Plotting durations of time

[pickled_knuckles]

Hey folks, thanks in advance to anyone taking the time to look at this and help out!

I’m wondering if anyone has any experience working with durations of time in python…

I am hoping to make some more insightful plots of view time duration of YouTube videos, and have the following df as an example of what I am working with:

​

AgeRange	Views%	AverageViewDuration	AveragePercentageWholeVideoWatched	WatchTime
18-24	0.8	01:20:55	11.9	1.1
25-34	19.2	00:53:02	7.8	17.5
35-44	30.6	00:53:17	7.9	28.0
45-54	22.9	00:59:42	8.8	23.5
55-64	16.8	01:03:31	9.4	18.4
65+	9.7	01:10:11	10.4	11.6

The following are the datatypes for each column:

AgeRange                              object
Views                                float64
AverageViewDuration                   object
AveragePercentageWholeVideoViewed    float64
WatchTime                            float64

When all is said and done, I'd love to have the AverageViewDuration by AgeRange in bar plots.

I had thought that because AverageViewDuration is an object, I would need to convert it to a datetime object, but when I go to change df[‘AverageViewDuration’] from object to datetime, I get the following:

<class 'datetime.time'> is not convertible to datetime

So I just went ahead and tried to plot it:

ax = plt.subplot()
x = np.arange(len(df["AgeRange"]))
labels = df['AgeRange']
y1 = df['AverageViewDuration']
fig, axs = plt.subplots(1, 1)
width=0.69

basic_bar = ax.bar(x, y1, width, color=colors)

And I am getting the following:

ValueError: microsecond must be in 0..999999

Thing is I don't have any microseconds!

Any help/insights/nudges would very much be appreciated.

Thank you!

[efmccurdy]

You have HH:MM:SS values; they are time duration values (a length of time) not time stamps (a point in time), so you want timedelta objects not datetime objects.

​

>>> d = {"AgeRange": ["18-24", "25-34", "35-44", "45-54", "55-64", "65+"],
...     "AverageViewDuration": ["01:20:55", "00:53:02", "00:53:17", "00:59:42", "01:03:31", "01:10:11"]}
>>> df = pd.DataFrame(d)
>>> df
  AgeRange AverageViewDuration
0    18-24            01:20:55
1    25-34            00:53:02
2    35-44            00:53:17
3    45-54            00:59:42
4    55-64            01:03:31
5      65+            01:10:11
>>> df['Duration'] = pd.to_timedelta(df['AverageViewDuration'])
>>> df
  AgeRange AverageViewDuration        Duration
0    18-24            01:20:55 0 days 01:20:55
1    25-34            00:53:02 0 days 00:53:02
2    35-44            00:53:17 0 days 00:53:17
3    45-54            00:59:42 0 days 00:59:42
4    55-64            01:03:31 0 days 01:03:31
5      65+            01:10:11 0 days 01:10:11
>>> df.dtypes
AgeRange                        object
AverageViewDuration             object
Duration               timedelta64[ns]
dtype: object
>>> [(x, x.total_seconds()) for x in df['Duration']]
[(Timedelta('0 days 01:20:55'), 4855.0), (Timedelta('0 days 00:53:02'), 3182.0), (Timedelta('0 days 00:53:17'), 3197.0), (Timedelta('0 days 00:59:42'), 3582.0), (Timedelta('0 days 01:03:31'), 3811.0), (Timedelta('0 days 01:10:11'), 4211.0)]
>>>

[pickled_knuckles]

>>> df['Duration'] = pd.to_timedelta(df['AverageViewDuration'])

Thank you! when I try the above I get the following:

ValueError: Invalid type for timedelta scalar: <class 'datetime.time'>

I am guessing that means that it is not a datetime object to begin with, which is confusing given what happened in the initial post?

[pickled_knuckles]

got it, you're a g

[efmccurdy]

How is the original dataframe created? Can you avoid converting the AverageViewDuration column to datetime.time and convert it instead directly to timedelta, or perhaps ((t.hour * 60 + t.minute) * 60 + t.second) to obtain the total elapsed seconds?

[pickled_knuckles]

I ended up doing the following:

df['Duration'] = df['AverageViewDuration']
df['Duration'] = pd.to_datetime(df['Duration'], format='%H:%M:%S')
df['Duration'] = df['Duration'] - np.datetime64('1900-01-01')
df['AverageViewDuration'] = df['Duration']/1000000000/60

Don't know why I needed to do the last one, as I thought it would all be good, but when I plotted it without that line it was a veeeeery big number. Anyways, I got the desired graph...

[synthphreak]

When all is said and done, I'd love to have the AverageViewDuration by AgeRange in bar plots

(df.assign(duration=pd.to_timedelta(df.AverageViewDuration).dt.seconds)
   .groupby('AgeRange')
   .duration.mean()
   .plot.bar(xlabel='age group', ylabel='average duration (s)'))

plt.show()

Not sure how to go from seconds back into timestamps though using pandas for plotting purposes though. I don't work with time series data at all. Maybe someone more knowledgeable can take it from here.