Plot aggregates of your data

Trực quan hóa dữ liệu chuỗi thời gian trong 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!

Trực quan hóa dữ liệu chuỗi thời gian trong Python