如果只有两个选择，请使用np.where()

df = pd.DataFrame({'A':range(3)})
df['B'] = np.where(df.A>2, 'yes', 'no')

如果你有超过2个选择，也许apply()可以工作 输入

arr = pd.DataFrame({'A':list('abc'), 'B':range(3), 'C':range(3,6), 'D':range(6, 9)})

arr是

    A   B   C   D
0   a   0   3   6
1   b   1   4   7
2   c   2   5   8

如果你想让列E等于arr。A ==' A '然后arr。B elif arr。A=='b' then arr. c elif arr。A == 'c'则arr。解析:选D

arr['E'] = arr.apply(lambda x: x['B'] if x['A']=='a' else(x['C'] if x['A']=='b' else(x['D'] if x['A']=='c' else 1234)), axis=1)

最后是arr

    A   B   C   D   E
0   a   0   3   6   0
1   b   1   4   7   4
2   c   2   5   8   8

如果你在处理海量数据，记忆方法是最好的:

# First create a dictionary of manually stored values
color_dict = {'Z':'red'}

# Second, build a dictionary of "other" values
color_dict_other = {x:'green' for x in df['Set'].unique() if x not in color_dict.keys()}

# Next, merge the two
color_dict.update(color_dict_other)

# Finally, map it to your column
df['color'] = df['Set'].map(color_dict)

当您有许多重复的值时，这种方法将是最快的。我的一般经验法则是记住data_size > 10**4 & n_distinct < data_size/4

在一种情况下，记忆10,000行，不同值不超过2,500。

当你有一个或几个条件时，可以使用下面的简单语句:

df['color'] = np.select(condlist=[df['Set']=="Z", df['Set']=="Y"], choicelist=["green", "yellow"], default="red")

容易，很好去!

更多信息请访问:https://numpy.org/doc/stable/reference/generated/numpy.select.html

下面的方法比这里计时的方法慢，但是我们可以基于多个列的内容计算额外的列，并且可以为额外的列计算两个以上的值。

使用“Set”列的简单示例:

def set_color(row):
    if row["Set"] == "Z":
        return "red"
    else:
        return "green"

df = df.assign(color=df.apply(set_color, axis=1))

print(df)

  Set Type  color
0   Z    A    red
1   Z    B    red
2   X    B  green
3   Y    C  green

考虑到更多颜色和更多列的例子:

def set_color(row):
    if row["Set"] == "Z":
        return "red"
    elif row["Type"] == "C":
        return "blue"
    else:
        return "green"

df = df.assign(color=df.apply(set_color, axis=1))

print(df)

  Set Type  color
0   Z    A    red
1   Z    B    red
2   X    B  green
3   Y    C   blue

编辑(21/06/2019):使用plydata

也可以使用plydata来做这类事情(不过，这似乎比使用assign和apply还要慢)。

from plydata import define, if_else

简单的if_else:

df = define(df, color=if_else('Set=="Z"', '"red"', '"green"'))

print(df)

  Set Type  color
0   Z    A    red
1   Z    B    red
2   X    B  green
3   Y    C  green

嵌套if_else:

df = define(df, color=if_else(
    'Set=="Z"',
    '"red"',
    if_else('Type=="C"', '"green"', '"blue"')))

print(df)                            

  Set Type  color
0   Z    A    red
1   Z    B    red
2   X    B   blue
3   Y    C  green

你可以使用pandas方法:

df['color'] = 'green'
df['color'] = df['color'].where(df['Set']=='Z', other='red')
# Replace values where the condition is False

or

df['color'] = 'red'
df['color'] = df['color'].mask(df['Set']=='Z', other='green')
# Replace values where the condition is True

或者，你也可以使用lambda函数的transform方法:

df['color'] = df['Set'].transform(lambda x: 'green' if x == 'Z' else 'red')

输出:

  Type Set  color
1    A   Z  green
2    B   Z  green
3    B   X    red
4    C   Y    red

@chai的性能比较:

import pandas as pd
import numpy as np
df = pd.DataFrame({'Type':list('ABBC')*1000000, 'Set':list('ZZXY')*1000000})
 
%timeit df['color1'] = 'red'; df['color1'].where(df['Set']=='Z','green')
%timeit df['color2'] = ['red' if x == 'Z' else 'green' for x in df['Set']]
%timeit df['color3'] = np.where(df['Set']=='Z', 'red', 'green')
%timeit df['color4'] = df.Set.map(lambda x: 'red' if x == 'Z' else 'green')

397 ms ± 101 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
976 ms ± 241 ms per loop
673 ms ± 139 ms per loop
796 ms ± 182 ms per loop

一个使用np.select的更简洁的方法:

a = np.array([['A','Z'],['B','Z'],['B','X'],['C','Y']])
df = pd.DataFrame(a,columns=['Type','Set'])

conditions = [
    df['Set'] == 'Z'
]

outputs = [
    'Green'
    ]
             # conditions Z is Green, Red Otherwise.
res = np.select(conditions, outputs, 'Red')
res 
array(['Green', 'Green', 'Red', 'Red'], dtype='<U5')
df.insert(2, 'new_column',res)    

df
    Type    Set new_column
0   A   Z   Green
1   B   Z   Green
2   B   X   Red
3   C   Y   Red

df.to_numpy()    
    
array([['A', 'Z', 'Green'],
       ['B', 'Z', 'Green'],
       ['B', 'X', 'Red'],
       ['C', 'Y', 'Red']], dtype=object)

%%timeit conditions = [df['Set'] == 'Z'] 
outputs = ['Green'] 
np.select(conditions, outputs, 'Red')

134 µs ± 9.71 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

df2 = pd.DataFrame({'Type':list('ABBC')*1000000, 'Set':list('ZZXY')*1000000})
%%timeit conditions = [df2['Set'] == 'Z'] 
outputs = ['Green'] 
np.select(conditions, outputs, 'Red')

188 ms ± 26.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)