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POPandas messing up dataframe
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copied!<p>I'm creating a data frame in Pandas—</p> <pre><code>df_data = dict() for x in data: series = pandas.Series(x['value']['values'], index=x['value']['timestamps']) df_data[x['_id']] = series df = pandas.DataFrame(df_data) </code></pre> <p><code>data</code> is a list of dicts in the format—</p> <pre><code>{u'_id': u'770000000049', u'value': {u'timestamps': [datetime.datetime(2012, 7, 25, 10, 16, 1, 270000), datetime.datetime(2012, 7, 25, 10, 18, 29, 745000), datetime.datetime(2012, 7, 25, 10, 21, 54, 931000), datetime.datetime(2012, 7, 25, 10, 23, 18, 896000)], u'values': [204.0, 16.788, 139.2, 116.004]}} </code></pre> <p>Printing an example series gives me—</p> <pre><code>>>> print df_data['770000000049'] >>> 2012-07-25 10:16:01.270000 204.000 2012-07-25 10:18:29.745000 16.788 2012-07-25 10:21:54.931000 139.200 2012-07-25 10:23:18.896000 116.004 </code></pre> <p>As expected. However, printing the resulting data frame gives me—</p> <pre><code>>>> print df['770000000049'] >>> 1992-06-05 15:50:11.527680 NaN 2181-10-17 22:55:34.850625 NaN 2215-08-27 21:41:15.306049 NaN 1936-05-22 00:55:45.848401 NaN 1783-06-08 06:38:26.257076 NaN 2017-03-12 18:30:17.469108 NaN 2209-08-06 03:45:09.779652 NaN 1768-02-06 12:00:22.653272 NaN 1916-07-20 06:51:31.628376 NaN 2086-01-25 18:30:58.261336 NaN 1940-08-26 15:13:33.790568 NaN 1712-12-17 22:48:01.743241 NaN 1803-06-16 16:32:58.309017 NaN 1981-11-05 04:38:27.140059 NaN 2246-05-25 09:09:27.875035 NaN ... </code></pre> <p>WTF! The data is all wrong. Both keys and values are completely wrong.</p> <p>What am I doing wrong? </p> <p><strong>Edit</strong>: Printing <code>df</code> gives me—</p> <pre><code>DatetimeIndex: 386 entries, 1992-06-05 15:50:11.527680 to 1774-08-13 02:00:15.237103 Data columns: 770000000006 0 non-null values 770000000009 0 non-null values 770000000010 0 non-null values 770000000011 0 non-null values 770000000012 0 non-null values 770000000013 0 non-null values 770000000018 0 non-null values 770000000020 0 non-null values 770000000021 0 non-null values 770000000022 0 non-null values 770000000024 0 non-null values 770000000029 0 non-null values 770000000030 0 non-null values 770000000032 0 non-null values 770000000034 0 non-null values 770000000049 0 non-null values dtypes: float64(16) </code></pre> <p>Completely wrong</p> <p><strong>Edit 2</strong>:</p> <p>I've <a href="http://pastebin.com/WTsCkKrL" rel="nofollow">written a module</a> that reproduces the bug for me.</p>

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