英文:
pandas apply a list of api functions to many dataframes
问题
以下是您提供的内容的翻译部分：
我想将一系列 pandas.api 函数应用于多个数据框并获取它们的响应。
我的代码：
panda_apis = [
pd.api.types.infer_dtype,
pd.api.types.is_bool_dtype,
pd.api.types.is_categorical_dtype,
pd.api.types.is_complex_dtype,
pd.api.types.is_datetime64_any_dtype,
pd.api.types.is_datetime64_dtype]
api_df = pd.DataFrame(data={'function':[i.__name__ for i in panda_apis]})
api_df.head()
# 两个示例数据框
df1 =
Datetime value
0 2002-12-31 01:00:00 5077.0
1 2002-12-31 02:00:00 4939.0
2 2002-12-31 03:00:00 4885.0
3 2002-12-31 04:00:00 4857.0
df2 =
Datetime value
0 2013-12-31 01:00:00 1861.0
1 2013-12-31 02:00:00 1835.0
2 2013-12-31 03:00:00 1841.0
3 2013-12-31 04:00:00 1872.0
4 2013-12-31 05:00:00 1934.0
df1 = df1.addprefix('df1_')
api_df[df1.columns] = api_df['function'].apply(lambda x: eval(x(df1)))
目前的输出：
TypeError: 'str' object is callable
期望的输出：
```python
api_df =
function df1_Datetime df1_value df2_Datetime df2_value
0 infer_dtype string float ....
1 is_bool_dtype False False ..
2 is_categorical_dtype False False ..
3 is_complex_dtype False False ..
4 is_datetime64_any_dtype True False ..
请注意，由于代码的复杂性，可能需要进一步的调试以获得期望的输出。
<details>
<summary>英文:</summary>
I want apply a list of [`pandas.api`][1] functions to many dataframes and get their response.
My code:
panda_apis = [
pd.api.types.infer_dtype,
pd.api.types.is_bool_dtype,
pd.api.types.is_categorical_dtype,
pd.api.types.is_complex_dtype,
pd.api.types.is_datetime64_any_dtype,
pd.api.types.is_datetime64_dtype]
api_df = pd.DataFrame(data={'function':[i.__name__ for i in panda_apis]})
api_df.head()
function
0 infer_dtype
1 is_bool_dtype
2 is_categorical_dtype
3 is_complex_dtype
4 is_datetime64_any_dtype
# Two example dataframes
df1 =
Datetime value
0 2002-12-31 01:00:00 5077.0
1 2002-12-31 02:00:00 4939.0
2 2002-12-31 03:00:00 4885.0
3 2002-12-31 04:00:00 4857.0
df2 =
Datetime value
0 2013-12-31 01:00:00 1861.0
1 2013-12-31 02:00:00 1835.0
2 2013-12-31 03:00:00 1841.0
3 2013-12-31 04:00:00 1872.0
4 2013-12-31 05:00:00 1934.0
df1 = df1.addprefix('df1_')
api_df[df1.columns] = api_df['function'].apply(lambda x: eval(x(df1)))
Present output:
TypeError: 'str' object is not callable
Expected output:
api_df =
function df1_Datetime df1_value df2_Datetime df2_value
0 infer_dtype string float ....
1 is_bool_dtype False False ..
2 is_categorical_dtype False False ..
3 is_complex_dtype False False ..
4 is_datetime64_any_dtype True False ..
[1]: https://pandas.pydata.org/docs/reference/api/pandas.api.types.pandas_dtype.html
</details>
# 答案1
**得分**: 1
lst = [df1.add_prefix("df1_"), df2.add_prefix("df2_")]
out = (
api_df.join(
pd.concat([pd.DataFrame([
df.apply(f) for f in panda_apis]) for df in lst], axis=1)
)
)
*NB : A DataFrame column can't have more than one [dtype][1]*.
Output :
print(out)
function df1_Datetime df1_value df2_Datetime df2_value
0 infer_dtype string floating string floating
1 is_bool_dtype False False False False
2 is_categorical_dtype False False False False
3 is_complex_dtype False False False False
4 is_datetime64_any_dtype False False False False
5 is_datetime64_dtype False False False False
With the `Datetime` *s* cast to `datetime64[ns]`, we get :
function df1_Datetime df1_value df2_Datetime df2_value
0 infer_dtype datetime64 floating datetime64 floating
1 is_bool_dtype False False False False
2 is_categorical_dtype False False False False
3 is_complex_dtype False False False False
4 is_datetime64_any_dtype True False True False
5 is_datetime64_dtype True False True False
[1]: https://pandas.pydata.org/docs/user_guide/basics.html#dtypes
<details>
<summary>英文:</summary>
You can try this :
lst = [df1.add_prefix("df1_"), df2.add_prefix("df2_")]
out = (
api_df.join(
pd.concat([pd.DataFrame([
df.apply(f) for f in panda_apis]) for df in lst], axis=1)
)
)
*NB : A DataFrame column can't have more than one [dtype][1]*.
Output :
print(out)
function df1_Datetime df1_value df2_Datetime df2_value
0 infer_dtype string floating string floating
1 is_bool_dtype False False False False
2 is_categorical_dtype False False False False
3 is_complex_dtype False False False False
4 is_datetime64_any_dtype False False False False
5 is_datetime64_dtype False False False False
With the `Datetime` *s* cast to `datetime64[ns]`, we get :
function df1_Datetime df1_value df2_Datetime df2_value
0 infer_dtype datetime64 floating datetime64 floating
1 is_bool_dtype False False False False
2 is_categorical_dtype False False False False
3 is_complex_dtype False False False False
4 is_datetime64_any_dtype True False True False
5 is_datetime64_dtype True False True False
[1]: https://pandas.pydata.org/docs/user_guide/basics.html#dtypes
</details>
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