Plotting pandas timedelta

You can plot nice histograms using the numpy timedelta data types.

For example:

df['time_delta'].astype('timedelta64[s]').plot.hist()

will produce a histogram of the time deltas in seconds. To use minutes instead, you could do this:

(df['time_delta'].astype('timedelta64[s]') / 60).plot.hist()

or use [m] timedelta.

df['time_delta'].astype('timedelta64[m]').plot.hist()

Here's list of other time delta types (from the docs) you might want, depending on the resolution you need:

Code    Meaning Time span (relative)    Time span (absolute)
h   hour    +/- 1.0e15 years    [1.0e15 BC, 1.0e15 AD]
m   minute  +/- 1.7e13 years    [1.7e13 BC, 1.7e13 AD]
s   second  +/- 2.9e11 years    [2.9e11 BC, 2.9e11 AD]
ms  millisecond +/- 2.9e8 years [ 2.9e8 BC, 2.9e8 AD]
us  microsecond +/- 2.9e5 years [290301 BC, 294241 AD]
ns  nanosecond  +/- 292 years   [ 1678 AD, 2262 AD]
ps  picosecond  +/- 106 days    [ 1969 AD, 1970 AD]
fs  femtosecond +/- 2.6 hours   [ 1969 AD, 1970 AD]
as  attosecond  +/- 9.2 seconds [ 1969 AD, 1970 AD]

How about

df['time_delta'].dt.days.hist()

...? (Where you can use seconds, microseconds, or nanoseconds instead of days depending on your needs / your data).

Here are ways to convert timedeltas, docs are here

In [2]: pd.to_timedelta(np.arange(5),unit='d')+pd.to_timedelta(1,unit='s')
Out[2]: 
0   0 days, 00:00:01
1   1 days, 00:00:01
2   2 days, 00:00:01
3   3 days, 00:00:01
4   4 days, 00:00:01
dtype: timedelta64[ns]

Convert to seconds (is an exact conversion)

In [3]: (pd.to_timedelta(np.arange(5),unit='d')+pd.to_timedelta(1,unit='s')).astype('timedelta64[s]')
Out[3]: 
0         1
1     86401
2    172801
3    259201
4    345601
dtype: float64

Convert using astype will round to that unit

In [4]: (pd.to_timedelta(np.arange(5),unit='d')+pd.to_timedelta(1,unit='s')).astype('timedelta64[D]')
Out[4]: 
0    0
1    1
2    2
3    3
4    4
dtype: float64

Division will give an exact repr

In [5]: (pd.to_timedelta(np.arange(5),unit='d')+pd.to_timedelta(1,unit='s')) / np.timedelta64(1,'D')
Out[5]: 
0    0.000012
1    1.000012
2    2.000012
3    3.000012
4    4.000012
dtype: float64

Another method (that worked for me) is to simply divide by a Timedelta :

plt.hist(df['time_delta']/pd.Timedelta(minutes=1), bins=20)