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POHaving an issue changing index from integer to date in pandas
 

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  1. POHaving an issue changing index from integer to date in pandas
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    <p>I'm having an issue changing a pandas DataFrame index to a datetime from an integer. I want to do it so that I can call reindex and fill in the dates between those listed in the table. Note that I have to use pandas 0.7.3 at the moment because I'm also using qstk, and qstk relies on pandas 0.7.3</p> <p>First, here's my layout:</p> <pre><code>(Pdb) df AAPL GOOG IBM XOM date 1 0 0 4000 0 2011-01-13 16:00:00 2 0 1000 4000 0 2011-01-26 16:00:00 3 0 1000 4000 0 2011-02-02 16:00:00 4 0 1000 4000 4000 2011-02-10 16:00:00 6 0 0 1800 4000 2011-03-03 16:00:00 7 0 0 3300 4000 2011-06-03 16:00:00 8 0 0 0 4000 2011-05-03 16:00:00 9 1200 0 0 4000 2011-06-10 16:00:00 11 1200 0 0 4000 2011-08-01 16:00:00 12 0 0 0 4000 2011-12-20 16:00:00 (Pdb) type(df['date']) &lt;class 'pandas.core.series.Series'&gt; (Pdb) df2 = DataFrame(index=df['date']) (Pdb) df2 Empty DataFrame Columns: array([], dtype=object) Index: array([2011-01-13 16:00:00, 2011-01-26 16:00:00, 2011-02-02 16:00:00, 2011-02-10 16:00:00, 2011-03-03 16:00:00, 2011-06-03 16:00:00, 2011-05-03 16:00:00, 2011-06-10 16:00:00, 2011-08-01 16:00:00, 2011-12-20 16:00:00], dtype=object) (Pdb) df2.merge(df,left_index=True,right_on='date') AAPL GOOG IBM XOM date 1 0 0 4000 0 2011-01-13 16:00:00 2 0 1000 4000 0 2011-01-26 16:00:00 3 0 1000 4000 0 2011-02-02 16:00:00 4 0 1000 4000 4000 2011-02-10 16:00:00 6 0 0 1800 4000 2011-03-03 16:00:00 8 0 0 0 4000 2011-05-03 16:00:00 7 0 0 3300 4000 2011-06-03 16:00:00 9 1200 0 0 4000 2011-06-10 16:00:00 11 1200 0 0 4000 2011-08-01 16:00:00 12 0 0 0 4000 2011-12-20 16:00:00 </code></pre> <p>I have tried multiple things to get a datetime index:</p> <p>1.) Using the reindex() method with a list of datetime values. This creates a datetime index, but then fills in NaNs for the data in the DataFrame. I'm guessing that this is because the original values are tied to the integer index and reindexing to datetime tries to fill the new indices with default values (NaNs if no fill method is indicated). Thusly:</p> <pre><code>(Pdb) df.reindex(index=df['date']) AAPL GOOG IBM XOM date date 2011-01-13 16:00:00 NaN NaN NaN NaN NaN 2011-01-26 16:00:00 NaN NaN NaN NaN NaN 2011-02-02 16:00:00 NaN NaN NaN NaN NaN 2011-02-10 16:00:00 NaN NaN NaN NaN NaN 2011-03-03 16:00:00 NaN NaN NaN NaN NaN 2011-06-03 16:00:00 NaN NaN NaN NaN NaN 2011-05-03 16:00:00 NaN NaN NaN NaN NaN 2011-06-10 16:00:00 NaN NaN NaN NaN NaN 2011-08-01 16:00:00 NaN NaN NaN NaN NaN 2011-12-20 16:00:00 NaN NaN NaN NaN NaN </code></pre> <p>2.) Using DataFrame.merge with my original df and a second dataframe, df2, that is basically just a datetime index with nothing else. So I end up doing something like:</p> <pre><code>(pdb) df2.merge(df,left_index=True,right_on='date') AAPL GOOG IBM XOM date 1 0 0 4000 0 2011-01-13 16:00:00 2 0 1000 4000 0 2011-01-26 16:00:00 3 0 1000 4000 0 2011-02-02 16:00:00 4 0 1000 4000 4000 2011-02-10 16:00:00 6 0 0 1800 4000 2011-03-03 16:00:00 8 0 0 0 4000 2011-05-03 16:00:00 7 0 0 3300 4000 2011-06-03 16:00:00 9 1200 0 0 4000 2011-06-10 16:00:00 11 1200 0 0 4000 2011-08-01 16:00:00 </code></pre> <p>(and vice-versa). But I always end up with this kind of thing, with integer indices.</p> <p>3.) Starting with an empty DataFrame with a datetime index (created from the 'date' field of df) and a bunch of empty columns. Then I attempt to assign each column by setting the columns with the same names to be equal to the columns from df:</p> <pre><code>(Pdb) df2['GOOG']=0 (Pdb) df2 GOOG date 2011-01-13 16:00:00 0 2011-01-26 16:00:00 0 2011-02-02 16:00:00 0 2011-02-10 16:00:00 0 2011-03-03 16:00:00 0 2011-06-03 16:00:00 0 2011-05-03 16:00:00 0 2011-06-10 16:00:00 0 2011-08-01 16:00:00 0 2011-12-20 16:00:00 0 (Pdb) df2['GOOG'] = df['GOOG'] (Pdb) df2 GOOG date 2011-01-13 16:00:00 NaN 2011-01-26 16:00:00 NaN 2011-02-02 16:00:00 NaN 2011-02-10 16:00:00 NaN 2011-03-03 16:00:00 NaN 2011-06-03 16:00:00 NaN 2011-05-03 16:00:00 NaN 2011-06-10 16:00:00 NaN 2011-08-01 16:00:00 NaN 2011-12-20 16:00:00 NaN </code></pre> <p>So, how in pandas 0.7.3 do I get df to be re-created with an datetime index instead of the integer index? What am I missing? </p>
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    1. COI don't have access to a machine with pandas 0.7.3 on it, but how are you reading the data? Does adding `index_col='date'` (or maybe just `index='date'` do anything?
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    2. CO@PaulH Thanks for the suggestion. I'm reading the data from a dictionary: `df = DataFrame(data=stocklist)`. If I pull the dates out of the dict with a list comprehension: `idx = [x['date'] for x in stocklist]`, I get a DataFrame with a datetime index. However, there are duplicate dates and I need to remove them with `df.drop_duplicates`. When I do that, I get an exception because duplicate index values are not allowed in pandas 0.7.3. I _think_ duplicate index values are allowed in later versions but for compatibility reasons, I'm dealing with 0.7.3.
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