Plot multiple time series

plot_utils.plot_multiple_timeseries(multiple_time_series, show_legend=True, fig=None, ax=None, figsize=(10, 3), dpi=100, ncol_legend=5, **kwargs)[source]

Plot multiple time series.

Note that setting keyword arguments such as color or linestyle will force all time series to have the same color or line style. So we recommend letting this function generate distinguishable line specifications (color/ linestyle/linewidth combinations) by itself. (Although the more time series, the less the distinguishability. 240 time series or less is recommended.)

Parameters:

multiple_time_series (pandas.DataFrame or pandas.Series) – If it is a pandas DataFrame, its index is the date, and each column is a different time series. If it is a pandas Series, it will be internally converted into a 1-column pandas DataFrame.
fig (matplotlib.figure.Figure or None) – Figure object. If None, a new figure will be created.
ax (matplotlib.axes._subplots.AxesSubplot or None) – Axes object. If None, a new axes will be created.
figsize ((float, float)) – Figure size in inches, as a tuple of two numbers. The figure size of fig (if not None) will override this parameter.
dpi (float) – Figure resolution. The dpi of fig (if not None) will override this parameter.
ncol_legend (int) – Number of columns of the legend.
**kwargs – Other keyword arguments to be passed to plot_timeseries(), such as color, marker, fontsize, alpha, etc.

Returns:

fig (matplotlib.figure.Figure) – The figure object being created or being passed into this function.
ax (matplotlib.axes._subplots.AxesSubplot) – The axes object being created or being passed into this function.