Another key metric that we can use in data analysis is the *median*. The median is the middle value of a dataset that’s been ordered in terms of magnitude (from lowest to highest).

Let’s look at the following array:

np.array( [1, 1, 2, 3, 4, 5, 5])

In this example, the median would be **3**, because it is positioned half-way between the minimum value and the maximum value.

If the length of our dataset was an even number, the median would be the value halfway between the two central values. So in the following example, the median would be **3.5**:

np.array( [1, 1, 2, 3, 4, 5, 5, 6])

But what if we had a very large dataset? It would get very tedious to count all of the values. Luckily, NumPy also has a function to calculate the median, `np.median`

:

>>> my_array = np.array([50, 38, 291, 59, 14]) >>> np.median(my_array) 50.0

### Instructions

**1.**

You’re doing some research on household incomes and come across the following small dataset:

10100, 35500, 105000, 85000, 25500, 40500, 65000

Calculate the median, **without using Numpy**, and save the value to the variable `small_set_median`

.

**2.**

As you continue your research, you come across a trove of research in the file **household_income.csv**, which we’ve already included in your program and saved as `large_set`

.

Use NumPy to find the median of `large_set`

and save the result to the variable `large_set_median`

.

**3.**

Print the results of `small_set_median`

and `large_set_median`

to the terminal.