为什么在使用 qcut 后我的数据值被“NaN”替换了？

Question

我正在使用 9000 行和 6 列的熊猫数据框。 在这一点上，我正在尝试将工作的连续变量“经验”年数转换为 4 个工作（商业经理 - 业务开发人员 -网络营销人员 - 流量管理器）。

鉴于每个工作的年经验范围不一样，我使用“qcut”将数据分为4组，如下所示：

（您可以运行下面的代码来获取数据帧示例）

import pandas as pd


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

看来它只适用于“交通管理器”，它用 NaN 取代了其他level经验。 我真的很失落。 请问有什么帮助吗？

Answer 1

您想在 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"))
)

Answer 2

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

df = pandas.DataFrame({
  'Job': numpy.random.choice(jobs, 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.rank(method="first"), q=4, labels=levels, duplicates="drop"))
)