python - 在DataFrame中添加计算字段

Question

我想创建一个名为 str_bos 的列在现有的DataFrame中叫result 。我有以下列 - 'str_nbr', 'ZIP Sales', 'str_Sales', 'ZIP_Distinct #', 'ZIP_Share_of_Str_Sales', 'Counter', 'Str_BOS_Cum%', 'Str_Sales_Rank' 。

这是我的想法。但是，需要2个小时才能完成。然而，其他操作(如排序、合并等)需要几秒钟的时间。我在这里缺少什么？

def str_bos(row):
    if row['str_sales_rank'] == 1 or row['str_bos_cum%'] <= 0.1:
        return 1
    elif row['str_bos_cum%'] <= 0.2:
        return 2
    elif row['str_bos_cum%'] <= 0.3:
        return 3
    elif row['str_bos_cum%'] <= 0.4:
        return 4
    elif row['str_bos_cum%'] <= 0.5:
        return 5
    elif row['str_bos_cum%'] <= 0.6:
        return 6
    elif row['str_bos_cum%'] <= 0.7:
        return 7
    elif row['str_bos_cum%'] <= 0.8:
        return 8
    elif row['str_bos_cum%'] <= 0.9:
        return 9
    else:
        return 10

result['str_bos'] = result.apply(lambda row: str_bos(row), axis=1)

Answer 1

我会使用cut()方法在这里:

In [21]: df = pd.DataFrame(np.random.rand(10), columns=['A'])

In [22]: df
Out[22]:
          A
0  0.513425
1  0.973631
2  0.549615
3  0.747600
4  0.099415
5  0.737613
6  0.885567
7  0.720187
8  0.446683
9  0.434688

In [23]: df['str_bos'] = pd.cut(df.A, bins=np.arange(0, 1.1, 0.1), labels=np.arange(10)+1)

In [24]: df
Out[24]:
          A str_bos
0  0.513425       6
1  0.973631      10
2  0.549615       6
3  0.747600       8
4  0.099415       1
5  0.737613       8
6  0.885567       9
7  0.720187       8
8  0.446683       5
9  0.434688       5