pandas DataFrame：如何使用自定义方式剪切数据框？
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pandas DataFrame: How to cut a dataframe using custom ways?
提问by Peng He
I want to cut a DataFrame to several dataframes using my own rules.
我想使用我自己的规则将一个 DataFrame 剪切为多个数据帧。
>>> data = pd.DataFrame({'distance':[1,2,3,4,5,6,7,8,9,10],'values':np.arange(0,1,0.1)})
>>> data
distance values
0 1 0.0
1 2 0.1
2 3 0.2
3 4 0.3
4 5 0.4
5 6 0.5
6 7 0.6
7 8 0.7
8 9 0.8
9 10 0.9
I'll cut dataaccording to values of distancecolumn. For example, there's some bins [1,3),[3,8),[8,10),[10,10+), if data's column distancein same bin,I separate them into same group and compute column valuesaverage value or sum value.That is
我会data根据distance列的值进行切割。例如，有一些 bins [1,3),[3,8),[8,10),[10,10+)，如果数据的列distance在同一个 bin 中，我将它们分成同一组并计算列values平均值或总和值。那就是
>>> data1 = data[lambda df:(df.distance >= 1) & (df.distance < 3)]
>>> data1
distance values
0 1 0.0
1 2 0.1
>>> np.mean(data1['values'])
0.05
How can I cut origin DataFrame into several groups(and then save them,process them...) efficiently?
如何有效地将原始 DataFrame 分成几组（然后保存它们，处理它们......）？
回答by Bob Baxley
Pandas cutcommand is useful for this:
Pandas cut命令对此很有用：
data['categories']=pd.cut(data['distance'],[-np.inf,1,3,8,10,np.inf],right=False)
data.groupby('categories').mean()
Output:
输出：
distance values
categories
[-inf, 1) NaN NaN
[1, 3) 1.5 0.05
[3, 8) 5.0 0.40
[8, 10) 8.5 0.75
[10, inf) 10.0 0.90
