A bin is like a slot on the graph that holds a range of data. To display a histogram we can use the matplotlib. The histogram can be used to display a distribution of data. Plt.bar(x2, y2, label='Second Bars', color='black') plt.bar(x, y, label='First Bars', color='red') import matplotlib.pyplot as pltĪlso, note that you can specify the color of the bars within the. We want them to be side by side for comparison sake, and this step accomplishes that. We need to do this so that the bars do not overlap with each other. Also notice that we used odd numbers now for the first x variable and even numbers for the x2 variable. In the following code, we add a second set of data using the x2 and y2 variables. We can plot more than one set of data using the bar chart, just like we did with line graphs. import matplotlib.pyplot as pltĭisplaying a bar chart using matplotlib is done with the. This is a keyword argument of label, so that the legend displays properly. When adding a legend, it is also important to note that you need to add a third argument to the plot() function. If you have more than one line on the graph, how do you know which line represents what? This is what you use a legend for. import matplotlib.pyplot as pltĪ legend is useful when there is more than one sequence of data being plotted. To begin, the following code adds both an X and Y label, as well as a title to the graph. It’s a good idea to have a title for your graph, labels for the x and y axes, and a legend that explains what the data is. Now we can talk about three more important aspects of matplotlib. At this point, we are ready to display the plot and this is done using plt.show(). These sequences should always be of equal length. The first list is x and the second list is y. In this example, we simply pass two Python lists. This function has a number of possible parameters, but the key thing to know is that you must pass it an x and a y value. This is a common convention to import and alias to plt. The code above first imports matplotlib using import matplotlib.pyplot as plt. The following code shows how to start with a very simple line graph using the x and y-axis. The line graph is kind of the “hello world” of matplotlib. Now launch your Jupyter notebook by simply typing jupyter notebook at the command prompt. To do so, navigate to the command prompt and type pip install matplotlib. Make sure you first have Jupyter notebook installed, then we can add Matplotlib to our virtual environment. Matplotlib in combination with Jupyter Notebook is a popular way to visualize data using Python for all kinds of applications in science, technology, and education. This means that pyplot has many functions to make changes to a figure. Matplotlib.pyplot provides a MATLAB-like way of plotting. In this tutorial, we’ll learn a little bit about matplotlib and how to use it in Jupyter Notebook. The module in matplotlib that is used is called pyplot. Matplotlib is a Python library that is used often with Jupyter Notebook.
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