["python","pandas","matplotlib","time-series","seaborn"]
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()