数据帧绘制一列相对于另一列的概率分布

2024-09-30 01:24:39 发布

您现在位置：Python中文网/ 问答频道 /正文
8168 3

我有一个数据帧，并希望绘制概率分布图，即第一列（OutsidePeriod）在x轴上的另一列（PAR）的每个bin上等于True

如何在熊猫身上做到这一点

import sys
if sys.version_info[0] < 3: 
    from StringIO import StringIO
else:
    from io import StringIO

import pandas as pd

TESTDATA = StringIO('At,OutsidePeriod,PAR\n2011-01-03 07:00:00+00:00,True,25.999999999999357\n2011-01-04 07:00:00+00:00,False,40.99999999999993\n2011-01-05 07:00:00+00:00,True,21.999999999999797\n2011-01-06 07:00:00+00:00,True,19.000000000000128\n2011-01-07 07:00:00+00:00,True,17.000000000000348\n2011-01-10 07:00:00+00:00,True,18.000000000000238\n2011-01-11 07:00:00+00:00,False,26.000000000001577\n2011-01-12 07:00:00+00:00,False,26.999999999999247\n2011-01-13 07:00:00+00:00,False,22.999999999999687\n2011-01-14 07:00:00+00:00,False,37.00000000000037\n2011-01-17 07:00:00+00:00,False,48.00000000000138\n2011-01-18 07:00:00+00:00,False,60.99999999999994\n2011-01-19 07:00:00+00:00,False,51.000000000001044\n2011-01-20 07:00:00+00:00,True,47.999999999999154\n2011-01-21 07:00:00+00:00,False,51.999999999998714\n2011-01-24 07:00:00+00:00,False,24.999999999999467\n2011-01-25 07:00:00+00:00,True,30.000000000001137\n2011-01-26 07:00:00+00:00,True,24.999999999999467\n2011-01-27 07:00:00+00:00,True,18.000000000000238\n2011-01-28 07:00:00+00:00,False,29.000000000001247\n2011-01-31 07:00:00+00:00,False,30.000000000001137\n2011-02-01 07:00:00+00:00,True,25.999999999999357\n2011-02-02 07:00:00+00:00,False,23.999999999999577\n2011-02-03 07:00:00+00:00,True,21.999999999999797\n2011-02-04 07:00:00+00:00,True,15.000000000000568\n2011-02-07 07:00:00+00:00,True,14.000000000000679\n2011-02-08 07:00:00+00:00,True,36.000000000000476\n2011-02-09 07:00:00+00:00,True,19.000000000000128\n2011-02-10 07:00:00+00:00,False,31.000000000001027\n2011-02-11 07:00:00+00:00,False,31.000000000001027\n2011-02-14 07:00:00+00:00,True,24.999999999999467\n2011-02-15 07:00:00+00:00,True,42.9999999999997\n2011-02-16 07:00:00+00:00,False,49.999999999998934\n2011-02-17 07:00:00+00:00,True,38.000000000000256\n2011-02-18 07:00:00+00:00,False,20.999999999999908\n2011-02-21 07:00:00+00:00,True,24.999999999999467\n2011-02-22 07:00:00+00:00,False,20.000000000000018\n2011-02-23 07:00:00+00:00,False,46.99999999999926\n2011-02-24 07:00:00+00:00,True,47.000000000001485\n2011-02-25 07:00:00+00:00,True,20.999999999999908\n2011-02-28 07:00:00+00:00,False,34.999999999998366\n2011-03-01 07:00:00+00:00,True,24.999999999999467\n2011-03-02 07:00:00+00:00,True,22.999999999999687\n2011-03-03 07:00:00+00:00,True,22.999999999999687\n2011-03-04 07:00:00+00:00,True,12.000000000000899\n2011-03-07 07:00:00+00:00,True,12.999999999998568\n2011-03-08 07:00:00+00:00,False,24.999999999999467\n2011-03-09 07:00:00+00:00,True,24.999999999999467\n2011-03-10 07:00:00+00:00,False,17.999999999998018\n2011-03-11 07:00:00+00:00,True,28.000000000001357\n2011-03-14 