# Plotting

## Overview

Teaching:5 min

Exercises:0 minQuestions

How can I plot my data?

Objectives

Create a time series plot showing a single data set.

Create a scatter plot showing relationship between two data sets.

`matplotlib`

is the most widely used scientific plotting library in Python.

- Commonly use a sub-library called
`matplotlib.pyplot`

. - The Jupyter Notebook will render plots inline if we ask it to using a “magic” command.

```
%matplotlib inline
import matplotlib.pyplot as plt
```

- Simple plots are then (fairly) simple to create.

```
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y)
plt.xlabel('Numbers')
plt.ylabel('Doubles')
```

## Plot data directly from a Pandas dataframe.

- We can also plot Pandas dataframes.
- This implicitly uses
`matplotlib.pyplot`

.

```
import pandas
data = pandas.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')
data.ix['Australia'].plot()
plt.xticks(rotation=90)
```

## Select and transform data, then plot it.

- By default,
`DataFrame.plot`

plots with the rows as the X axis. - We can transpose the data in order to plot multiple series.

```
data.T.plot()
plt.ylabel('GDP per capita')
plt.xticks(rotation=90)
```

## Many styles of plot are available.

- For example, do a bar plot using a fancier style.

```
plt.style.use('ggplot')
data.T.plot(kind='bar')
plt.xticks(rotation=90)
plt.ylabel('GDP per capita')
```

- Extract years from the last four characters of the columns’ names.
- Store these in a list using the Accumulator pattern.

- Can also convert dataframe data to a list.

```
# Accumulator pattern to collect years (as character strings).
years = []
for col in data.columns:
year = col[-4:]
years.append(year)
# Australia data as list.
gdp_australia = data.ix['Australia'].tolist()
# Plot: 'b-' sets the line style.
plt.plot(years, gdp_australia, 'b-')
```

## Can plot many sets of data together.

```
# Accumulator pattern to collect years (as character strings).
years = []
for col in data.columns:
year = col[-4:]
years.append(year)
# Select two countries' worth of data.
gdp_australia = data.ix['Australia']
gdp_nz = data.ix['New Zealand']
# Plot with differently-colored markers.
plt.plot(years, gdp_australia, 'b-', label='Australia')
plt.plot(years, gdp_nz, 'g-', label='New Zealand')
# Create legend.
plt.legend(loc='upper left')
plt.xlabel('Year')
plt.ylabel('GDP per capita ($)')
```

- Plot a scatter plot correlating the GDP of Australia and New Zealand
- Use either
`plt.scatter`

or`DataFrame.plot.scatter`

```
plt.scatter(gdp_australia, gdp_nz)
```

```
data.T.plot.scatter(x = 'Australia', y = 'New Zealand')
```

## Minima and Maxima

Fill in the blanks below to plot the minimum GDP per capita over time for all the countries in Europe. Modify it again to plot the maximum GDP per capita over time for Europe.

`data_europe = pandas.read_csv('data/gapminder_gdp_europe.csv') data_europe.____.plot(label='min') data_europe.____ plt.legend(loc='best')`

## Correlations

Modify the example in the notes to create a scatter plot showing the relationship between the minimum and maximum GDP per capita among the countries in Asia for each year in the data set. What relationship do you see (if any)?

`data_asia = pandas.read_csv('gapminder_gdp_asia.csv') data_asia.describe().T.plot(kind='scatter', x='min', y='max')`

You might note that the variability in the maximum is much higher than that of the minimum. Take a look at the maximum and the max indexes:

`data_asia = pandas.read_csv('gapminder_gdp_asia.csv') data_asia.max().plot() print(data_asia.idxmax()) print(data_asia.idxmin())`

## More Correlations

This short programs creates a plot showing the correlation between GDP and life expectancy for 2007, normalizing marker size by population:

`data_all = pandas.read_csv('gapminder_all.csv') data_all.plot(kind='scatter', x='gdpPercap_2007', y='lifeExp_2007', s=data_all['pop_2007']/1e6)`

Using online help and other resources, explain what each argument to

`plot`

does.

## Key Points

`matplotlib`

is the most widely used scientific plotting library in Python.Plot data directly from a Pandas dataframe.

Select and transform data, then plot it.

Many styles of plot are available.

Can plot many sets of data together.