[python] Plot different DataFrames in the same figure

I have a temperature file with many years temperature records, in a format as below:

2012-04-12,16:13:09,20.6
2012-04-12,17:13:09,20.9
2012-04-12,18:13:09,20.6
2007-05-12,19:13:09,5.4
2007-05-12,20:13:09,20.6
2007-05-12,20:13:09,20.6
2005-08-11,11:13:09,20.6
2005-08-11,11:13:09,17.5
2005-08-13,07:13:09,20.6
2006-04-13,01:13:09,20.6

Every year has different numbers, time of the records, so the pandas datetimeindices are all different.

I want to plot the different year's data in the same figure for comparing . The X-axis is Jan to Dec, the Y-axis is temperature. How should I go about doing this?

To do this for multiple dataframes, you can do a for loop over them:

fig = plt.figure(num=None, figsize=(10, 8))
ax = dict_of_dfs['FOO'].column.plot()
for BAR in dict_of_dfs.keys():
    if BAR == 'FOO':
        pass
    else:
        dict_of_dfs[BAR].column.plot(ax=ax)

If you a running Jupyter/Ipython notebook and having problems using;

ax = df1.plot()

df2.plot(ax=ax)

Run the command inside of the same cell!! It wont, for some reason, work when they are separated into sequential cells. For me at least.

Just to enhance @adivis12 answer, you don't need to do the if statement. Put it like this:

fig, ax = plt.subplots()
for BAR in dict_of_dfs.keys():
    dict_of_dfs[BAR].plot(ax=ax)

Try:

ax = df1.plot()
df2.plot(ax=ax)