如何将elif与数据帧结合使用

你可以用 np.select()

df["Category"] = np.select([df.Sales>=300, df.Sales<200], 
                           ["Fast Movers", "Slow Movers"], 
                           default="Medium Fast Movers")

将lambda函数(或命名函数)与 apply

df.Category = df.Sales.apply(
    lambda x: 'Fast Movers' if x >=300 else ('Medium Fast Movers' if x >= 200 else 'Slow Movers')
)

命名函数可以这样定义,而不是lambda:

def move_speed(x):
    if x >= 300:
        return 'Fast Movers'
    if x >= 200:
        return 'Medium Fast Movers'
    return 'Slow Movers'

或者,可以使用 loc

df.loc[df.Sales > 300, 'Category'] = 'Fast Movers'
df.loc[df.Sales < 200, 'Category'] = 'Slow Movers'
df.Category.fillna('Medium Fast Movers', inplace=True)

两者都会产生这种数据框架:

  Company  Sales            Category
0      MC  360.0         Fast Movers
1      MC  340.0         Fast Movers
2      MC  338.5         Fast Movers
3      MC  335.5         Fast Movers
4      MC  235.0  Medium Fast Movers
5      MC  235.0  Medium Fast Movers
6      MC  234.0  Medium Fast Movers
7      MC  127.0         Slow Movers
8      MC  121.0         Slow Movers
9      MC  120.5         Slow Movers

下面是一个使用第二个数据帧和 pd.IntervalIndex 。如果你有大量的范围,这可以让你的代码更干净。

安装程序

ranges = pd.DataFrame({
    'START': [0, 200, 300],
    'STOP': [200, 300, float('inf')],
    'CLASS': ['Slow Workers', 'Medium Workers', 'Fast Workers']
})

#                          CLASS  START        STOP
# [0.0, 200.0)      Slow Workers      0  200.000000
# [200.0, 300.0)  Medium Workers    200  300.000000
# [300.0, inf)      Fast Workers    300         inf

ranges.index = pd.IntervalIndex.from_arrays(ranges.START, ranges.STOP, closed='left')

map

df['Category'] = df.Sales.map(ranges.CLASS)

# Result

  Company  Sales        Category
0      MC  360.0    Fast Workers
1      MC  340.0    Fast Workers
2      MC  338.5    Fast Workers
3      MC  335.5    Fast Workers
4      MC  235.0  Medium Workers
5      MC  235.0  Medium Workers
6      MC  234.0  Medium Workers
7      MC  127.0    Slow Workers
8      MC  121.0    Slow Workers
9      MC  120.5    Slow Workers