Plotting pandas timedelta

I have a pandas dataframe that has two datetime64 columns and one timedelta64 column that is the difference between the two columns. I'm trying to plot a histogram of the timedelta column to visualize the time differences between the two events.

However, just using df['time_delta'] results in: TypeError: ufunc add cannot use operands with types dtype('<m8[ns]') and dtype('float64')

Trying to convert the timedelta column to : float--> df2 = df1['time_delta'].astype(float) results in: TypeError: cannot astype a timedelta from [timedelta64[ns]] to [float64]

How would one create a histogram of pandas timedelta 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

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).

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

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