Pandas returns the following horizontal bar chart using the default settings: We can specify that we would like a horizontal bar chart by passing barh to the kind argument: x.plot(kind=‘barh’) To create a horizontal bar chart, we will use pandas plot() method. Now that we have our dataset aggregated, we are ready to visualize the data. We now have a new dataframe assigned to the variable x that contains the top 15 start stations with the highest average trip durations. You can analyze the dataframe to find these stations using the following method chain on our existing dataframe object: x = df.groupby('start_station_name').mean().sort_values().tail(15) 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 = datasetsĪs previously mentioned, your goal is to visualize the 15 start stations with the highest average trip duration. Mode automatically pipes the results of your SQL queries into a pandas dataframe assigned to the variable datasets. Inside of the Python notebook, start by importing the Python modules that you'll be using throughout the remainder of this recipe: import pandas as pdįrom 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 SF Bike Share Trip Rankings 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 sf_bike_share_trips 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: You will then visualize these average trip durations using a horizontal bar chart. In our example, you'll be using the publicly available San Francisco bike share trip dataset to identify the top 15 bike stations with the highest average trip durations. Specifically, you’ll be using pandas plot() method, which is simply a wrapper for the matplotlib pyplot API. This recipe will show you how to go about creating a horizontal bar chart using Python. On the other hand, when grouping your data by a nominal variable, or a variable that has long labels, you may want to display those groupings horizontally to aid in readability. For example, when grouping your data by an ordinal variable, you may want to display those groupings along the x-axis. While there are no concrete rules, there are quite a few factors that can go into making this decision. Let-s consider an example to modify the color and to change the format of the text.Often when visualizing data using a bar chart, you’ll have to make a decision about the orientation of your bars. Note: All the extra parameters should be passed within the square brackets before the \pie command.
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