Python Tutorial : Using pandas with Seaborn

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-data-visualization-with-seaborn at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Data scientists commonly use Pandas to perform data analysis, so it's a huge advantage that Seaborn works extremely well with Pandas data structures. Let's see how this works! Pandas is a python library for data analysis. It can easily read datasets from many types of files including csv and txt files. Pandas support several types of data structures, but the most common one is the DataFrame object. When you read in a dataset with Pandas, you will create a DataFrame. Let's look at an example. First, import the Pandas library as "pd". Then, use the "read_csv" function to read the csv file named "masculinity dot csv" and create a Pandas DataFrame called "df". Calling "head" on the DataFrame will show us its first five rows. This dataset contains the results of a survey of adult men. We can see that it has four columns: "participant_id"; "age"; "how_masculine", which is that person's response to the question "how masculine or 'manly' do you feel?"; and "how_important", which is the response to the question "how important is it to you that others see you as masculine?" Now let's look at how to make a count plot with a DataFrame instead of a list of data. The first thing we'll do is import Pandas, Matplotlib and Seaborn as we have in past examples. Then, we'll create a Pandas DataFrame called "df" from the masculinity csv file. To create a count plot with a Pandas DataFrame column instead of a list of data, set x equal to the name of the column in the DataFrame - in this case, we'll use the "how_masculine" column. Then, we'll set the data parameter equal to our DataFrame, "df". After calling "plt dot show", we can see that we have a nice count plot of the values in the "how_masculine" column of our data. This plot shows u