07:00:00+00:00,True,25.000000000001688\n2011-03-15 07:00:00+00:00,False,72.00000000000095\n2011-03-16 07:00:00+00:00,True,23.000000000001908\n2011-03-17 07:00:00+00:00,False,22.999999999999687\n2011-03-18 07:00:00+00:00,True,30.000000000001137\n2011-03-21 07:00:00+00:00,True,10.999999999998789\n2011-03-22 07:00:00+00:00,False,19.000000000000128\n2011-03-23 07:00:00+00:00,True,12.999999999998568\n2011-03-24 07:00:00+00:00,True,20.000000000000018\n2011-03-25 07:00:00+00:00,True,13.000000000000789\n2011-03-28 07:00:00+00:00,True,20.000000000000018\n2011-03-29 07:00:00+00:00,False,52.00000000000094\n2011-03-30 07:00:00+00:00,True,25.999999999999357\n2011-03-31 07:00:00+00:00,False,40.000000000000036\n2011-04-01 07:00:00+00:00,True,20.000000000000018\n2011-04-04 07:00:00+00:00,True,17.000000000000348\n2011-04-05 07:00:00+00:00,False,20.000000000000018\n2011-04-06 07:00:00+00:00,False,16.00000000000046\n2011-04-07 07:00:00+00:00,False,20.000000000000018\n2011-04-08 07:00:00+00:00,False,43.999999999999595\n2011-04-11 07:00:00+00:00,False,16.00000000000046\n2011-04-12 07:00:00+00:00,False,32.00000000000092\n2011-04-13 07:00:00+00:00,False,32.00000000000092\n2011-04-14 07:00:00+00:00,False,67.0000000000015\n2011-04-15 07:00:00+00:00,True,16.00000000000046\n2011-04-18 07:00:00+00:00,False,21.999999999999797\n2011-04-19 07:00:00+00:00,True,15.000000000000568\n2011-04-20 07:00:00+00:00,False,21.999999999999797\n2011-04-21 07:00:00+00:00,False,54.00000000000071\n2011-04-22 07:00:00+00:00,False,40.000000000000036\n2011-04-25 07:00:00+00:00,False,41.999999999999815\n2011-04-26 07:00:00+00:00,True,20.000000000000018\n2011-04-27 07:00:00+00:00,False,31.000000000001027\n2011-04-28 07:00:00+00:00,False,41.999999999999815\n2011-04-29 07:00:00+00:00,False,24.999999999999467\n2011-05-02 07:00:00+00:00,False,40.99999999999993\n2011-05-03 07:00:00+00:00,False,52.99999999999861\n2011-05-04 07:00:00+00:00,False,40.000000000000036\n2011-05-05 07:00:00+00:00,False,39.00000000000014\n2011-05-06 07:00:00+00:00,True,42.9999999999997\n2011-05-09 07:00:00+00:00,False,38.999999999997925\n2011-05-10 07:00:00+00:00,True,70.99999999999883\n2011-05-11 07:00:00+00:00,True,25.000000000001688\n2011-05-12 07:00:00+00:00,False,40.99999999999993\n2011-05-13 07:00:00+00:00,False,45.999999999999375\n2011-05-16 07:00:00+00:00,False,27.000000000001467\n2011-05-17 07:00:00+00:00,True,44.99999999999949\n2011-05-18 07:00:00+00:00,False,25.000000000001688\n2011-05-19 07:00:00+00:00,False,46.99999999999926\n2011-05-20 07:00:00+00:00,True,22.999999999999687\n2011-05-23 07:00:00+00:00,False,34.000000000000696\n2011-05-24 07:00:00+00:00,False,43.999999999999595\n2011-05-25 07:00:00+00:00,False,45.000000000001705\n2011-05-26 07:00:00+00:00,False,47.000000000001485\n2011-05-27 07:00:00+00:00,False,90.99999999999886\n2011-05-30 07:00:00+00:00,True,30.000000000001137\n2011-05-31 