分箱极坐标
[英]Binning polar coordinates
问题描述
My question is quite straight forward.
我的问题很直接。 I want to bin polar coordinates, which means that the domain in which I want to bin is limited by 0 and 360, where 0 = 360. Here start my problems, due to this circular behavior of the data, and as I want to bin each 1 degree starting from 0.5 degrees up to 355.5 degrees (Unfortunately due to the nature of the project binning from (0,1] until (359,360]), then, I have to make sure that there is a bin that goes from (355.5,0.5], which is obviously not what will happen by default.我想合并极坐标，这意味着我想要合并的域受 0 和 360 的限制，其中 0 = 360。由于数据的这种循环行为，这里开始我的问题，因为我想合并每 1 度从 0.5 度开始到 355.5 度（不幸的是，由于项目从 (0,1] 到 (359,360]) 分箱的性质，然后，我必须确保有一个从 (355.5) 开始的分箱,0.5]，这显然不是默认情况下会发生的。
I made up this script to better illustrate what I am looking for:我编写了这个脚本来更好地说明我在寻找什么：
bins_direction = np.linspace(0.5,360.5,360, endpoint = False)
points = np.random.rand(10000)*360
df = pd.DataFrame({'Points': points})
df['Bins'] = pd.cut(x= df['Points'],
bins=bins_direction)
You will see that if the data is between 355.5 and 0.5 degrees, the binning will be NaN.您将看到，如果数据介于 355.5 和 0.5 度之间，则分箱将为 NaN。 I want to find a solution in which that would be (355.5,0.5]我想找到一个解决方案，即 (355.5,0.5]
So, my result (depending on which seed you set, of course), will look something like this:因此，我的结果（当然取决于您设置的种子）将如下所示：
Points Bins
0 17.102993 (16.5, 17.5]
1 97.665600 (97.5, 98.5]
2 46.697548 (46.5, 47.5]
3 9.832000 (9.5, 10.5]
4 21.260980 (20.5, 21.5]
5 47.433179 (46.5, 47.5]
6 359.813283 nan
7 355.654251 (355.5, 356.5]
8 0.23740105 nan
And I would like it to be:我希望它是：
Points Bins
0 17.102993 (16.5, 17.5]
1 97.665600 (97.5, 98.5]
2 46.697548 (46.5, 47.5]
3 9.832000 (9.5, 10.5]
4 21.260980 (20.5, 21.5]
5 47.433179 (46.5, 47.5]
6 359.813283 (359.5, 0.5]
7 355.654251 (355.5, 356.5]
8 0.23740105 (359.5, 0.5]
1 个解决方案
解决方案1
1 2020-02-12 15:20:30
Since you cannot have a pandas interval of the form (355.5, 0.5] , you can only have them as string:由于您不能拥有(355.5, 0.5]形式的pandas区间，因此您只能将它们作为字符串：
bins = [0] + list(np.linspace(0.5,355.5,356)) + [360]
df = pd.DataFrame({'Points': [0,1,350,356, 357, 359]})
(pd.cut(df['Points'], bins=bins, include_lowest=True)
.astype(str)
.replace({'(-0.001, 0.5]':'(355.5,0.5]', '(355.5, 360.0]':'(355.5,0.5]'})
)
Output:输出：
0 (355.5,0.5]
1 (0.5, 1.5]
2 (349.5, 350.5]
3 (355.5,0.5]
4 (355.5,0.5]
5 (355.5,0.5]
Name: Points, dtype: object
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