Beyond summary statistics

Visualizing Time Series Data in Python

Thomas Vincent

Head of Data Science, Getty Images

Facet plots of the jobs dataset

jobs.plot(subplots=True,
            layout=(4, 4),
            figsize=(20, 16),
            sharex=True,
            sharey=False)

plt.show()

Facet plots of the jobs dataset

Annotating events in the jobs dataset

ax = jobs.plot(figsize=(20, 14), colormap='Dark2')

ax.axvline('2008-01-01', color='black', 
            linestyle='--')
ax.axvline('2009-01-01', color='black', 
            linestyle='--')

Facet plots of the jobs dataset

Taking seasonal average in the jobs dataset

print(jobs.index)

DatetimeIndex(['2000-01-01', '2000-02-01', '2000-03-01', 
'2000-04-01', '2009-09-01','2009-10-01', 
'2009-11-01', '2009-12-01','2010-01-01', '2010-02-01'], 
dtype='datetime64[ns]', name='datestamp',
length=122, freq=None)

index_month = jobs.index.month
jobs_by_month = jobs.groupby(index_month).mean()
print(jobs_by_month)

datestamp    Agriculture  Business services  Construction                                        
1            13.763636           7.863636     12.909091   
2            13.645455           7.645455     13.600000   
3            13.830000           7.130000     11.290000   
4             9.130000           6.270000      9.450000   
5             7.100000           6.600000      8.120000   
...

Monthly averages in the jobs dataset

ax = jobs_by_month.plot(figsize=(12, 5),
colormap='Dark2')

ax.legend(bbox_to_anchor=(1.0, 0.5), 
loc='center left')

Monthly averages in the jobs dataset

Time to practice!

Visualizing Time Series Data in Python