I want, but the index labels remain as they were originally assigned. How can index labels be reassigned from 0 to N-1, where N is the number of rows after dropna ()?

Answer 1

Use pandas.DataFrame.reset_index() , the drop=True option will do what you are looking for.

 In [14]: df = pd.DataFrame(np.random.randn(5,4)) In [15]: df.ix[::3] = np.nan In [16]: df Out[16]: 0 1 2 3 0 NaN NaN NaN NaN 1 1.895803 0.532464 1.879883 -1.802606 2 0.078928 0.053323 0.672579 -1.188414 3 NaN NaN NaN NaN 4 -0.766554 -0.419646 -0.606505 -0.162188 In [17]: df = df.dropna() In [18]: df.reset_index(drop=True) Out[18]: 0 1 2 3 0 1.895803 0.532464 1.879883 -1.802606 1 0.078928 0.053323 0.672579 -1.188414 2 -0.766554 -0.419646 -0.606505 -0.162188