07:00:00+00:00,True,21.999999999999797\n2011-06-01 07:00:00+00:00,True,34.999999999998366\n2011-06-02 07:00:00+00:00,False,20.999999999999908\n2011-06-03 07:00:00+00:00,True,22.999999999999687\n2011-06-06 07:00:00+00:00,True,20.000000000000018\n2011-06-07 07:00:00+00:00,False,39.00000000000014\n2011-06-08 07:00:00+00:00,True,28.999999999999027\n2011-06-09 07:00:00+00:00,True,20.000000000000018\n2011-06-10 07:00:00+00:00,False,55.000000000000604\n2011-06-13 07:00:00+00:00,True,17.000000000000348\n2011-06-14 07:00:00+00:00,False,29.999999999998916\n2011-06-15 07:00:00+00:00,False,27.000000000001467\n2011-06-16 07:00:00+00:00,False,109.99999999999899\n2011-06-17 07:00:00+00:00,False,59.00000000000016\n2011-06-20 07:00:00+00:00,False,57.000000000000384\n2011-06-21 07:00:00+00:00,False,39.00000000000014\n2011-06-22 07:00:00+00:00,False,32.00000000000092\n2011-06-23 07:00:00+00:00,True,25.999999999999357\n2011-06-24 07:00:00+00:00,True,22.999999999999687\n2011-06-27 07:00:00+00:00,False,22.999999999999687\n2011-06-28 07:00:00+00:00,True,38.999999999997925\n2011-06-29 07:00:00+00:00,False,34.999999999998366\n2011-06-30 07:00:00+00:00,True,21.999999999999797\n2011-07-01 07:00:00+00:00,False,40.000000000000036\n2011-07-04 07:00:00+00:00,False,57.000000000000384\n2011-07-05 07:00:00+00:00,True,27.000000000001467\n2011-07-06 07:00:00+00:00,False,21.999999999999797\n2011-07-07 07:00:00+00:00,True,29.999999999998916\n2011-07-08 07:00:00+00:00,True,20.000000000000018\n2011-07-11 07:00:00+00:00,False,29.000000000001247\n2011-07-12 07:00:00+00:00,False,39.00000000000014\n2011-07-13 07:00:00+00:00,False,37.99999999999804\n2011-07-14 07:00:00+00:00,True,50.99999999999882\n2011-07-15 07:00:00+00:00,False,19.000000000000128\n2011-07-18 07:00:00+00:00,False,22.000000000002018\n2011-07-19 07:00:00+00:00,True,53.000000000000824\n2011-07-20 07:00:00+00:00,False,29.999999999998916\n2011-07-21 07:00:00+00:00,True,30.000000000001137\n2011-07-22 07:00:00+00:00,False,31.000000000001027\n2011-07-25 07:00:00+00:00,False,45.000000000001705\n2011-07-26 07:00:00+00:00,False,45.000000000001705\n2011-07-27 07:00:00+00:00,True,30.000000000001137\n2011-07-28 07:00:00+00:00,True,26.999999999999247\n2011-07-29 07:00:00+00:00,False,81.99999999999986\n2011-08-01 07:00:00+00:00,False,24.999999999999467\n2011-08-02 07:00:00+00:00,False,40.99999999999993\n2011-08-03 07:00:00+00:00,True,43.999999999999595\n2011-08-04 07:00:00+00:00,True,39.00000000000014\n2011-08-05 07:00:00+00:00,True,40.000000000000036\n2011-08-08 07:00:00+00:00,False,54.00000000000071\n2011-08-09 07:00:00+00:00,True,44.00000000000182\n2011-08-10 07:00:00+00:00,True,29.999999999998916\n2011-08-11 07:00:00+00:00,False,41.999999999999815\n2011-08-12 07:00:00+00:00,True,34.000000000000696\n2011-08-15 07:00:00+00:00,False,32.00000000000092\n2011-08-16 07:00:00+00:00,False,33.00000000000081\n2011-08-17 07:00:00+00:00,False,36.000000000000476\n2011-08-18 