Pandas：如何创建一个运行计数列？

Question

我有一个平面文本文件，格式为（我添加的第 headers 列）

CASE        Diagnosis
  S1 no diagnosis
  S2 fungus
     squamous lesion
  S3 fungus
  S4 squamous lesion
     glandular lesion
     atypia

我想堆叠和取消堆叠多个诊断的病例，所以我想

CASE DxN         Diagnosis
  S1 A   no diagnosis
  S2 A   fungus   
     B   squamous lesion
  S3 A   fungus
  S4 A   squamous lesion
     B   glandular lesion
     C   atypia

和

CASE                 A                 B       C
  S1 no diagnosis
  S2 fungus             squamous lesion
  S3 fungus
  S4 squamous lesion    glandular lesion  atypia

如何制作该子系列 DxN？计数不应大于 F。即使有 10,000 个可能的答案，每个案例也不会超过 6 个，因此不超过 6 列。我只想要 "What is diagnosis A for case S1, what's diagnosis B for case S1, what's diagnosis 3 for case S1?" 我不想为每个可能的答案都设置一个专栏。

Answer 1

您可以创建一个包含每个病例运行总诊断数的列。有关详细信息，请参阅此 post：SQL-like window functions in PANDAS: Row Numbering in Python Pandas Dataframe

使用此样本数据：

df = pd.DataFrame([
    {'Case': 'S1', 'Diagnosis': 'no diagnosis'},
    {'Case': 'S2', 'Diagnosis': 'fungus'},
    {'Case': 'S2', 'Diagnosis': 'squamous lesion'}
])

此脚本将为您提供运行总数：

df['DxN'] = df.sort_values(['Case'], ascending=[1]).groupby('Case').cumcount() + 1

Answer 2

这是您需要的吗？

    df=df.replace('',np.nan).ffill()
    df.assign(DxN=df.groupby('CASE').cumcount()).set_index(['CASE','DxN']).Diagnosis.unstack(fill_value='')
    Out[709]: 
    DxN                0                1
    CASE                                 
    S1       nodiagnosis                 
    S2            fungus   squamouslesion
    S3            fungus                 
    S4    squamouslesion  glandularlesion

Answer 3

这是一种方法，从您拥有的文本格式的数据开始：

import pandas as pd
import numpy as np

df = pd.DataFrame([['S1', 'no diagnosis'],
                   ['S2', 'fungus'],
                   ['', 'squamous lesion'],
                   ['S3', 'fungus'],
                   ['S4', 'squamous lesion'],
                   ['', 'glandular lesion']],
                  columns=['CASE', 'Diagnosis'])

# front fill CASE series
df.CASE = df.CASE.replace('', np.nan).ffill()

# pivot data
df = pd.pivot_table(df, index=['CASE'], values=['Diagnosis'],
                    aggfunc=lambda x: list(x)).reset_index()

# split columns of lists into separate columns
df = pd.concat([df[['CASE']], pd.DataFrame(df['Diagnosis'].values.tolist())], axis=1)

#   CASE                0                 1
# 0   S1     no diagnosis              None
# 1   S2           fungus   squamous lesion
# 2   S3           fungus              None
# 3   S4  squamous lesion  glandular lesion

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