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POReindexing pandas timeseries from object dtype to datetime dtype
 

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  1. POReindexing pandas timeseries from object dtype to datetime dtype
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    <p>I have a time-series that is not recognized as a DatetimeIndex despite being indexed by standard YYYY-MM-DD strings with valid dates. Coercing them to a valid DatetimeIndex seems to be inelegant enough to make me think I'm doing something wrong.</p> <p>I read in (someone else's lazily formatted) data that contains invalid datetime values and remove these invalid observations.</p> <pre><code>In [1]: df = pd.read_csv('data.csv',index_col=0) In [2]: print df['2008-02-27':'2008-03-02'] Out[2]: count 2008-02-27 20 2008-02-28 0 2008-02-29 27 2008-02-30 0 2008-02-31 0 2008-03-01 0 2008-03-02 17 In [3]: def clean_timestamps(df): # remove invalid dates like '2008-02-30' and '2009-04-31' to_drop = list() for d in df.index: try: datetime.date(int(d[0:4]),int(d[5:7]),int(d[8:10])) except ValueError: to_drop.append(d) df2 = df.drop(to_drop,axis=0) return df2 In [4]: df2 = clean_timestamps(df) In [5] :print df2['2008-02-27':'2008-03-02'] Out[5]: count 2008-02-27 20 2008-02-28 0 2008-02-29 27 2008-03-01 0 2008-03-02 17 </code></pre> <p>This new index is still only recognized as a 'object' dtype rather than a DatetimeIndex. </p> <pre><code>In [6]: df2.index Out[6]: Index([2008-01-01, 2008-01-02, 2008-01-03, ..., 2012-11-27, 2012-11-28, 2012-11-29], dtype=object) </code></pre> <p>Reindexing produces NaNs because they're different dtypes.</p> <pre><code>In [7]: i = pd.date_range(start=min(df2.index),end=max(df2.index)) In [8]: df3 = df2.reindex(index=i,columns=['count']) In [9]: df3['2008-02-27':'2008-03-02'] Out[9]: count 2008-02-27 NaN 2008-02-28 NaN 2008-02-29 NaN 2008-03-01 NaN 2008-03-02 NaN </code></pre> <p>I create a fresh dataframe with the appropriate index, drop the data to a dictionary, then populate the new dataframe based on the dictionary values (skipping missing values).</p> <pre><code>In [10]: df3 = pd.DataFrame(columns=['count'],index=i) In [11]: values = dict(df2['count']) In [12]: for d in i: try: df3.set_value(index=d,col='count',value=values[d.isoformat()[0:10]]) except KeyError: pass In [13]: print df3['2008-02-27':'2008-03-02'] Out[13]: count 2008-02-27 20 2008-02-28 0 2008-02-29 27 2008-03-01 0 2008-03-02 17 In [14]: df3.index Out[14]; &lt;class 'pandas.tseries.index.DatetimeIndex'&gt; [2008-01-01 00:00:00, ..., 2012-11-29 00:00:00] Length: 1795, Freq: D, Timezone: None </code></pre> <p>This last part of setting values based on lookups to a dictionary keyed by strings seems especially hacky and makes me think I've missed something important.</p>
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