Pandas dataframe groupby plot

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?

2 Answers

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()

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)

If you want a chart with individual subplots:

grouped = df.groupby('ticker')
ncols=2
nrows = int(np.ceil())
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()

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: