使用条件在 pandas 数据框中生成新列 [英] Using conditional to generate new column in pandas dataframe

问题描述

我有一个看起来像这样的熊猫数据框:

I have a pandas dataframe that looks like this:

   portion  used
0        1   1.0
1        2   0.3
2        3   0.0
3        4   0.8

我想基于 used 列创建一个新列，以便 df 看起来像这样:

I'd like to create a new column based on the used column, so that the df looks like this:

   portion  used    alert
0        1   1.0     Full
1        2   0.3  Partial
2        3   0.0    Empty
3        4   0.8  Partial

• 根据
创建一个新的alert列
• 如果used是1.0，alert应该是Full.
• 如果 used 为 0.0，则 alert 应为 Empty.
• 否则，alert 应该是 Partial.
• Create a new alert column based on
• If used is 1.0, alert should be Full.
• If used is 0.0, alert should be Empty.
• Otherwise, alert should be Partial.

最好的方法是什么?

推荐答案

你可以定义一个函数来返回你的不同状态Full"、Partial"、Empty"等，然后使用 df.apply将函数应用于每一行.请注意，您必须传递关键字参数 axis=1 以确保它将函数应用于行.

You can define a function which returns your different states "Full", "Partial", "Empty", etc and then use df.apply to apply the function to each row. Note that you have to pass the keyword argument axis=1 to ensure that it applies the function to rows.

import pandas as pd

def alert(row):
  if row['used'] == 1.0:
    return 'Full'
  elif row['used'] == 0.0:
    return 'Empty'
  elif 0.0 < row['used'] < 1.0:
    return 'Partial'
  else:
    return 'Undefined'

df = pd.DataFrame(data={'portion':[1, 2, 3, 4], 'used':[1.0, 0.3, 0.0, 0.8]})

df['alert'] = df.apply(alert, axis=1)

#    portion  used    alert
# 0        1   1.0     Full
# 1        2   0.3  Partial
# 2        3   0.0    Empty
# 3        4   0.8  Partial

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