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Introduction to Seaborn IN TRODUCTION TO S EABORN Erin Case Data Scientist What is Seaborn? Python data visualization library Easily create the most common types of plots INTRODUCTION TO SEABORN Why is Seaborn useful? INTRODUCTION TO

  1. Introduction to Seaborn IN TRODUCTION TO S EABORN Erin Case Data Scientist

  2. What is Seaborn? Python data visualization library Easily create the most common types of plots INTRODUCTION TO SEABORN

  3. Why is Seaborn useful? INTRODUCTION TO SEABORN

  4. Advantages of Seaborn Easy to use Works well with pandas data structures Built on top of matplotlib INTRODUCTION TO SEABORN

  5. Getting started S amuel N orman S eaborn ( sns ) import seaborn as sns import matplotlib.pyplot as plt "The West Wing" television show INTRODUCTION TO SEABORN

  6. Example 1: Scatter plot import seaborn as sns import matplotlib.pyplot as plt height = [62, 64, 69, 75, 66, 68, 65, 71, 76, 73] weight = [120, 136, 148, 175, 137, 165, 154, 172, 200, 187] sns.scatterplot(x=height, y=weight) plt.show() INTRODUCTION TO SEABORN

  7. Example 2: Create a count plot import seaborn as sns import matplotlib.pyplot as plt gender = ["Female", "Female", "Female", "Female", "Male", "Male", "Male", "Male", "Male", "Male"] sns.countplot(x=gender) plt.show() INTRODUCTION TO SEABORN

  8. INTRODUCTION TO SEABORN

  9. Let's practice! IN TRODUCTION TO S EABORN

  10. Using pandas with Seaborn IN TRODUCTION TO S EABORN Erin Case Data Scientist

  11. What is pandas? Python library for data analysis Easily read datasets from csv, txt, and other types of �les Datasets take the form of DataFrame objects INTRODUCTION TO SEABORN

  12. Working with DataFrames import pandas as pd df = pd.read_csv("masculinity.csv") df.head() participant_id age how_masculine how_important 0 1 18 - 34 Somewhat Somewhat 1 2 18 - 34 Somewhat Somewhat 2 3 18 - 34 Very Not very 3 4 18 - 34 Very Not very 4 5 18 - 34 Very Very INTRODUCTION TO SEABORN

  13. Using DataFrames with countplot() import pandas as pd import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv("masculinity.csv") sns.countplot(x="how_masculine", data=df) plt.show() INTRODUCTION TO SEABORN

  14. INTRODUCTION TO SEABORN

  15. INTRODUCTION TO SEABORN

  16. Let's practice! IN TRODUCTION TO S EABORN

  17. Adding a third variable with hue IN TRODUCTION TO S EABORN Erin Case Data Scientist

  18. Tips dataset import pandas as pd import seaborn as sns tips = sns.load_dataset("tips") tips.head() total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 INTRODUCTION TO SEABORN

  19. A basic scatter plot import matplotlib.pyplot as plt import seaborn as sns sns.scatterplot(x="total_bill", y="tip", data=tips) plt.show() INTRODUCTION TO SEABORN

  20. A scatter plot with hue import matplotlib.pyplot as plt import seaborn as sns sns.scatterplot(x="total_bill", y="tip", data=tips, hue="smoker") plt.show() INTRODUCTION TO SEABORN

  21. Setting hue order import matplotlib.pyplot as plt import seaborn as sns sns.scatterplot(x="total_bill", y="tip", data=tips, hue="smoker", hue_order=["Yes", "No"]) plt.show() INTRODUCTION TO SEABORN

  22. Specifying hue colors import matplotlib.pyplot as plt import seaborn as sns hue_colors = {"Yes": "black", "No": "red"} sns.scatterplot(x="total_bill", y="tip", data=tips, hue="smoker", palette=hue_colors) plt.show() INTRODUCTION TO SEABORN

  23. INTRODUCTION TO SEABORN

  24. Using HTML hex color codes with hue import matplotlib.pyplot as plt import seaborn as sns hue_colors = {"Yes": "#808080", "No": "#00FF00"} sns.scatterplot(x="total_bill", y="tip", data=tips, hue="smoker", palette=hue_colors) plt.show() INTRODUCTION TO SEABORN

  25. Using hue with count plots import matplotlib.pyplot as plt import seaborn as sns sns.countplot(x="smoker", data=tips, hue="sex") plt.show() INTRODUCTION TO SEABORN

  26. Let's practice! IN TRODUCTION TO S EABORN

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