在带有多个 if 语句的 Pandas Lambda 函数中使用 Apply
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Using Apply in Pandas Lambda functions with multiple if statements
提问by abutremutante
I'm trying to infer a classification according to the size of a person in a dataframe like this one:
我正在尝试根据数据框中人的大小来推断分类，如下所示：
Size
1 80000
2 8000000
3 8000000000
...
I want it to look like this:
我希望它看起来像这样：
Size Classification
1 80000 <1m
2 8000000 1-10m
3 8000000000 >1bi
...
I understand that the ideal process would be to apply a lambda function like this:
我知道理想的过程是应用这样的 lambda 函数：
df['Classification']=df['Size'].apply(lambda x: "<1m" if x<1000000 else "1-10m" if 1000000<x<10000000 else ...)
I checked a few posts regarding multiple ifs in a lambda function, here is an example link, but that synthax is not working for me for some reason in a multiple ifs statement, but it was working in a single if condition.
我检查了一些关于 lambda 函数中多个 ifs 的帖子，这里是一个示例链接，但是由于某种原因，在多个 ifs 语句中 synthax 对我不起作用，但它在单个 if 条件下工作。
So I tried this "very elegant" solution:
所以我尝试了这个“非常优雅”的解决方案：
df['Classification']=df['Size'].apply(lambda x: "<1m" if x<1000000 else pass)
df['Classification']=df['Size'].apply(lambda x: "1-10m" if 1000000 < x < 10000000 else pass)
df['Classification']=df['Size'].apply(lambda x: "10-50m" if 10000000 < x < 50000000 else pass)
df['Classification']=df['Size'].apply(lambda x: "50-100m" if 50000000 < x < 100000000 else pass)
df['Classification']=df['Size'].apply(lambda x: "100-500m" if 100000000 < x < 500000000 else pass)
df['Classification']=df['Size'].apply(lambda x: "500m-1bi" if 500000000 < x < 1000000000 else pass)
df['Classification']=df['Size'].apply(lambda x: ">1bi" if 1000000000 < x else pass)
Works out that "pass" seems not to apply to lambda functions as well:
计算出“pass”似乎也不适用于 lambda 函数：
df['Classification']=df['Size'].apply(lambda x: "<1m" if x<1000000 else pass)
SyntaxError: invalid syntax
Any suggestions on the correct synthax for a multiple if statement inside a lambda function in an apply method in Pandas? Either multi-line or single line solutions work for me.
对于 Pandas 中的 apply 方法中 lambda 函数内的多个 if 语句的正确合成器有什么建议吗？多线或单线解决方案都适合我。
回答by Anton vBR
Here is a small example that you can build upon:
这是一个小示例，您可以在此基础上进行构建：
Basically, lambda x: x..is the short one-liner of a function. What apply really asks for is a function which you can easily recreate yourself.
基本上，lambda x: x..是一个函数的短单行。apply 真正需要的是一个您可以轻松地重新创建自己的功能。
import pandas as pd
# Recreate the dataframe
data = dict(Size=[80000,8000000,800000000])
df = pd.DataFrame(data)
# Create a function that returns desired values
# You only need to check upper bound as the next elif-statement will catch the value
def func(x):
if x < 1e6:
return "<1m"
elif x < 1e7:
return "1-10m"
elif x < 5e7:
return "10-50m"
else:
return 'N/A'
# Add elif statements....
df['Classification'] = df['Size'].apply(func)
print(df)
Returns:
返回：
Size Classification
0 80000 <1m
1 8000000 1-10m
2 800000000 N/A
回答by MaxU
You can use pd.cutfunction:
您可以使用pd.cut功能：
bins = [0, 1000000, 10000000, 50000000, ...]
labels = ['<1m','1-10m','10-50m', ...]
df['Classification'] = pd.cut(df['Size'], bins=bins, labels=labels)
回答by piRSquared
Using Numpy's searchsorted
使用 Numpy searchsorted
labels = np.array(['<1m', '1-10m', '10-50m', '>50m'])
bins = np.array([1E6, 1E7, 5E7])
# Using assign is my preference as it produces a copy of df with new column
df.assign(Classification=labels[bins.searchsorted(df['Size'].values)])
If you wanted to produce new column in existing dataframe
如果您想在现有数据框中生成新列
df['Classification'] = labels[bins.searchsorted(df['Size'].values)]
Some Explanation
一些解释
From Docs:np.searchsorted
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array a such that, if the corresponding elements in v were inserted before the indices, the order of a would be preserved.
查找应插入元素以保持顺序的索引。
找到排序数组 a 中的索引，如果 v 中的相应元素插入在索引之前，则将保留 a 的顺序。
The labelsarray has a length greater than that of binsby one. Because when something is greater than the maximum value in bins, searchsortedreturns a -1. When we slice labelsthis grabs the last label.
该labels数组的长度比 的长度大一bins。因为当某物大于 中的最大值时bins，searchsorted返回 a -1。当我们切片时，labels它会抓取最后一个标签。
