The world population data spans from $1960$ to $2017.$ We'd like to build a predictive model that can give us the best guess at what the population growth rate in a given year might be$.$ We will calculate the population growth rate as follows:$-$

$Growt{h}_r$ate $=\frac{curren{t}_{y}ea{r}_{p}opulatio{n}_{p}reviou{s}_{y}ea{r}_p\text{o}\text{p}\text{u}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}}{previou{s}_{y}ea{r}_p\text{o}\text{p}\text{u}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}}$

As such, we can only calculate the growth rate for the year $1961$ onwards.

Write a function that takes the population$\_$df and a country$\_$code as input and computes the population growth rate for a given country starting from the year $1961.$ This function must retum a return a $2-$d numpy array that contains the year and corresponding growth rate for the country.

Function Specifications:

Should take a population$\_$df and country$\_$code string as input and retum a numpy array as output.

The array should only have two columns containing the year and the population growth rate, in other words, it should have a shape $(?,2)$ where $?$ is the length of the data.