We can write a new exercise in the inequality lecture to teach the difference in python loops and vectorization.
Here is a starting point for the exercise.
```{exercise}
:label: inequality_ex3
The {ref}`code to compute the Gini coefficient is listed in the lecture above <code:gini-coefficient>`.
This code uses loops to calculate the coefficient based on income or wealth data.
This function can be re-written using vectorization which will greatly improve the computational efficiency when using `python`.
Re-write the function `gini_coefficient` using `numpy` and vectorized code.
You can compare the output of this new function with the one above, and note the speed differences.
```
```{solution-start} inequality_ex3
:class: dropdown
```
Let's take a look at some raw data for the US that is stored in `df_income_wealth`
```{code-cell} ipython3
df_income_wealth.describe()
```
```{code-cell} ipython3
df_income_wealth.head(n=4)
```
We will focus on wealth variable `n_wealth` to compute a Gini coefficient for the year 1990.
```{code-cell} ipython3
data = df_income_wealth[df_income_wealth.year == 2016]
```
```{code-cell} ipython3
data.head(n=2)
```
We can first compute the Gini coefficient using the function defined in the lecture above.
```{code-cell} ipython3
gini_coefficient(data.n_wealth.values)
```
Now we can write a vectorized version using `numpy`
```{code-cell} ipython3
def gini(y):
n = len(y)
y_1 = np.reshape(y, (n, 1))
y_2 = np.reshape(y, (1, n))
g_sum = np.sum(np.abs(y_1 - y_2))
return g_sum / (2 * n * np.sum(y))
```
```{code-cell} ipython3
gini(data.n_wealth.values)
```however this uses a long run time series so it would be better to migrate this to use simulation data that we can control the size and generate in the lecture.
We can write a new exercise in the
inequalitylecture to teach the difference in python loops and vectorization.Here is a starting point for the exercise.
however this uses a long run time series so it would be better to migrate this to use simulation data that we can control the size and generate in the lecture.