Grouping rows with same date Pandas df

Question

I have a csv file with data from multiple sensors, like this:

YY,   mm, dd, HH, MM,  sensor, sensorvalue
2018, 1,  1,  00, 00,  1,      0.2
2018, 1,  1,  00, 10,  1,      0
2018, 1,  1,  00, 20,  1,      0.1
2018, 1,  1,  00, 00,  2,      90.1
2018, 1,  1,  00, 10,  2,      90.3
2018, 1,  1,  00, 20,  2,      91.0
2018, 1,  1,  00, 00,  7,      1.5
2018, 1,  1,  00, 10,  7,      1.3
2018, 1,  1,  00, 20,  7,      0.7

And I want to transform that on a pandas df with a column for each sensor, with datetime as index, like this:

date,           sensor1value, sensor2value, sensor7value
2018-1-1 00:00, 0.2,          90.1,         1.5
2018-1-1 00:10, 0,            90.3,         1.3
2018-1-1 00:20, 0.1,          91.0,         0.7

There's an easy way to do that on pandas?

Gabriel Trégoat · Accepted Answer · 2020-11-17 22:16:51Z

1

You may want to use pandas apply to loop over rows and create a datetime date and set it as an index. Something like:

df.set_index(df.apply(lambda row: datetime(int(row["YY"]), int(row["mm"]), int(row["dd"]), int(row["HH"]), int(row["MM"])), axis=1)).loc[:, ["sensor1value", "sensor2value", "sensor7value"]]

answered Nov 17, 2020 at 22:16

Gabriel Trégoat

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Comments

dmildem · Accepted Answer · 2020-11-18 15:18:09Z

Thanks Gabriel for the answer, the lambda function worked well, but the iloc part is not exactly what I needed to do, so I created a solution using a dic with the name of each sensor code and using pivot table:

estParams = {
    1: 'sensor1value',
    2: 'sensor2value',
    7: 'sensor7value'
}
    
date = df.apply(lambda row: datetime(int(row["YY"]), int(row["mm"]), int(row["dd"]), int(row["HH"]), int(row["MM"])), axis=1)
df.insert(0, 'Date', date)

df["sensor"].replace(estParams, inplace=True)
pivot = df.pivot_table('sensorvalue', ['Date'], 'sensor')

The pivot table:

sensor                sensor1value  sensor2value    sensor7value
Date            
2018-01-01 00:00:00   0.2              90.1          1.5
2018-01-01 00:10:00   0.0              90.3          1.3
2018-01-01 00:20:00   0.1              91.0          0.7

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