分组/分类年龄列在Python熊猫

Question

我有一个数据框，上面写着dfdf有一列“年龄”

<代码>

我想对这些年龄段进行分组，并创建一个类似这样的新专栏

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。

我试过这样做

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'

但这样做我得到了这个警告

/Users/Anand/miniconda3/envs/learn/lib/python3.7/site packages/ipykernel_launcher.py:3:SettingWithCopyWarning:试图在数据帧切片的副本上设置值请参阅文档中的注意事项：http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-查看与复制这是独立于ipykernel包的，因此我们在/Users/Anand/miniconda3/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之前，可以避免进行导入

如何避免此警告并以更好的方式执行。

Answer 1

只需使用：

X_train_data.loc[(X_train_data.Age < 13),  'AgeGroup'] = 'Kid'

Answer 2

使用带有参数right=False的pandas.cut，用于不包括箱子最右边的边缘：

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

最后一个替换缺少的值使用将类别添加为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