# Lesson 22

Plotting in notebooks requires using a magic command, which starts with `%`

,
to initialize the plotting backend.

```
# Setup
%matplotlib inline
```

Begin by loading the data in hf.h5. This data set contains high-frequency price data for IBM and MSFT on a single day stored as two Series. IBM is stored as "IBM" in the HDF file, and MSFT is stored as "MSFT.

```
```

### Problem: Histogram¶

Produce a histogram of MSFT 1-minute returns (Hint: you have to produce
the 1-minute Microsoft returns first using `resample`

and `pct_change`

).

```
```

### Problem: Scatter Plot¶

Scatter the 5-minute MSFT returns against the 5-minute IBM returns.

*Hint*: You will need to create both 5-minute return series, merge them,
and then plot using the combined DataFrame.

```
```

### Problem: Saving plots¶

Save the previous plot to PNG and PDF.

```
```

```
```

#### Exercise: Export the result of the previous exercise to JPEG and PDF¶

```
```

### Exercise: Plot histograms and a scatter plot¶

Produce a 2 by 2 subplot with:

- Create a square figure with a size of 10 by 10 using
`plt.rc`

- Histograms of IBM and MSFT on the diagonals
- Scatter plots on the off-diagonals where the x and y line up with the histogram on the diagonal.
- Set the limits of the scatter plots to match the appropriate histogram x and y limit.
- Clean up the plot using
`tight_layout`

```
```

#### Exercise: Use pandas plotting tools¶

Use `pandas.plotting.scatter_matrix`

to produce a similar plot to the previous exercise.

```
```