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?

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(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()

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