Rather than create 2 temporary dfs you can just pass these as params within a dict using the DataFrame constructor: pd.DataFrame({’email’:sf.index, ‘list’:sf.values}) There are lots of ways to construct a df, see the docs
A new answer to reflect the most current practices: as of now (v1.2.4), neither astype(‘str’) nor astype(str) work. As per the documentation, a Series can be converted to the string datatype in the following ways: df[‘id’] = df[‘id’].astype(“string”) df[‘id’] = pandas.Series(df[‘id’], dtype=”string”) df[‘id’] = pandas.Series(df[‘id’], dtype=pandas.StringDtype)
>>> import pandas as pd >>> df = pd.DataFrame({‘x’ : [1, 2, 3, 4], ‘y’ : [4, 5, 6, 7]}) >>> df x y 0 1 4 1 2 5 2 3 6 3 4 7 >>> s = df.ix[:,0] >>> type(s) <class ‘pandas.core.series.Series’> >>> =========================================================================== UPDATE If you’re reading this after June 2017, ix … Read more
.ix indexer works okay for pandas version prior to 0.20.0, but since pandas 0.20.0, the .ix indexer is deprecated, so you should avoid using it. Instead, you can use .loc or iloc indexers. You can solve this problem by: mask = df.my_channel > 20000 column_name=”my_channel” df.loc[mask, column_name] = 0 Or, in one line, df.loc[df.my_channel > … Read more
Since version 0.15.0 this can now be easily done using .dt to access just the date component: df[‘just_date’] = df[‘dates’].dt.date The above returns a datetime.date dtype, if you want to have a datetime64 then you can just normalize the time component to midnight so it sets all the values to 00:00:00: df[‘normalised_date’] = df[‘dates’].dt.normalize() This … Read more
I think concat is a nice way to do this. If they are present it uses the name attributes of the Series as the columns (otherwise it simply numbers them): In [1]: s1 = pd.Series([1, 2], index=[‘A’, ‘B’], name=”s1″) In [2]: s2 = pd.Series([3, 4], index=[‘A’, ‘B’], name=”s2″) In [3]: pd.concat([s1, s2], axis=1) Out[3]: s1 … Read more
either: df[‘index1’] = df.index or, .reset_index: df = df.reset_index(level=0) so, if you have a multi-index frame with 3 levels of index, like: >>> df val tick tag obs 2016-02-26 C 2 0.0139 2016-02-27 A 2 0.5577 2016-02-28 C 6 0.0303 and you want to convert the 1st (tick) and 3rd (obs) levels in the index … Read more