Python Pandas：categorize/bin 按零值的数字分组

Question

我不确定这是否是最有效的方式，但我正在努力将客户支出分组到 bins/buckets。

这是我正在处理的 df:

df.head()

Best_ID_S| Dollar
abc2464    0.00 
fdhg357    672.00  
hjg5235    250.00 
mjhur57    199.00 
erew3452   116.25

这是我的代码：

bins = [0,250,500,750,1000,1500,2000,2500,3000,3500,4000,4500,5000,5500,6000,6500,7000,8000,1000000000000]
#I didn't know how to create 8000+ so I just added a crazy number in the end, it works

group_names = ['0-250','251-500','501-749','750-999','1000-1499','1500-1999','2000-2499','2500-2999','3000-3499','3500-3999','4000-4499','4500-4999','5000-5499','5500-5999','6000-6499','6500-6999','7000-7499','8000+']

categories = pd.cut(df_2014['Dollar'], bins, labels=group_names)
df['Category'] = pd.cut(df['Dollar'], bins, labels=group_names)
df['Buckets'] = pd.cut(df['Dollar'], bins)

这就是我得到的，当我做 df.head():

Best_ID_S| Dollar | Category |  Buckets
abc2464    0.00     NaN
fdhg357    672.00   501-749        (500, 750]
hjg5235    250.00   0-250          (0, 250]
mjhur57    199.00   0-250          (0, 250]
erew3452   116.25   0-250          (0, 250]

如果美元价值为 0，我需要它是 0-250 的桶。但我得到了 NaN。

Answer 1

right参数的默认值为真。数学上 ( 表示排除左边的，所以需要 [ 来包含左边的值。所以将 pd.cut 更改为

df['Category'] = pd.cut(df['Dollar'], bins, labels=group_names,right=False)
df['Buckets'] = pd.cut(df['Dollar'], bins,right=False)

 Best_ID_S|  Dollar Category     Buckets
0    abc2464    0.00    0-250    [0, 250)
1    fdhg357  672.00  501-749  [500, 750)
2    hjg5235  250.00  251-500  [250, 500)
3    mjhur57  199.00    0-250    [0, 250)
4   erew3452  116.25    0-250    [0, 250)

Incase 使其左包含，您还可以通过保留右参数 True 将 include_lowest 设置为 True。

Answer 2

要创建 8000 以上的 bin，您可以将最后一个 bin 用作 np.inf

bins = [0,250,500,750,1000,1500,2000,2500,3000,3500,4000,4500,5000,5500,6000,6500,7000,8000,np.inf]

为了包括下限，您可以使用参数 include_lowest = True

df['Category'] = pd.cut(df['Dollar'], bins, labels=group_names, include_lowest=True)
df['Buckets'] = pd.cut(df['Dollar'], bins, include_lowest=True)

你得到

    Best_ID_S   Dollar  Category    Buckets
0   abc2464     0.00    0-250   [0, 250]
1   fdhg357     672.00  501-749 (500, 750]
2   hjg5235     250.00  0-250   [0, 250]
3   mjhur57     199.00  0-250   [0, 250]
4   erew3452    116.25  0-250   [0, 250]

Python Pandas：categorize/bin 按零值的数字分组

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