Python Tutorial: Data formats

Want to learn more? Take the full course at https://learn.datacamp.com/courses/interactive-data-visualization-with-bokeh at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that we have some practice using basic glyph methods, let's take a closer look at what kinds of data formats can be easily used to drive glyph properties. We have already seen that standard Python lists work just fine, and can be passed as a value for any glyph property. Another common possibility is NumPy arrays. NumPy is a python library for dealing with multi-dimensional arrays of data efficiently. It is the foundation of the scientific python stack, and many of the powerful python packages for analytics and data science rely on it directly or indirectly. Here we use the NumPy function "linspace" to create an array of 1000 values evenly spaced between zero and ten. These will be our "x-values" Then we use the NumPy "sin" and "random" functions to compute a noisy sin curve based on the x-values. Just like with python lists, these NumPy arrays can passed directly to the circle glyph method to specify the x and y coordinates. Another common Python library for data science is Pandas. Panda provides a DataFrame structure (analogous to the R data frame) that is efficient for working with tabular data sets and time series. A Pandas dataframe is comprised of several columns, each with a unique name. The columns in a dataframe are accessed using square brackets, similar to a Python dictionary. As it happens, the underlying implementation of Pandas columns uses NumPy arrays, so passing Pandas DataFrame columns works exactly as you would expect. In this example we have loaded the "flowers" data set, which contains measurements of three different species of iris flowers, as a pandas dataframe. We can show a scatter plot of petal length vs sepal length by passing the columns for petal_length and sepal_length to the circle function.