在 Python Pandas 中对年龄列进行分组/分类
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Grouping / Categorising ages column in Python Pandas
提问by Anand Siddharth
I have a dataframe say df. dfhas a column 'Ages'
我有一个数据框说df。df有一列'Ages'
>>> df['Age']
>>> df['Age']
I want to group this ages and create a new column something like this
我想对这个年龄分组并创建一个像这样的新列
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 .....
How can I achieve this using Pandas library.
如何使用 Pandas 库实现这一点。
I tried doing this something like this
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'
but doing this i get this warning
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 See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copyThis is separate from the ipykernel package so we can avoid doing imports until /Users/Anand/miniconda3/envs/learn/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
/Users/Anand/miniconda3/envs/learn/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 正在尝试在 DataFrame 的切片副本上设置值 请参阅文档：http: //pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy 这与 ipykernel 包分开，因此我们可以避免在 /Users/Anand/miniconda3/ 之前进行导入envs/learn/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: 试图在 DataFrame 的切片副本上设置一个值
How to avoid this warning and do it in a better way.
如何避免此警告并以更好的方式做到这一点。
回答by jezrael
Use pandas.cutwith parameter right=Falsefor not includes the rightmost edge of bins:
使用pandas.cut与参数right=False不包括箱的最右边：
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
Last for replace missing value use add_categorieswith fillna:
最后的替代缺失值使用add_categories具有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
回答by quest
Just use:
只需使用：
X_train_data.loc[(X_train_data.Age < 13), 'AgeGroup'] = 'Kid'
