pandas.DataFrame.set_index#
Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.
Parameters keys label or array-like or list of labels/arrays
This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, “array” encompasses Series , Index , np.ndarray , and instances of Iterator .
drop bool, default True
Delete columns to be used as the new index.
append bool, default False
Whether to append columns to existing index.
inplace bool, default False
Whether to modify the DataFrame rather than creating a new one.
verify_integrity bool, default False
Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method.
Returns DataFrame or None
Changed row labels or None if inplace=True .
Change to new indices or expand indices.
Change to same indices as other DataFrame.
>>> df = pd.DataFrame('month': [1, 4, 7, 10], . 'year': [2012, 2014, 2013, 2014], . 'sale': [55, 40, 84, 31]>) >>> df month year sale 0 1 2012 55 1 4 2014 40 2 7 2013 84 3 10 2014 31
Set the index to become the ‘month’ column:
>>> df.set_index('month') year sale month 1 2012 55 4 2014 40 7 2013 84 10 2014 31
Create a MultiIndex using columns ‘year’ and ‘month’:
>>> df.set_index(['year', 'month']) sale year month 2012 1 55 2014 4 40 2013 7 84 2014 10 31
Create a MultiIndex using an Index and a column:
>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year']) month sale year 1 2012 1 55 2 2014 4 40 3 2013 7 84 4 2014 10 31
Create a MultiIndex using two Series:
>>> s = pd.Series([1, 2, 3, 4]) >>> df.set_index([s, s**2]) month year sale 1 1 1 2012 55 2 4 4 2014 40 3 9 7 2013 84 4 16 10 2014 31