Force to use DatetimeIndex with Pandas

Question

I have the following Pandas dataframe:

df.head()

Output

id unplug_hourDateTime 0 2018-09-01 01:00:00+02:00 1 2018-03-01 01:00:00+02:00 2 2018-03-01 01:00:00+02:00 3 2018-04-01 01:00:00+02:00 4 2018-04-01 01:00:00+02:00

My objective is to build a calmap graph based on the occurrence of record every day, so I need a dataframe with the index in DatetimeIndex, TimedeltaIndex or PeriodIndex format.

I wrote the following:

df['unplug_Date']=df['unplug_hourDateTime'].map(lambda x : x.date()) df_calmap=df['unplug_Date'].value_counts().to_frame() df_calmap.head()

Output

 unplug_Date 2018-09-20 16562 2018-09-13 16288 2018-09-19 16288 2018-09-12 16092 2018-09-27 16074

At first glance it looks what I was looking for but if I use the calmap package, and I execute calmap.calendarplot(df_calmap) I get the an error, which I supposed is due to the format of the index.

AttributeError: 'Index' object has no attribute 'year'

How can I force the dataframe to use the index column as DatetimeIndex? I have found this interesting answer but I can't understand how to use df = df.set_index(pd.DatetimeIndex(df['b'])) with the already existing index and not with a new column.

It work, thanks! (I used DatetimeIndex method instead of DateTimeIndex) — Nicolaesse
– Nicolaesse, Commented Jan 6, 2019 at 20:07

Sander van den Oord · Accepted Answer · 2019-01-06 20:25:42Z

The calmap documentation states that it will default sum per day, so you don't have to change your datetime field to a date field. Just change your unplug_hourDateTime column to a datetime index as follows. My example uses method chaining, which means everything is done in 1 go:

df_calmap = (df .assign(unplug_hourDateTime=pd.DatetimeIndex(df['unplug_hourDateTime'])) .groupby('unplug_hourDateTime') .size() .to_frame('count') ) calmap.calendarplot(df_calmap['count'])

Of course, you can also use Josh Friedlander's nice answer:

df.index = pd.DateTimeIndex(df.index)

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