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python - Python Pandas 中的分组/分类年龄列

问题描述

我有一个数据框说df。df有一列'Ages'

>>> df['Age']

年龄数据

我想对这个年龄进行分组并创建一个类似这样的新列

If age >= 0 & age < 2 then AgeGroup = Infant
If age >= 2 & age < 4 then AgeGroup = Toddler
If age >= 4 & age < 13 then AgeGroup = Kid
If age >= 13 & age < 20 then AgeGroup = Teen
and so on .....

如何使用 Pandas 库实现这一点。

我试着做这样的事情

X_train_data['AgeGroup'][ X_train_data.Age < 13 ] = 'Kid'
X_train_data['AgeGroup'][ X_train_data.Age < 3 ] = 'Toddler'
X_train_data['AgeGroup'][ X_train_data.Age < 1 ] = 'Infant'

但这样做我得到这个警告

/Users/Anand/miniconda3/envs/learn/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame 查看注意事项文档：http : //pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy 这与 ipykernel 包是分开的，所以我们可以避免在 /Users/Anand/miniconda3/ 之前进行导入envs/learn/lib/python3.7/site-packages/ipykernel_launcher.py:4：SettingWithCopyWarning：试图在数据帧的切片副本上设置值

如何避免此警告并以更好的方式进行。

标签： pythonpandasdataframe

解决方案

使用pandas.cutwith 参数right=False表示不包括 bin 的最右边：

X_train_data = pd.DataFrame({'Age':[0,2,4,13,35,-1,54]})

bins= [0,2,4,13,20,110]
labels = ['Infant','Toddler','Kid','Teen','Adult']
X_train_data['AgeGroup'] = pd.cut(X_train_data['Age'], bins=bins, labels=labels, right=False)
print (X_train_data)
   Age AgeGroup
0    0   Infant
1    2  Toddler
2    4      Kid
3   13     Teen
4   35    Adult
5   -1      NaN
6   54    Adult

最后用于替换缺失值add_categories使用fillna：

X_train_data['AgeGroup'] = X_train_data['AgeGroup'].cat.add_categories('unknown')
                                                   .fillna('unknown')
print (X_train_data)
   Age AgeGroup
0    0   Infant
1    2  Toddler
2    4      Kid
3   13     Teen
4   35    Adult
5   -1  unknown
6   54    Adult

bins= [-1,0,2,4,13,20, 110]
labels = ['unknown','Infant','Toddler','Kid','Teen', 'Adult']
X_train_data['AgeGroup'] = pd.cut(X_train_data['Age'], bins=bins, labels=labels, right=False)

print (X_train_data)
   Age AgeGroup
0    0   Infant
1    2  Toddler
2    4      Kid
3   13     Teen
4   35    Adult
5   -1  unknown
6   54    Adult

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