Added legend_loc parameter to plot function

[mittal-aakriti]

• closes BUG: df.plot - Changing the legend does not work for shared_y #20183 Added 'legend_loc' parameter to the plot function in pandas that calls matplotlib's legend loc internally.
• Tests added and passed. Fixing a bug or adding a new feature
• All code checks passed.
• Added type annotations to new arguments.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

[mroeschke]

Could you link the associated issue in #62568 (comment)?

[mroeschke]

Could you remove these commented out lines?

[mittal-aakriti]

Updated

[jbrockmendel]

Is there an alternative where the user does this in post-processing? I.e. can we make this Not Our Problem?

[mittal-aakriti]

The alternative is to use the ax object created by matplotlib and then change the legend location but the problem arises when we call secondary_y. Pandas internally calls matplotlib's twinx and attaches it to ax as:

• ax, ax.right_ax: When there are few columns on secondary_y and not all. In this case you need to get the legend handles for both ax and ax.right_ax, append them and then change their location. Something like the following:

h1, l1 = ax.get_legend_handles_labels()
h2, l2 = ax.right_ax.get_legend_handles_labels()
ax.legend(h1 + h2, l1 + l2, loc='upper center')

• ax, ax.left_ax: When all the columns are on secondary_y. In order to set the legend location correctly in this case, we will have to follow following steps:

While plotting turn off the legend by (legend=False) and then set ax.legend(loc='upper..')
However if we don't turn off the legend then the legend will be overwritten instead of changing the location

leg = ax.left_ax.get_legend()
leg.set_loc('upper center')

So just giving the legend location legend_loc as the parameter initially solves all these problems in a non-hacky way.

[github-actions]

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[mittal-aakriti]

@mroeschke @jbrockmendel any thoughts on this? Please let me know if more changes are needed. Thanks