列表推导式是有条件地创建另一列的另一种方法。如果您在列中使用对象dtype，就像您的示例一样，列表推导式通常优于大多数其他方法。

示例列表理解:

df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]

%时间它测试：

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
%timeit df['color'] = ['red' if x == 'Z' else 'green' for x in df['Set']]
%timeit df['color'] = np.where(df['Set']=='Z', 'green', 'red')
%timeit df['color'] = df.Set.map( lambda x: 'red' if x == 'Z' else 'green')

1000 loops, best of 3: 239 µs per loop
1000 loops, best of 3: 523 µs per loop
1000 loops, best of 3: 263 µs per loop

如果你只有两种选择:

df['color'] = np.where(df['Set']=='Z', 'green', 'red')

例如,

import pandas as pd
import numpy as np

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
df['color'] = np.where(df['Set']=='Z', 'green', 'red')
print(df)

收益率

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

如果你有两个以上的条件，那么使用np.select。例如，如果你想要颜色

黄色时(df['设置']= = ' Z ') & (df(“类型”)= =“一”) 否则蓝色当(df['设置']= = ' Z ') & (df(“类型”)= = ' B ') 否则为紫色，当(df['Type'] == 'B') 否则黑,

然后使用

df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')})
conditions = [
    (df['Set'] == 'Z') & (df['Type'] == 'A'),
    (df['Set'] == 'Z') & (df['Type'] == 'B'),
    (df['Type'] == 'B')]
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
print(df)

的收益率

  Set Type   color
0   Z    A  yellow
1   Z    B    blue
2   X    B  purple
3   Y    C   black

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。

另一种实现这一目标的方法是

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

pyjanitor中的case_when函数是pd.Series.mask的包装器，并为多种条件提供了可链接/方便的形式:

对于单一条件:

df.case_when(
    df.col1 == "Z",  # condition
    "green",         # value if True
    "red",           # value if False
    column_name = "color"
    )

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

适用于多种情况:

df.case_when(
    df.Set.eq('Z') & df.Type.eq('A'), 'yellow', # condition, result
    df.Set.eq('Z') & df.Type.eq('B'), 'blue',   # condition, result
    df.Type.eq('B'), 'purple',                  # condition, result
    'black',              # default if none of the conditions evaluate to True
    column_name = 'color'  
)
  Type  Set   color
1    A   Z  yellow
2    B   Z    blue
3    B   X  purple
4    C   Y   black

更多的例子可以在这里找到

使用.apply()方法的一行代码如下:

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

之后，df数据帧是这样的:

>>> print(df)
  Type Set  color
0    A   Z  green
1    B   Z  green
2    B   X    red
3    C   Y    red