cannot convert float NaN to integer的错误。这个错误通常是由于我们试图将一个NaN（Not a Numb...
dtype: datetime64[ns] In [566]: store.select_column("df_dc", "string") Out[566]: 0 foo 1 foo 2 foo 3 foo 4 NaN 5 NaN 6 foo 7 bar Name: string, dtype: object
File ~/work/pandas/pandas/pandas/core/series.py:1237,inSeries._get_value(self, label, takeable)1234returnself._values[label]1236# Similar to Index.get_value, but we do not fall back to positional->1237loc = self.index.get_loc(label)1239ifis_integer(loc):1240returnself._values[loc] Fi...
如果您指定了一个数字(一个 `float`,比如 `5.0` 或一个 `integer`,比如 `5`),则相应的等价值也将暗示一个缺失值(在这种情况下,实际上 `[5.0, 5]` 被认为是 `NaN`)。要完全覆盖默认被识别为缺失的值,请指定 `keep_default_na=False`。默认的 `NaN` 被识别的值为 `['-1.#IND', '1.#QNAN',...
np.nan cannot be coerced into an integer. If either the column it belongs to has float or the row it belongs to has float, it does not throw the error - rather its datatype becomes float as well pd.DataFrame({'a': [1, 2], 'b': pd.Categorical([3.0, np.nan])}).iloc[1, :...
(key): File ~/work/pandas/pandas/pandas/core/series.py:1237, in Series._get_value(self, label, takeable) 1234 return self._values[label] 1236 # Similar to Index.get_value, but we do not fall back to positional -> 1237 loc = self.index.get_loc(label) 1239 if is_integer(loc): ...
students.rename(columns={'Foo':'FOO','Name':'NAME'},inplace=True) students 修改其中的一部分为空 # 不转变ID类型为浮点,会报错Cannot assign nan to integer series,意思是不能讲整型去修改为nanstudents['ID'] = students['ID'].astype(float)foriinrange(5,15): ...
inplace:是否直接修改原始 DataFrame。默认为 False。 limit:填充的连续 NaN 数量最大值。 downcast:指定数据类型,取值为 {‘infer’, ‘integer’, ‘signed’, ‘unsigned’, ‘float’, ‘complex’},用来对填充后的数据类型进行优化。默认为None。 例如,下面的代码将 DataFrame 中所有的 NaN 填充为 0: impo...
d NaN Name: st, dtype: float64 2.Series属性和方法 s1.index.name="first" s1 #first a 1 b 2 c 3 Name: s2, dtype: int64 s1.index.name #'first' import pandas as pd s=pd.Series(list("abcdf")) print(s) 输出: 0 a 1 b ...
但是如果想要获取部分行部分列的上述两种方法就无能为力了。这时就得用到ix, loc,iloc方法(ix已弃用)loc是指location的意思,iloc中的i是指integer。iloc和loc方式索引也更为精细。这两者的区别如下:...