Pandas dataframe groupby plot

Question

I have a dataframe which is structured as:

          Date   ticker  adj_close  0   2016-11-21     AAPL    111.730      1   2016-11-22     AAPL    111.800     2   2016-11-23     AAPL    111.230     3   2016-11-25     AAPL    111.790      4   2016-11-28     AAPL    111.570     ...           8   2016-11-21      ACN    119.680             9   2016-11-22      ACN    119.480               10  2016-11-23      ACN    119.820               11  2016-11-25      ACN    120.740  ...              

How can I plot based on the ticker the adj_close versus Date?

Answer 1

Simple plot,

you can use:

df.plot(x='Date',y='adj_close') 

Or you can set the index to be Date beforehand, then it's easy to plot the column you want:

df.set_index('Date', inplace=True) df['adj_close'].plot() 

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If you want a chart with one series by ticker on it

You need to groupby before:

df.set_index('Date', inplace=True) df.groupby('ticker')['adj_close'].plot(legend=True) 

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If you want a chart with individual subplots:

grouped = df.groupby('ticker')  ncols=2 nrows = int(np.ceil(grouped.ngroups/ncols))  fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12,4), sharey=True)  for (key, ax) in zip(grouped.groups.keys(), axes.flatten()):     grouped.get_group(key).plot(ax=ax)  ax.legend() plt.show() 

Answer 2

Similar to Julien's answer above, I had success with the following:

fig, ax = plt.subplots(figsize=(10,4)) for key, grp in df.groupby(['ticker']):     ax.plot(grp['Date'], grp['adj_close'], label=key)  ax.legend() plt.show() 

This solution might be more relevant if you want more control in matlab.

Solution inspired by: https://stackoverflow.com/a/52526454/10521959