Plot aggregates of your data

Visualizing Time Series Data in Python

Thomas Vincent

Head of Data Science, Getty Images

Moving averages

In the field of time series analysis, a moving average can be used for many different purposes:
- smoothing out short-term fluctuations
- removing outliers
- highlighting long-term trends or cycles.

The moving average model

co2_levels_mean = co2_levels.rolling(window=52).mean()

ax = co2_levels_mean.plot()
ax.set_xlabel("Date")
ax.set_ylabel("The values of my Y axis")
ax.set_title("52 weeks rolling mean of my time series")

plt.show()

A plot of the moving average for the CO2 data

Rolling mean of a time series

Computing aggregate values of your time series

co2_levels.index

DatetimeIndex(['1958-03-29', '1958-04-05',...],
              dtype='datetime64[ns]', name='datestamp', 
              length=2284, freq=None)

print(co2_levels.index.month)

array([ 3,  4,  4, ..., 12, 12, 12], dtype=int32)

print(co2_levels.index.year)

array([1958, 1958, 1958, ..., 2001,
      2001, 2001], dtype=int32)

Plotting aggregate values of your time series

index_month = co2_levels.index.month
co2_levels_by_month = co2_levels.groupby(index_month).mean()
co2_levels_by_month.plot()

plt.show()

Plotting aggregate values of your time series

Monthly aggregates of the CO2 levels data

Let's practice!

Visualizing Time Series Data in Python