avoid displaying data in plt.hist python jupyter notebook

We need to serialize our ticker list into a separated string of tickers. The IEX Cloud API accepts tickers separated by commas. Next, we're going to store our list of tickers in a Python list: tickers = [ Storing your own personalized API key in a variable called IEX API Key will be enough for you to follow along. The first thing that you'll need to do is create an IEX Cloud account and generate an API token.įor obvious reasons, I'm not going to be publishing my API key in this article. Specifically, we will be visualizing historical stock prices for a few of the largest banks in the US: In this tutorial, we are going to be using stock market data from the IEX Cloud API. The best data set candidates for auto-updating visualizations are time series data where new observations are being added on a regular basis (say, each day). Writing a Python script that automatically updates a chart of Michael Jordan's annual points-per-game would be useless - his career is over, and that data set is never going to change. To be more specific, there is no need for your visualizations to update automatically if the data they are presenting does not change over time. But before you invest the time in building them, it is important to understand whether or not you need your charts to be automatically updated.

Step 1: Gather Your DataĪutomatically updating charts sound appealing. We'll use data from IEX Cloud and we'll also use the matplotlib library and some simple Amazon Web Services product offerings. In this tutorial, I will teach you how to create automatically-updating Python visualizations. Developers creating visualizations must accept more technical complexity in exchange for vastly more input into how their visualizations look.

However, working with a raw programming language like Python (instead of more sophisticated software like, say, Tableau) presents some challenges. If the bins are spaced out at 15 minute intervals, it makes sense to label the x-axis at these same intervals.Python is an excellent programming language for creating data visualizations. plt.xticks() defines the location of the x-axis tick labels. The lines plt.xlabel(), plt.ylabel(), and plt.title() give our histogram axis labels and a title. We'll also specify density=False, color='b'(blue), edgecolor='k'(black), and alpha=0.5(half transparent). Let's specify our bins in 15 min increments. Type of histogram: bar, stacked, step or step-filled A table of some keyword arguments used with plt.hist() is below: keyword argument Let's also add some axis labels and a title to the histogram. If we want our bins to have specific bin ranges, we can specify a list or array of bin edges in the keyword argument bins=.

Avoid displaying data in plt.hist python jupyter notebook