You can read more about this on the guide to working with pandas. Usually, I use some pandas functions to fix some data issues like null values and add information to the data set that may be helpful. I like to print the first few rows of the data set to get a feeling of the columns and the data itself. In our case, we will use the dataset “tips” that you can download directly using seaborn. We know the basics of seaborn, now let’s get them into practice by building multiple charts over the same dataset. Sns.lineplot(data = data, x = "year", y = "passengers") Seaborn gives you the ability to change your graphs’ interface, and it provides five different styles out of the box: darkgrid, whitegrid, dark, white, and ticks. Sns.barplot(x="country", y="beer_servings", data=drinks_df) So far, we saw examples of using seaborn with pre-loaded data, but what if we want to draw a plot from data we already have loaded using pandas?ĭrinks_df = pd.read_csv("data/drinks.csv") We already talked about this, but seaborn loves pandas to such an extent that all its functions build on top of the pandas dataframe. We are rendering a seaborn chart in each subplot, mixing matplotlib with seaborn functions. The function takes three parameters, the first is the number of rows, the second is the number of columns, and the last one is the plot number. Using the subplot function, we can draw more than one chart on a single plot. Sns.countplot(x='depth', data=diamonds_data) Sns.countplot(x='carat', data=diamonds_data) Let’s say that you, for example, want to plot multiple graphs simultaneously using seaborn then you could use the subplot function from matplotlib.ĭiamonds_data = sns.load_dataset('diamonds') It can come in handy for specific operations and allows seaborn to leverage the power of matplotlib without having to rewrite all its functions. Any seaborn chart can be customized using functions from the matplotlib library. With that said, it does not limit its capabilities. Seaborn builds on top of matplotlib, extending its functionality and abstracting complexity. It’s very colorful, I know, we will learn how to customize it later on in the guide. Sns.barplot(data=flights_data, x="year", y="passengers") It is probably the best-known type of chart, and as you may have predicted, we can plot this type of plot with seaborn in the same way we do for lines and scatter plots by using the function barplot. Sns.lineplot(data=flights_data, x="year", y="passengers") Similarly to before, we use the function lineplot with the dataset and the columns representing the x and y axis. Seaborn will do the rest. It is a popular and known type of chart, and it’s super easy to produce. This plot draws a line that represents the revolution of continuous or categorical data. Very easy, right? The function scatterplot expects the dataset we want to plot and the columns representing the x and y axis. Sns.scatterplot(data=flights_data, x="year", y="passengers") Creating a scatter plot in the seaborn library is so simple and with just one line of code. All these datasets are available on a GitHub repository.Ī scatter plot is a diagram that displays points based on two dimensions of the dataset. Let’s see how that works by loading a dataset that contains information about flights.įlights_data = sns.load_dataset("flights")Īll the magic happens when calling the function load_dataset, which expects the name of the data to be loaded and returns a dataframe. Even more so, the library comes with some built-in datasets that you can now load from code, no need to manually downloading files. The beauty of seaborn is that it works directly with pandas dataframes, making it super convenient. Let’s then install seaborn, and of course, also the package notebook to get access to our data playground.Īdditionally, we are going to import a few modules before we get started.īefore we can start plotting anything, we need data. When installing seaborn, the library will install its dependencies, including matplotlib, pandas, numpy, and scipy. Installing seaborn is as easy as installing one library using your favorite Python package manager. It abstracts complexity while allowing you to design your plots to your requirements. Seaborn works by capturing entire dataframes or arrays containing all your data and performing all the internal functions necessary for semantic mapping and statistical aggregation to convert data into informative plots. Seaborn design allows you to explore and understand your data quickly. It builds on top of matplotlib and integrates closely with pandas data structures. Seaborn is a library for making statistical graphics in Python.
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