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I have several pandas dataframes. I want to plot several columns against one another in separate scatter plots, and combine them as subplots in a figure. I want to label each subplot accordingly. I had a lot of trouble with getting subplot labels working, until I discovered that there are two ways of plotting directly from dataframes, as far as I know; see SO and pandasdoc:

ax0 = plt.scatter(df.column0, df.column5)
type(ax0): matplotlib.collections.PathCollection

and

ax1 = df.plot(0,5,kind='scatter')
type(ax1): matplotlib.axes._subplots.AxesSubplot

ax.set_title('title') works on ax1 but not on ax0, which returns AttributeError: 'PathCollection' object has no attribute 'set_title'

I don't understand why the two separate ways exist. What is the purpose of the first method using PathCollections? The second one was added in 17.0; is the first one obsolete or has it a different purpose?

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    Since this may be useful to anyone visiting the question, I have found df.plot(0,5,style='.') to work better than df.plot(0,5,kind='scatter') since the former will work after using groupby() whereas the latter does not Commented Mar 13, 2018 at 22:33

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As you have found, the pandas function returns an axes object. The PathCollection object can be interpreted as an axes object as well using the "get current axes" function. For instance:

plot = plt.scatter(df.column0, df.column5)
ax0 = plt.gca()
type(ax0)

< matplotlib.axes._subplots.AxesSubplot at 0x10d2cde10>

A more standard way you might see this is the following:

fig = plt.figure()
ax0 = plt.add_subplot()
ax0.scatter(df.column0, df.column5)

At this point you are welcome to do "set" commands such as your set_title.

Hope this helps.

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The difference between the two is that they are from different libraries. The first one is from matplotlib, the second one from pandas. They do the same, which is create a matplotlib scatter plot, but the matplotlib version returns a collection of points, whereas the pandas version returns a matplotlib subplot. This makes the matplotlib version a bit more versatile, as you can use the collection of points in another plot.

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