使用qcut后，为什么我的数据值被“ NaN”替换？

编程问答 2022-07-26

问题描述

我正在使用9000行和6列的pandas数据框。在这一点上，我正在尝试将工作的连续变量“经验”年转换为四个工作（商业经理-业务开发员-专家）的专业知识的分类变量（初级-中级-高级-专家）网络营销人员-流量管理器）。

考虑到每项工作的年限范围并不相同，我使用“ qcut”将数据分为以下四个组：

（您可以运行下面的代码以获取数据帧示例）

import pandas as pd


df = pd.DataFrame({'Job': ['Commercial Manager','Traffic Manager','Web Marketer','Commercial Manager','Business Developer','Web Marketer'],'Experience': [1.00000,3.00000,1.50000,2.00000,6.00000,0.00000,4.00000,8.00000,5.00000,0.50000,3.50000]})


levels = ["beginner","intermediate","advanced","expert"]
jobs = ["Commercial Manager","Business Developer","Web Marketer","Traffic Manager"]


def convert(levels,jobs):
  for j in jobs:
    df["Level"] = pd.qcut(df.loc[df["Job"] == j,"Experience"].rank(method="first"),q = 4,labels = levels,duplicates = "drop")
  return df

convert(levels,jobs)

这是使用“ qcut”后的输出：

    Job                     Experience       Level 
0   Commercial Manager      1.00000          NaN
1   Traffic Manager         3.00000          intermediate
2   Web Marketer            3.00000          NaN
3   Commercial Manager      1.50000          NaN
4   Commercial Manager      2.00000          NaN
5   Web Marketer            6.00000          NaN
6   Commercial Manager      0.00000          NaN
7   Commercial Manager      4.00000          NaN
8   Traffic Manager         8.00000          expert
9   Business Developer      5.00000          NaN 
10  Business Developer      0.50000          NaN 
11  Web Marketer            3.00000          NaN 
12  Traffic Manager         3.00000          intermediate
13  Traffic Manager         0.00000          beginner
14  Commercial Manager      2.00000          NaN
15  Business Developer      3.00000          NaN
16  Traffic Manager         0.50000          beginner
17  Commercial Manager      3.00000          NaN
18  Business Developer      3.00000          NaN
19  Business Developer      8.00000          NaN
20  Web Marketer            3.50000          NaN

似乎它仅适用于“流量管理器”，并且用NaN替代了其他level经验。我真的迷路了。有什么帮助吗？

解决方法

您要在groupby操作中执行此操作：

import numpy
import pandas

levels = ["beginner","intermediate","advanced","expert"]
jobs = ["Commercial Manager","Business Developer","Web Marketer","Traffic Manager"]

df = pandas.DataFrame({
    'Job': numpy.random.choice(levels,size=150),'Experience': numpy.random.uniform(0.25,10.5,size=150)
}).assign(
    level=df.groupby(['Job'])['Experience'] # for each unique job...
            # apply a quantile (quartile) cut 
            .apply(lambda g: pd.qcut(g,q=4,labels=levels,duplicates="drop"))
)
, 
  # I would just change two things to what Paul suggested (jobs instead of levels and the rank(method="first") because there was still an error:

levels = ["beginner","Traffic Manager"]

df = pandas.DataFrame({
  'Job': numpy.random.choice(jobs,size=150)
}).assign(
  level=df.groupby(['Job'])['Experience'] # for each unique job...
        # apply a quantile (quartile) cut 
        .apply(lambda g: pd.qcut(g.rank(method="first"),duplicates="drop"))
)
binningdataframepandaspandaspython