07:00:00+00:00,True,39.00000000000014\n2011-08-19 07:00:00+00:00,False,55.000000000000604\n2011-08-22 07:00:00+00:00,True,20.999999999999908\n2011-08-23 07:00:00+00:00,False,21.999999999999797\n2011-08-24 07:00:00+00:00,True,33.99999999999848\n2011-08-25 07:00:00+00:00,False,18.000000000000238\n2011-08-26 07:00:00+00:00,False,31.000000000001027\n2011-08-29 07:00:00+00:00,False,65.00000000000172\n2011-08-30 07:00:00+00:00,False,20.999999999999908\n2011-08-31 07:00:00+00:00,True,15.000000000000568\n2011-09-01 07:00:00+00:00,False,20.000000000000018\n2011-09-02 07:00:00+00:00,False,30.000000000001137\n2011-09-05 07:00:00+00:00,False,23.999999999999577\n2011-09-06 07:00:00+00:00,False,43.999999999999595\n2011-09-07 07:00:00+00:00,False,39.00000000000014\n2011-09-08 07:00:00+00:00,False,20.999999999999908\n2011-09-09 07:00:00+00:00,False,46.99999999999926\n2011-09-12 07:00:00+00:00,False,94.99999999999842\n2011-09-13 07:00:00+00:00,True,43.999999999999595\n2011-09-14 07:00:00+00:00,True,49.999999999998934\n2011-09-15 07:00:00+00:00,False,36.000000000000476\n2011-09-16 07:00:00+00:00,False,26.999999999999247\n2011-09-19 07:00:00+00:00,True,20.999999999999908\n2011-09-20 07:00:00+00:00,False,33.00000000000081\n2011-09-21 07:00:00+00:00,False,31.000000000001027\n2011-09-22 07:00:00+00:00,True,41.999999999999815\n2011-09-23 07:00:00+00:00,False,72.00000000000095\n2011-09-26 07:00:00+00:00,True,44.99999999999949\n2011-09-27 07:00:00+00:00,True,50.00000000000115\n2011-09-28 07:00:00+00:00,False,39.00000000000014\n2011-09-29 07:00:00+00:00,False,69.99999999999895\n2011-09-30 07:00:00+00:00,False,40.99999999999993\n2011-10-03 07:00:00+00:00,False,28.000000000001357\n2011-10-04 07:00:00+00:00,True,35.00000000000058\n2011-10-05 07:00:00+00:00,False,40.000000000000036\n2011-10-06 07:00:00+00:00,True,17.000000000000348\n2011-10-07 07:00:00+00:00,True,18.000000000000238\n2011-10-10 07:00:00+00:00,False,46.99999999999926\n2011-10-11 07:00:00+00:00,True,29.000000000001247\n2011-10-12 07:00:00+00:00,False,34.000000000000696\n2011-10-13 07:00:00+00:00,True,33.99999999999848\n2011-10-14 07:00:00+00:00,True,43.999999999999595\n2011-10-17 07:00:00+00:00,False,32.999999999998586\n2011-10-18 07:00:00+00:00,False,29.000000000001247\n2011-10-19 07:00:00+00:00,False,63.999999999999616\n2011-10-20 07:00:00+00:00,True,44.99999999999949\n2011-10-21 07:00:00+00:00,False,40.000000000000036\n2011-10-24 07:00:00+00:00,True,61.99999999999983\n2011-10-25 07:00:00+00:00,True,39.00000000000014\n2011-10-26 07:00:00+00:00,True,17.000000000000348\n2011-10-27 07:00:00+00:00,False,55.99999999999827\n2011-10-28 07:00:00+00:00,True,27.000000000001467\n2011-10-31 07:00:00+00:00,True,63.999999999999616\n2011-11-01 07:00:00+00:00,False,60.00000000000006\n2011-11-02 07:00:00+00:00,False,28.999999999999027\n2011-11-03 07:00:00+00:00,True,41.999999999999815\n2011-11-04 07:00:00+00:00,False,34.000000000000696\n2011-11-07 