Python pandas.cut
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Python pandas.cut
提问by j riot
Edit: Added defT
编辑：添加 defT
Does using pandas.cutchange the structure of a pandas.DataFrame.
using 是否会pandas.cut更改pandas.DataFrame.
I am using pandas.cutin the following manner to map single age years to age groups and then aggregating afterwards. However, the aggregation does not work as I end up with NaNin all columns that are being aggregated. Here is my code:
我使用pandas.cut以下方式将单个年龄年份映射到年龄组，然后进行聚合。但是，聚合不起作用，因为我最终NaN在所有被聚合的列中。这是我的代码：
cutoff = numpy.hstack([numpy.array(defT.MinAge[0]), defT.MaxAge.values])
labels = defT.AgeGrp
df['ageGrp'] = pandas.cut(df.Age,
bins = cutoff,
labels = labels,
include_lowest = True)
Here is defT:
这是defT：
AgeGrp MaxAge MinAge
1 18 14
2 21 19
3 24 22
4 34 25
5 44 35
6 54 45
7 65 55
Then I pass the data-frame into another function to aggregate:
然后我将数据帧传递给另一个函数进行聚合：
grouped = df.groupby(['Year', 'Month', 'OccID', 'ageGrp', 'Sex', \
'Race', 'Hisp', 'Educ'],
as_index = False)
final = grouped.aggregate(numpy.sum)
If I change the ages to age groups via this manner it works perfectly:
如果我通过这种方式将年龄更改为年龄组，它可以完美地工作：
df['ageGrp'] = 1
df.ix[(df.Age >= 14) & (df.Age <= 18), 'ageGrp'] = 1 # Age 16 - 20
df.ix[(df.Age >= 19) & (df.Age <= 21), 'ageGrp'] = 2 # Age 21 - 25
df.ix[(df.Age >= 22) & (df.Age <= 24), 'ageGrp'] = 3 # Age 26 - 44
df.ix[(df.Age >= 25) & (df.Age <= 34), 'ageGrp'] = 4 # Age 45 - 64
df.ix[(df.Age >= 35) & (df.Age <= 44), 'ageGrp'] = 5 # Age 64 - 85
df.ix[(df.Age >= 45) & (df.Age <= 54), 'ageGrp'] = 6 # Age 64 - 85
df.ix[(df.Age >= 55) & (df.Age <= 64), 'ageGrp'] = 7 # Age 64 - 85
df.ix[df.Age >= 65, 'ageGrp'] = 8 # Age 85+
I would prefer to do this on the fly, importing the definition table and using pandas.cut, instead of being hard-coded.
我更愿意即时执行此操作，导入定义表并使用pandas.cut，而不是进行硬编码。
Thank you in advance.
先感谢您。
采纳答案by unutbu
Here is, perhaps, a work-around.
这也许是一种解决方法。
Consider the following example which replicates the symptom you describe:
考虑以下示例，该示例复制了您描述的症状：
import numpy as np
import pandas as pd
np.random.seed(2015)
defT = pd.DataFrame({'AgeGrp': [1, 2, 3, 4, 5, 6, 7],
'MaxAge': [18, 21, 24, 34, 44, 54, 65],
'MinAge': [14, 19, 22, 25, 35, 45, 55]})
cutoff = np.hstack([np.array(defT['MinAge'][0]), defT['MaxAge'].values])
labels = defT['AgeGrp']
N = 50
df = pd.DataFrame(np.random.randint(100, size=(N,2)), columns=['Age', 'Year'])
df['ageGrp'] = pd.cut(df['Age'], bins=cutoff, labels=labels, include_lowest=True)
grouped = df.groupby(['Year', 'ageGrp'], as_index=False)
final = grouped.agg(np.sum)
print(final)
# Year ageGrp Age
# Year ageGrp
# 3 1 NaN NaN NaN
# 2 NaN NaN NaN
# ...
# 97 1 NaN NaN NaN
# 2 NaN NaN NaN
# [294 rows x 3 columns]
If we change
如果我们改变
grouped = df.groupby(['Year', 'ageGrp'], as_index=False)
final = grouped.agg(np.sum)
to
到
grouped = df.groupby(['Year', 'ageGrp'], as_index=True)
final = grouped.agg(np.sum).dropna()
print(final)
then we obtain:
然后我们得到：
Age
Year ageGrp
6 7 61
16 4 32
18 1 34
25 3 23
28 5 39
34 7 60
35 5 42
38 4 25
40 2 19
53 7 59
56 4 25
5 35
66 6 54
67 7 55
70 7 56
73 6 51
80 5 36
81 6 46
85 5 38
90 7 58
97 1 18
