为什么使用 qcut 后我的数据值被替换为“NaN”？

Question

我正在使用 9000 行和 6 列的 pandas 数据框。此时，我正在尝试将 4 种工作（商业经理 - 业务开发人员 -网络营销人员 - 流量经理）。

鉴于每个工作的年经验范围并不相同，我使用“qcut”将数据分为四组，如下所示：

（您可以运行下面的代码来获取数据框示例）

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 

看来它只适用于“流量管理器”，并且level用 NaN 替换了其他体验。我真的迷失了。有什么帮助吗？

Answer 1

您想在 groupby 操作中执行此操作：import numpyimport pandaslevels = ["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")))