07:00:00+00:00,False,23.999999999999577\n2011-11-08 07:00:00+00:00,True,21.999999999999797\n2011-11-09 07:00:00+00:00,False,22.999999999999687\n2011-11-10 07:00:00+00:00,True,53.999999999998494\n2011-11-11 07:00:00+00:00,True,20.000000000000018\n2011-11-14 07:00:00+00:00,False,34.999999999998366\n2011-11-15 07:00:00+00:00,False,42.00000000000203\n2011-11-16 07:00:00+00:00,False,36.000000000000476\n2011-11-17 07:00:00+00:00,False,46.99999999999926\n2011-11-18 07:00:00+00:00,False,29.999999999998916\n2011-11-21 07:00:00+00:00,False,25.999999999999357\n2011-11-22 07:00:00+00:00,False,26.999999999999247\n2011-11-23 07:00:00+00:00,True,31.999999999998696\n2011-11-24 07:00:00+00:00,False,28.000000000001357\n2011-11-25 07:00:00+00:00,True,21.999999999999797\n2011-11-28 07:00:00+00:00,True,39.00000000000014\n2011-11-29 07:00:00+00:00,False,40.000000000000036\n2011-11-30 07:00:00+00:00,False,47.000000000001485\n2011-12-01 07:00:00+00:00,True,29.999999999998916\n2011-12-02 07:00:00+00:00,False,21.999999999999797\n2011-12-05 07:00:00+00:00,False,25.000000000001688\n2011-12-06 07:00:00+00:00,True,19.399999999998307\n2011-12-07 07:00:00+00:00,True,32.40000000000131\n2011-12-08 07:00:00+00:00,True,15.700000000000713\n2011-12-09 07:00:00+00:00,True,42.79999999999839\n2011-12-12 07:00:00+00:00,False,23.200000000000998\n2011-12-13 07:00:00+00:00,True,20.799999999998597\n2011-12-14 07:00:00+00:00,False,28.200000000000447\n2011-12-15 07:00:00+00:00,True,20.599999999999508\n2011-12-16 07:00:00+00:00,True,15.199999999999658\n2011-12-19 07:00:00+00:00,False,31.799999999999606\n2011-12-20 07:00:00+00:00,False,14.499999999999513\n2011-12-21 07:00:00+00:00,False,25.700000000001832\n2011-12-22 07:00:00+00:00,False,27.600000000000957\n2011-12-23 07:00:00+00:00,False,30.999999999998806\n2011-12-26 07:00:00+00:00,False,30.999999999998806\n2011-12-27 07:00:00+00:00,True,9.300000000000974\n2011-12-28 07:00:00+00:00,True,11.600000000000499\n2011-12-29 07:00:00+00:00,True,13.900000000000023\n2011-12-30 07:00:00+00:00,False,23.900000000001143\n')

test_df = pd.read_csv(TESTDATA)
test_df

0   2011-01-03 07:00:00+00:00   True    26.0
1   2011-01-04 07:00:00+00:00   False   41.0
2   2011-01-05 07:00:00+00:00   True    22.0
3   2011-01-06 07:00:00+00:00   True    19.0
4   2011-01-07 07:00:00+00:00   True    17.0
... ... ... ...
255 2011-12-26 07:00:00+00:00   False   31.0
256 2011-12-27 07:00:00+00:00   True    9.3
257 2011-12-28 07:00:00+00:00   True    11.6
258 2011-12-29 07:00:00+00:00   True    13.9
259 2011-12-30 07:00:00+00:00   False   23.9
260 rows × 3 columns

Tags： 数据fromtestimportfalsetruedfsys
1条回答
网友
1楼 · 发布于 2024-09-30 01:24:39

所以我想到的是：

bin_width = 5
test_df['bin'] = np.floor(test_df['PAR'] / bin_width) * bin_width
test_df.groupby('bin')['OutsidePeriod'].agg(lambda g: len(g[g])/len(g)).plot()

enter image description here

相关问题 更多 >

    编程相关推荐

    热门问题