As can be seen in the below code, column 3, 8, 9, and 10 were misinterpreted as datetime objects. Columns 1, 6 and 7 should be integer. How do I force the columns to be interpreted as the proper type? Only 2, 4, 5 and 11 appear to have been read properly. I can pass 'infer_types=False' I suppose and do manual conversion afterwards, but since infer_types is going away, this won't work.
In [63]: import pandas as pd
In [64]: path = r"http://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_population"
In [65]: tables = pd.read_html(path)
In [66]: df = tables[1]
In [67]: df.head()
Out[67]:
1 2 3 4 5 6 7 8 \
1 !000001 California NaT 37253956 33871648 !000053 !000055 NaT
2 !000002 Texas NaT 25145561 20851820 !000036 !000038 NaT
3 !000003 New York 1965-11-27 19378102 18976457 !000027 !000029 NaT
4 !000004 Florida NaT 18801310 15982378 !000027 !000029 NaT
5 !000005 Illinois NaT 12830632 12419293 !000018 !000020 NaT
9 10 11
1 NaT NaT 11.91%
2 NaT NaT 8.04%
3 NaT NaT 6.19%
4 NaT NaT 6.01%
5 NaT NaT 4.10%
[5 rows x 11 columns]
dtype: object
In [68]: df.dtypes
Out[68]:
1 object
2 object
3 datetime64[ns]
4 object
5 object
6 object
7 object
8 datetime64[ns]
9 datetime64[ns]
10 datetime64[ns]
11 object
dtype: object
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