将列float64/int64转换为pandas datafram中类型为float/int的列

Question

我想将pandas dataframe保存为Stata文件，但似乎存在一个问题，即列具有int64或{}类型，因此需要转换为标准Python类型int和{}。我找了很多东西，但没有找到解决问题的办法，因为没有一个办法对我有效。在

我试过使用类似的方法：

import numpy as np
def conversion(obj):
    if isinstance(obj, np.generic):
        return np.asscalar(obj)

mergeddfnew["speech_main_wordspersentcount_wc"]=mergeddfnew["speech_main_wordspersentcount_wc"].apply(conversion)

我也试过打字。列的类型始终保持不变。在

Answer 1

参见IO section of the docs：

Stata data files have limited data type support; only strings with 244 or fewer characters, int8, int16, int32, float32 and float64 can be stored in .dta files. Additionally, Stata reserves certain values to represent missing data. Exporting a non-missing value that is outside of the permitted range in Stata for a particular data type will retype the variable to the next larger size. For example, int8 values are restricted to lie between -127 and 100 in Stata, and so variables with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data type (. in Stata).

不过，熊猫会尽力克服这些限制，为你转化：

The Stata writer gracefully handles other data types including int64, bool, uint8, uint16, uint32 by casting to the smallest supported type that can represent the data. For example, data with a type of uint8 will be cast to int8 if all values are less than 100 (the upper bound for non-missing int8 data in Stata), or, if values are outside of this range, the variable is cast to int16.

也就是说，你的专栏似乎不满足这些条件。在

我会尝试手动将其转换为dta支持的int32（假设它是int）：

df["speech_main_wordspersentcount_wc"].astype(np.int32)
df["speech_main_wordspersentcount_wc"] = df["speech_main_wordspersentcount_wc"].astype(np.int32)