如何检测 pandas 对象列中的子类型? [英] How could I detect subtypes in pandas object columns?

问题描述

我有下一个DataFrame:

I have the next DataFrame:

df = pd.DataFrame({'a': [100, 3,4], 'b': [20.1, 2.3,45.3], 'c': [datetime.time(23,52), 30,1.00]})

，如果可能的话，我想在没有显式编程循环的情况下检测列中的子类型 .

and I would like to detect subtypes in columns without explicit programming a loop, if possible.

我正在寻找下一个输出:

I am looking for the next output:

column a = [int]
column b = [float]
column c = [datetime.time, int, float]

推荐答案

您应该了解，使用Pandas可以有2种广泛的系列类型:

You should appreciate that with Pandas you can have 2 broad types of series:

1. 优化的结构:通常为数字数据，其中包括np.datetime64和bool.
2. object dtype:用于具有混合类型或不能在NumPy数组中本地保存的类型的系列.该系列的结构是指向任意Python对象的指针序列，通常效率不高.

1. Optimised structures: Usually numeric data, this includes np.datetime64 and bool.
2. object dtype: Used for series with mixed types or types which cannot be held natively in a NumPy array. The series is structured as a sequence of pointers to arbitrary Python objects and is generally inefficient.

此序言的原因是，您只需要对第二种类型应用基于元素的逻辑.第一类数据本质上是同类的.

The reason for this preamble is you should only ever need to apply element-wise logic to the second type. Data in the first category is homogeneous by nature.

所以您应该相应地分离逻辑.

So you should separate your logic accordingly.

使用 pd.DataFrame.dtypes :

print(df.dtypes)

a      int64
b    float64
c     object
dtype: object

object dtype

通过 pd.DataFrame.select_dtypes 然后使用字典理解:

object dtype

Isolate these series via pd.DataFrame.select_dtypes and then use a dictionary comprehension:

obj_types = {col: set(map(type, df[col])) for col in df.select_dtypes(include=[object])}

print(obj_types)

{'c': {int, datetime.time, float}}

您将需要做更多的工作才能获得所需的 exact 格式，但是以上内容应作为您的攻击计划.

You will need to do a little more work to get the exact format you require, but the above should be your plan of attack.

这篇关于如何检测 pandas 对象列中的子类型?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！