Sometimes we want to get a feel for a large dataset with many samples beyond knowing just the basic metrics of mean, median, or standard deviation. To get more of an intuitive sense for a dataset, we can use a histogram to display all the values.

A histogram tells us how many values in a dataset fall between different sets of numbers (i.e., how many numbers fall between 0 and 10? Between 10 and 20? Between 20 and 30?). Each of these questions represents a bin, for instance, our first bin might be between 0 and 10.

All bins in a histogram are always the same size. The *width* of each bin is the distance between the minimum and maximum values of each bin. In our example, the width of each bin would be 10.

Each bin is represented by a different rectangle whose height is the number of elements from the dataset that fall within that bin.

Here is an example:

To make a histogram in Matplotlib, we use the command `plt.hist`

. `plt.hist`

finds the minimum and the maximum values in your dataset and creates 10 equally-spaced bins between those values.

The histogram above, for example, was created with the following code:

plt.hist(dataset) plt.show()

If we want more than 10 bins, we can use the keyword `bins`

to set how many bins we want to divide the data into.
The keyword `range`

selects the minimum and maximum values to plot. For example, if we wanted to take our data from the last example and make a new histogram that just displayed the values from 66 to 69, divided into 40 bins (instead of 10), we could use this function call:

plt.hist(dataset, range=(66,69), bins=40)

which would result in a histogram that looks like this:

Histograms are best for showing the shape of a dataset. For example, you might see that values are close together, or skewed to one side. With this added intuition, we often discover other types of analysis we want to perform.

### Instructions

**1.**

We’ve provided data in the file **sales_times.csv** and loaded it into a list called `sales_times`

. You can see how we did this in the **script.py** file. This set represents the 270 sales at MatplotSip’s first location from 8am to 10pm on a certain day.

**2.**

Make a histogram out of this data in **histogram.py** using the `plt.hist`

function.

**3.**

Use the `bins`

keyword to create 20 bins instead of the default 10.