如何将数字数据映射到 Pandas 数据框中的类别/箱
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How to map numeric data into categories / bins in Pandas dataframe
提问by kiltannen
I've just started coding in python, and my general coding skills are fairly rusty :( so please be a bit patient
我刚刚开始用 python 编码，我的一般编码技能相当生疏:(所以请耐心等待
I have a pandas dataframe:
我有一个Pandas数据框：
It has around 3m rows. There are 3 kinds of age_units: Y, D, W for years, Days & Weeks. Any individual over 1 year old has an age unit of Y and my first grouping I want is <2y old so all I have to test for in Age Units is Y...
它有大约 3m 行。有 3 种年龄单位：Y、D、W 表示年、天和周。任何超过 1 岁的人的年龄单位都是 Y，我想要的第一个分组是 <2 岁，所以我必须在年龄单位中测试的是 Y...
I want to create a new column AgeRange and populate with the following ranges:
我想创建一个新列 AgeRange 并填充以下范围：
- <2
- 2 - 18
- 18 - 35
- 35 - 65
- 65+
- <2
- 2 - 18
- 18 - 35
- 35 - 65
- 65+
so I wrote a function
所以我写了一个函数
def agerange(values):
for i in values:
if complete.Age_units == 'Y':
if complete.Age > 1 AND < 18 return '2-18'
elif complete.Age > 17 AND < 35 return '18-35'
elif complete.Age > 34 AND < 65 return '35-65'
elif complete.Age > 64 return '65+'
else return '< 2'
I thought if I passed in the dataframe as a whole I would get back what I needed and then could create the column I wanted something like this:
我想如果我作为一个整体传入数据框，我会得到我需要的东西，然后可以创建我想要的列：
agedetails['age_range'] = ageRange(agedetails)
BUT when I try to run the first code to create the function I get:
但是当我尝试运行第一个代码来创建函数时，我得到：
File "<ipython-input-124-cf39c7ce66d9>", line 4
if complete.Age > 1 AND complete.Age < 18 return '2-18'
^
SyntaxError: invalid syntax
Clearly it is not accepting the AND - but I thought I heard in class I could use AND like this? I must be mistaken but then what would be the right way to do this?
显然它不接受 AND - 但我想我在课堂上听说我可以像这样使用 AND ？我一定是弄错了，但是这样做的正确方法是什么？
So after getting that error, I'm not even sure the method of passing in a dataframe will throw an error either. I am guessing probably yes. In which case - how would I make that work as well?
因此，在收到该错误后，我什至不确定传入数据帧的方法是否会引发错误。我猜可能是的。在这种情况下 - 我将如何使其工作？
I am looking to learn the best method, but part of the best method for me is keeping it simple even if that means doing things in a couple of steps...
我想学习最好的方法，但对我来说最好的方法之一就是保持简单，即使这意味着要分几个步骤做事......
回答by jpp
With Pandas, you should avoid row-wise operations, as these usually involve an inefficient Python-level loop. Here are a couple of alternatives.
使用 Pandas，您应该避免按行操作，因为这些操作通常涉及低效的 Python 级循环。这里有几个选择。
Pandas: pd.cut
Pandas： pd.cut
As @JonClements suggests, you can use pd.cutfor this, the benefit here being that your new column becomes a Categorical.
正如@JonClements 所建议的那样，您可以pd.cut为此使用它，这样做的好处是您的新列变成了Categorical。
You only need to define your boundaries (including np.inf) and category names, then apply pd.cutto the desired numeric column.
您只需要定义边界（包括np.inf）和类别名称，然后应用于pd.cut所需的数字列。
bins = [0, 2, 18, 35, 65, np.inf]
names = ['<2', '2-18', '18-35', '35-65', '65+']
df['AgeRange'] = pd.cut(df['Age'], bins, labels=names)
print(df.dtypes)
# Age int64
# Age_units object
# AgeRange category
# dtype: object
NumPy: np.digitize
NumPy： np.digitize
np.digitizeprovides another clean solution. The idea is to define your boundaries and names, create a dictionary, then apply np.digitizeto your Age column. Finally, use your dictionary to map your category names.
np.digitize提供了另一种干净的解决方案。这个想法是定义你的界限和名字，创建一个字典，然后应用np.digitize到你的年龄列。最后，使用您的字典来映射您的类别名称。
Note that for boundary cases the lower bound is used for mapping to a bin.
请注意，对于边界情况，下限用于映射到 bin。
import pandas as pd, numpy as np
df = pd.DataFrame({'Age': [99, 53, 71, 84, 84],
'Age_units': ['Y', 'Y', 'Y', 'Y', 'Y']})
bins = [0, 2, 18, 35, 65]
names = ['<2', '2-18', '18-35', '35-65', '65+']
d = dict(enumerate(names, 1))
df['AgeRange'] = np.vectorize(d.get)(np.digitize(df['Age'], bins))
Result
结果
Age Age_units AgeRange
0 99 Y 65+
1 53 Y 35-65
2 71 Y 65+
3 84 Y 65+
4 84 Y 65+
