在Pandas列中查找混合类型 [英] Find mixed types in Pandas columns

问题描述

在解析数据文件时，我经常收到以下警告:

Ever so often I get this warning when parsing data files:

WARNING:py.warnings:/usr/local/python3/miniconda/lib/python3.4/site-
packages/pandas-0.16.0_12_gdcc7431-py3.4-linux-x86_64.egg/pandas
/io/parsers.py:1164: DtypeWarning: Columns (0,2,14,20) have mixed types. 
Specify dtype option on import or set low_memory=False.
          data = self._reader.read(nrows)

但是，如果数据很大(我有5万行)，如何在数据中找到dtype发生变化的地方?

But if the data is large (I have 50k rows), how can I find WHERE in the data the change of dtype occurs?

推荐答案

我不确定您要做什么，但是很容易找到包含不共享第一个元素类型的元素的行排.例如:

I'm not entirely sure what you're after, but it's easy enough to find the rows which contain elements which don't share the type of the first row. For example:

>>> df = pd.DataFrame({"A": np.arange(500), "B": np.arange(500.0)})
>>> df.loc[321, "A"] = "Fred"
>>> df.loc[325, "B"] = True
>>> weird = (df.applymap(type) != df.iloc[0].apply(type)).any(axis=1)
>>> df[weird]
        A     B
321  Fred   321
325   325  True

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