[英]Find minimum daily value using pandas GroupBy or pivot_table
I have a Dataframe obtained from a csv file (after some filtering) that looks like this: 我有一个从csv文件获得的数据帧(经过一些过滤),看起来像这样:
df3.head(n = 10)
DateTime Det_ID Speed
16956 2014-01-01 07:00:00 1201085 65.0
16962 2014-01-01 07:00:00 1201110 69.5
19377 2014-01-01 08:00:00 1201085 65.0
19383 2014-01-01 08:00:00 1201110 65.0
21798 2014-01-01 09:00:00 1201085 65.0
21804 2014-01-01 09:00:00 1201110 65.4
75060 2014-01-02 07:00:00 1201085 64.9
75066 2014-01-02 07:00:00 1201110 66.1
77481 2014-01-02 08:00:00 1201085 65.0
77487 2014-01-02 08:00:00 1201110 62.5
This represents the speeds measured by different detectors (two for now) at various times of day. 这代表了一天中不同时间由不同检测器(现在为两个)测量的速度。 I have converted the DateTime column to a datetime object.
我已经将DateTime列转换为datetime对象。
I need to know for each detector, the minimum daily value of the speed. 我需要知道每个检测器的最低每日速度值。
Basically, something like this, which I can then use to build a heat map. 基本上是这样的,然后我可以用它来构建热图。
df4 = df3.pivot_table(index='DateTime',columns='Det_ID',aggfunc=min)
df4.head()
Speed
Det_ID 1201085 1201110
DateTime
2014-01-01 07:00:00 65.0 69.5
2014-01-01 08:00:00 65.0 65.0
2014-01-01 09:00:00 65.0 65.4
2014-01-02 07:00:00 64.9 66.1
2014-01-02 08:00:00 65.0 62.5
Clearly, the way I've used the pivot table is incorrect as I'm getting multiple values of daily speeds, not just one. 显然,我使用数据透视表的方式是不正确的,因为我获得了多个每日速度值,而不仅仅是一个。 I suspect it is because the minimum is being calculated over each unique DateTime field, not just the for the date part.
我怀疑这是因为最小值是在每个唯一的DateTime字段上计算的,而不仅仅是日期部分的。
Also trying groupby options. 还尝试使用groupby选项。
list(df3.groupby(['DateTime'], sort = False)['Speed'].min())
But it just gives a list of numbers, without any other columns. 但是它只是给出了一个数字列表,没有任何其他列。
65.0,
65.0,
65.0,
64.900000000000006,
62.5,
64.200000000000003,
54.700000000000003,
62.600000000000001,
64.799999999999997,
59.5,
etc. 等等
How do I isolate just the date part in the DateTime field? 如何在DateTime字段中仅隔离日期部分? Am I even going in the right direction?
我什至朝着正确的方向前进吗? Thanks.
谢谢。
Call .dt.strftime
and reformat your DateTime
column. 调用
.dt.strftime
并重新格式化DateTime
列。
df.DateTime = df.DateTime.dt.strftime('%m/%d/%Y')
df
DateTime Det_ID Speed
16956 01/01/2014 1201085 65.0
16962 01/01/2014 1201110 69.5
19377 01/01/2014 1201085 65.0
19383 01/01/2014 1201110 65.0
21798 01/01/2014 1201085 65.0
21804 01/01/2014 1201110 65.4
75060 01/02/2014 1201085 64.9
75066 01/02/2014 1201110 66.1
77481 01/02/2014 1201085 65.0
77487 01/02/2014 1201110 62.5
Now, call pivot_table
: 现在,调用
pivot_table
:
df = df.pivot_table(index='DateTime', columns='Det_ID', values='Speed', aggfunc=np.min)
df
Det_ID 1201085 1201110
DateTime
01/01/2014 65.0 65.0
01/02/2014 64.9 62.5
Or using unstack
或使用
unstack
df.DateTime = df.DateTime.dt.strftime('%m/%d/%Y')
df.groupby(['DateTime','Det_ID']).Speed.min().unstack()
Out[300]:
Det_ID 1201085 1201110
DateTime
01/01/2014 65.0 65.0
01/02/2014 64.9 62.5
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