matplotlib.pyplot: how to include custom legends when plotting dataframes?

Question

I am plotting two dataframes in the same chart: the USDEUR exchange rate and the 3-day moving average.

df.plot(ax=ax, linewidth=1)
rolling_mean.plot(ax=ax, linewidth=1)

Both dataframes are labelled "Value" so I would like to customize that:

I tried passing the label option but that didn't work, as it seems that this option is exclusive to matplotlib.axes.Axes.plot and not to pandas.DataFrame.plot. So I tried using axes instead, and passing each label:

ax.plot(df, linewidth=1, label='FRED/DEXUSEU')
ax.plot(rolling_mean, linewidth=1, label='3-day SMA')

However now the legend is not showing up at all unless I explicitly call ax.legend() afterwards.

Is it possible to plot the dataframes while passing custom labels without the need of an additional explicit call?

Answer 1

When setting a label using df.plot() you have to specifiy the data which is being plotted:

fig, (ax1, ax2) = plt.subplots(1,2)

df = pd.DataFrame({'Value':np.random.randn(10)})
df2 = pd.DataFrame({'Value':np.random.randn(10)})

df.plot(label="Test",ax=ax1)
df2.plot(ax=ax1)

df.plot(y="Value", label="Test",ax=ax2)
df2.plot(y="Value", ax=ax2)

ax1.set_title("Reproduce problem")
ax2.set_title("Possible solution")

plt.show()

Which gives:

Update: It appears that there is a difference between plotting a dataframe, and plotting a series. When plotting a dataframe, the labels are taken from the column names. However, when specifying y="Value" you are then plotting a series, which then actually uses the label argument.