使用多个Pandas列应用函数？[重复]

Question

我需要在我的熊猫数据框中制作一个列，它依赖于同一行中的其他项目。例如，这是我的数据框。

    df = pd.DataFrame(
        [['a',],['a',1],['a',1],['a',2],['b',2],['b',2],['c',3]],
        columns=['letter','number']
    )

   letters  numbers
 0    a     1
 1    a     1
 2    a     1
 3    a     2
 4    b     2
 5    b     2
 6    c     3

I need a third column, that is 1 if 'a' and 2 are present in the row, and 0 otherwise. So it would be [`0,0,0,1,0,0,0]`

How can I use Pandas `apply` or `map` to do this? Iterating over the rows is my first thought, but this seems like a clumsy way of doing it.

Answer 1

您可以尝试使用pd.Series.where（）/np.where（）。如果您只对布尔值的int表示感兴趣，可以选择其他解决方案。如果希望If/else值有更多的自由度，可以使用np.where（）

import pandas as pd
import numpy as np

# create example
values = ['a', 'b', 'c']
df = pd.DataFrame()
df['letter'] = np.random.choice(values, size=10)
df['number'] = np.random.randint(1,3, size=10)

# condition
df['result'] = np.where((df['letter'] == 'a') & (df['number'] == 2), 1, 0)

Answer 2

您可以使用应用与轴=1。假设您想调用新列c：

df['c'] = df.apply(
    lambda row: (row['letter'] == 'a') and (row['number'] == 2),
    axis=1
).astype(int)

print(df)
#  letter  number  c
#0      a     NaN  0
#1      a     1.0  0
#2      a     1.0  0
#3      a     2.0  1
#4      b     2.0  0
#5      b     2.0  0
#6      c     3.0  0

但是apply很慢，如果可能的话应该避免。在这种情况下，最好使用向量化的布尔逻辑运算。

df['c'] = ((df['letter'] == "a") & (df['number'] == 2)).astype(int)

这与上面使用apply的结果相同。