如何基于另一列的值在pandas dataframe列中创建新值

Question

我有一个pandas数据帧,我从csv文件读入的值.我有一个标有'SleepQuality'的列,值从0.0到100.0浮动.我想创建一个标记为'SleepQualityGroup'的新列,其中原始列btw 0 - 49中的值在新列中的值为0,50 - 59 = 1,60 - 69 = 2,70 - 79 = 3,80 - 89 = 4,而90 - 100 = 5

为了做到这一点,最好的配方是什么？我坚持识别每个范围中所有值并分配给新值所需的逻辑.

下面在新的"SleepQualityGroup"列中输出结果的示例.

SleepQuality    SleepQualityGroup
80.4              4
90.1              5
66.4              2
50.3              1
86.2              4
75.4              3
45.7              0
91.5              5
61.3              2 
54                1
58.2              1

Answer 1

使用pd.cutie

df['new'] = pd.cut(df['SleepQuality'],bins=[0,50 , 60, 70 , 80 , 90,100], labels=[0,1,2,3,4,5])

输出:

        SleepQuality  SleepQualityGroup new
0           80.4                  4   4
1           90.1                  5   5
2           66.4                  2   2
3           50.3                  1   1
4           86.2                  4   4
5           75.4                  3   3
6           45.7                  0   0
7           91.5                  5   5
8           61.3                  2   2
9           54.0                  1   1
10          58.2                  1   1

Answer 2

这基本上是一个分箱操作.因此可以在这里使用这两种工具.

使用np.searchsorted-

bins = np.arange(50,100,10)
df['SleepQualityGroup'] = bins.searchsorted(df.SleepQuality)

使用np.digitize-

df['SleepQualityGroup'] = np.digitize(df.SleepQuality, bins)

样品输出 -

In [866]: df
Out[866]: 
    SleepQuality  SleepQualityGroup
0           80.4                  4
1           90.1                  5
2           66.4                  2
3           50.3                  1
4           86.2                  4
5           75.4                  3
6           45.7                  0
7           91.5                  5
8           61.3                  2
9           54.0                  1
10          58.2                  1

运行时测试 -

In [921]: df
Out[921]: 
    SleepQuality  SleepQualityGroup
0           80.4                  4
1           90.1                  5
2           66.4                  2
3           50.3                  1
4           86.2                  4
5           75.4                  3
6           45.7                  0
7           91.5                  5
8           61.3                  2
9           54.0                  1
10          58.2                  1

In [922]: df = pd.concat([df]*10000,axis=0)

# @Dark's soln using pd.cut
In [923]: %timeit df['new'] = pd.cut(df['SleepQuality'],bins=[0,50 , 60, 70 , 80 , 90,100], labels=[0,1,2,3,4,5])
1000 loops, best of 3: 1.04 ms per loop

In [926]: %timeit df['SleepQualityGroup'] = bins.searchsorted(df.SleepQuality)
1000 loops, best of 3: 591 µs per loop

In [927]: %timeit df['SleepQualityGroup'] = np.digitize(df.SleepQuality, bins)
1000 loops, best of 3: 538 µs per loop