python - 为什么在调用 '.values' 时 pd.Timestamp 转换为 np.datetime64 ？

Question

访问时DataFrame.values , 全部 pd.Timestamp对象转换为 np.datetime64对象，为什么？ 安 np.ndarray包含 pd.Timestamp对象可以存在，因此我不明白为什么总是会发生这种自动转换。

你知道如何预防吗？

最小的例子:

import numpy as np
import pandas as pd
from datetime import datetime

# Let's declare an array with a datetime.datetime object
values = [datetime.now()]
print(type(values[0]))
> <class 'datetime.datetime'>

# Clearly, the datetime.datetime objects became pd.Timestamp once moved to a pd.DataFrame
df = pd.DataFrame(values, columns=['A'])
print(type(df.iloc[0][0]))
> <class 'pandas._libs.tslibs.timestamps.Timestamp'>

# Just to be sure, lets iterate over each datetime and manually convert them to pd.Timestamp
df['A'].apply(lambda x: pd.Timestamp(x))
print(type(df.iloc[0][0]))
> <class 'pandas._libs.tslibs.timestamps.Timestamp'>

# df.values (or series.values in this case) returns an np.ndarray
print(type(df.iloc[0].values))
> <class 'numpy.ndarray'>

# When we check what is the type of elements of the '.values' array, 
# it turns out the pd.Timestamp objects got converted to np.datetime64
print(type(df.iloc[0].values[0]))
> <class 'numpy.datetime64'>


# Just to double check, can an np.ndarray contain pd.Timestamps?
timestamp = pd.Timestamp(datetime.now())
timestamps = np.array([timestamp])
print(type(timestamps))
> <class 'numpy.ndarray'>

# Seems like it does. Why the above conversion then?
print(type(timestamps[0]))
> <class 'pandas._libs.tslibs.timestamps.Timestamp'>

python :3.6.7.final.0

Pandas :0.25.3

NumPy :1.16.4

Answer 1

找到了解决方法 - 使用 .array而不是 .values ( docs )

print(type(df['A'].array[0]))
> <class 'pandas._libs.tslibs.timestamps.Timestamp'>

这可以防止转换并使我可以访问我想要使用的对象。