Plotting distribution of time data in Python using Pandas

Question

I have a pandas dataframe with some time data which looks like

0    08:00 AM
1    08:15 AM
2    08:30 AM
3     7:45 AM
4     7:30 AM

There are 660 rows like these in total (datatype- String). I want to plot the distribution(histogram) of this column. How can I do that? Also some of the rows are just an empty strings (missing data), so I have to also handle that while plotting. What can be the best way to handle that?

I have tried to use pandas.to_datetime() to convert string to timestamp, but still after that I am stuck on how to plot distribution of those timestamps and missing data.

Answer 1

Let's assume you have the dataframe you're talking about, and you're able to cast as pandas datetime objects:

import pandas as pd
df = pd.DataFrame(['8:00 AM', '8:15 AM', '08:30 AM', '', '7:45 AM','7:45 AM'], columns = ['time'])

df.time = pd.to_datetime(df.time)

df looks like this:

time
0   2019-08-16 08:00:00
1   2019-08-16 08:15:00
2   2019-08-16 08:30:00
3   NaT
4   2019-08-16 07:45:00
5   2019-08-16 07:45:00

I would groupby both hour and minute .

df.groupby([df['time'].dt.hour, df['time'].dt.minute]).count().plot(kind="bar")

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