如何将数字数据映射到Pandas数据框中的类别/箱中 [英] How to map numeric data into categories / bins in Pandas dataframe
问题描述
我刚刚开始使用python进行编码，我的一般编码技能还很生锈:(所以请耐心等待
I've just started coding in python, and my general coding skills are fairly rusty :( so please be a bit patient
我有一个熊猫数据框:
它大约有300万行.年龄单位分为3种:Y，D，W(年)，Days&周.任何1岁以上的人的年龄单位均为Y，而我想要的第一个分组年龄为< 2岁，因此我要测试的所有年龄单位均为Y ...
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...
我想创建一个新列AgeRange并填充以下范围:
I want to create a new column AgeRange and populate with the following ranges:
- < 2
- 2-18
- 18-35
- 35-65
- 65岁以上
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'
我想，如果我将数据帧作为一个整体传递，我会得到我需要的东西，然后可以创建我想要的列:
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:
agedetails['age_range'] = ageRange(agedetails)
但是，当我尝试运行第一个代码来创建我得到的功能时:
BUT when I try to run the first code to create the function I get:
File "<ipython-input-124-cf39c7ce66d9>", line 4
if complete.Age > 1 AND complete.Age < 18 return '2-18'
^
SyntaxError: invalid syntax
很明显，它不接受AND-但我想我在课堂上听说可以使用AND了吗?我一定会弄错，但是这样做的正确方法是什么?
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?
因此，在收到该错误之后，我什至不确定传入数据帧的方法是否也会引发错误.我猜大概是.在这种情况下-我也将如何使它正常工作?
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?
我希望学习最好的方法，但是对我来说，最好的方法的一部分就是保持简单，即使那意味着要分几步做事...
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...
推荐答案
对于Pandas，您应该避免按行操作，因为这些操作通常涉及效率低下的Python级循环.这有两种选择.
With Pandas, you should avoid row-wise operations, as these usually involve an inefficient Python-level loop. Here are a couple of alternatives.
正如@JonClements所建议的那样，您可以使用pd.cut，这样做的好处是您的新列将成为
As @JonClements suggests, you can use pd.cut for this, the benefit here being that your new column becomes a Categorical.
您只需要定义边界(包括np.inf)和类别名称，然后将pd.cut应用于所需的数字列即可.
You only need to define your boundaries (including np.inf) and category names, then apply pd.cut to the desired numeric column.
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
np.digitize提供了另一种干净的解决方案.想法是定义边界和名称，创建字典，然后将np.digitize应用于年龄"列.最后，使用您的字典来映射类别名称.
NumPy: np.digitize
np.digitize provides another clean solution. The idea is to define your boundaries and names, create a dictionary, then apply np.digitize to your Age column. Finally, use your dictionary to map your category names.
请注意，在边界情况下，下限用于映射到bin.
Note that for boundary cases the lower bound is used for mapping to a 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))
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+
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