根据特定的行值选择熊猫数据透视表中的列？
[英]Selecting columns in a pandas pivot table based on specific row value?
问题描述
I have data in a pivot table dataframe. 
我在数据透视表数据框中有数据。 The columns are dates labeled 'UniqueDate' and the index is 'Time' of day. 列是标有“ UniqueDate”的日期，索引是“时间”。 I would like to select all columns where the 10th minute of the day is a positive value. 我想选择一天中第10分钟为正值的所有列。 Having some difficulty doing this due to the pivoted nature of the data. 由于数据的关键性，这样做有些困难。
UniqueDate Apr 03, 2015 Apr 04, 2014 Apr 11, 2014 Apr 24, 2015
Time
00:00:00 NaN NaN NaN NaN
00:01:00 0.060665 -0.066291 0.136705 0.000000
00:02:00 0.121326 -0.066291 0.136705 0.000000
00:03:00 0.181984 -0.066291 0.136705 0.059305
00:04:00 0.181984 -0.066291 0.205049 0.000004
00:05:00 0.181984 -0.066291 0.136710 0.059308
00:06:00 0.121330 -0.066291 0.136710 0.059308
00:07:00 -0.060643 0.000004 0.068367 0.000007
00:08:00 -0.242649 0.066295 0.136715 0.118617
00:09:00 -0.242649 0.066295 0.136715 0.059319
00:10:00 -0.181969 0.132582 0.136715 0.059319
So in this example id like to return a new dataframe with columns: Apr 04, 2014 Apr 11, 2014 Apr 24, 2015 因此，在此示例中，id喜欢返回带有列的新数据框：2014年4月4日2014年4月11日2015年4月24日
UniqueDate Apr 04, 2014 Apr 11, 2014 Apr 24, 2015
Time
00:00:00 NaN NaN NaN
00:01:00 -0.066291 0.136705 0.000000
00:02:00 -0.066291 0.136705 0.000000
00:03:00 -0.066291 0.136705 0.059305
00:04:00 -0.066291 0.205049 0.000004
00:05:00 -0.066291 0.136710 0.059308
00:06:00 -0.066291 0.136710 0.059308
00:07:00 0.000004 0.068367 0.000007
00:08:00 0.066295 0.136715 0.118617
00:09:00 0.066295 0.136715 0.059319
00:10:00 0.132582 0.136715 0.059319
1 个解决方案
解决方案1
0 已采纳 2018-08-20 22:09:33
Try: 尝试：
df.loc[:,df.columns[df.iloc[10] > 0]]
Output: 输出：
UniqueDate Apr 04, 2014 Apr 11, 2014 Apr 24, 2014
Time
00:00:00 NaN NaN NaN
00:01:00 -0.066291 0.136705 0.000000
00:02:00 -0.066291 0.136705 0.000000
00:03:00 -0.066291 0.136705 0.059305
00:04:00 -0.066291 0.205049 0.000004
00:05:00 -0.066291 0.136710 0.059308
00:06:00 -0.066291 0.136710 0.059308
00:07:00 0.000004 0.068367 0.000007
00:08:00 0.066295 0.136715 0.118617
00:09:00 0.066295 0.136715 0.059319
00:10:00 0.132582 0.136715 0.059319
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