annotate() method for even further annotation customization: # AnnotateĪrrowprops=dict(headwidth=10, width=4, color='#363d46', connectionstyle="angle3,angleA=0,angleB=-90"), You can also use an Axes object's native. You can add these components using the Axes object. For example, you could add a vertical line around the most recent holiday with text exclaiming that this is the holiday season. One way of doing this is by adding vertical and/or horizontal lines and supplementary text to your visualization. To give your data consumers the proper context when they view this visualization, add annotations to the data. You can see that there are noticeable dips in order volume around the holiday season. You can then apply assorted matplotlib styling to the visualization: # DespineĪx.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on")Īx.axhline(y=val, linestyle='dashed', alpha=0.3, color='#eeeeee', zorder=1)Īx.t_major_formatter(StrMethodFormatter(''))Īx.set_ylabel("Orders", labelpad=20, weight='bold')Īt this point, you will be returned the following visualization: To create a line chart of daily orders, you will use pandas plot() method: ax = df.plot(color='#334f8d', figsize=(11,8), fontsize=11, zorder=2) Now that we have our dataset prepared, we are ready to visualize the data. You can use the following line of Python to access the results of your SQL query as a dataframe and assign them to a new variable: df = datasetsīefore visualizing the data, set the index of the dataframe to be the date column: df = df.set_index('date')
Mode automatically pipes the results of your SQL queries into a pandas dataframe assigned to the variable datasets.
Inside of the Python notebook, let’s start by importing the Python modules that you'll be using throughout the remainder of this recipe: import numpy as npįrom matplotlib.ticker import StrMethodFormatter Now that you have your data wrangled, you’re ready to move over to the Python notebook to prepare your data for visualization. Once the SQL query has completed running, rename your SQL query to Orders so that you can easily identify it within the Python notebook. Using the schema browser within the editor, make sure your data source is set to the Mode Public Warehouse data source and run the following query to wrangle your data: select * For this example, you’ll be using the daily_orders dataset available in Mode's Public Data Warehouse. You’ll use SQL to wrangle the data you’ll need for our analysis. You can find implementations of all of the steps outlined below in this example Mode report. The steps in this recipe are divided into the following sections: In this example, you're going to be applying annotations to a visualization of daily orders for a fictitious e-commerce store to provide context to a dip in daily order volume.
#ANNOTATE TEXT MATPLOTLIB HOW TO#
In this recipe, you'll learn how to apply supplementary text and annotations to a python matplotlib visualization. In situations like this, it's common practice to enrich a data visualization with annotations in order to answer the inevitable “What happened here?” question. For example, maybe there was a significant dip in traffic to your website on a certain day. When creating data visualizations, it's often useful to provide supplementary context to the data being shown.
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