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Changing plot style and color IN TRODUCTION TO S EABORN Erin Case - - PowerPoint PPT Presentation

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Changing plot style and color IN TRODUCTION TO S EABORN Erin Case Data Scientist Why customize? Reasons to change style: Personal preference Improve readability Guide interpretation INTRODUCTION TO SEABORN Changing the gure style

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Changing plot style and color

IN TRODUCTION TO S EABORN

Erin Case

Data Scientist

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INTRODUCTION TO SEABORN

Why customize?

Reasons to change style: Personal preference Improve readability Guide interpretation

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INTRODUCTION TO SEABORN

Changing the gure style

Figure "style" includes background and axes Preset options: "white", "dark", "whitegrid", "darkgrid", "ticks"

sns.set_style()

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Default gure style ("white")

sns.catplot(x="age", y="masculinity_important", data=masculinity_data, hue="feel_masculine", kind="point") plt.show()

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Figure style: "whitegrid"

sns.set_style("whitegrid") sns.catplot(x="age", y="masculinity_important", data=masculinity_data, hue="feel_masculine", kind="point") plt.show()

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Other styles

sns.set_style("ticks") sns.catplot(x="age", y="masculinity_important", data=masculinity_data, hue="feel_masculine", kind="point") plt.show()

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Other styles

sns.set_style("dark") sns.catplot(x="age", y="masculinity_important", data=masculinity_data, hue="feel_masculine", kind="point") plt.show()

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Other styles

sns.set_style("darkgrid") sns.catplot(x="age", y="masculinity_important", data=masculinity_data, hue="feel_masculine", kind="point") plt.show()

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Changing the palette

Figure "palette" changes the color of the main elements of the plot

sns.set_palette()

Use preset palettes or create a custom palette

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Diverging palettes

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Example (default palette)

category_order = ["No answer", "Not at all", "Not very", "Somewhat", "Very"] sns.catplot(x="how_masculine", data=masculinity_data, kind="count",

  • rder=category_order)

plt.show()

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Example (diverging palette)

sns.set_palette("RdBu") category_order = ["No answer", "Not at all", "Not very", "Somewhat", "Very"] sns.catplot(x="how_masculine", data=masculinity_data, kind="count",

  • rder=category_order)

plt.show()

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Sequential palettes

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Sequential palette example

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Custom palettes

custom_palette = ["red", "green", "orange", "blue", "yellow", "purple"] sns.set_palette(custom_palette)

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Custom palettes

custom_palette = ['#FBB4AE', '#B3CDE3', '#CCEBC5', '#DECBE4', '#FED9A6', '#FFFFCC', '#E5D8BD', '#FDDAEC', '#F2F2F2'] sns.set_palette(custom_palette)

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Changing the scale

Figure "context" changes the scale of the plot elements and labels

sns.set_context()

Smallest to largest: "paper", "notebook", "talk", "poster"

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Default context: "paper"

sns.catplot(x="age", y="masculinity_important", data=masculinity_data, hue="feel_masculine", kind="point") plt.show()

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Larger context: "talk"

sns.set_context("talk") sns.catplot(x="age", y="masculinity_important", data=masculinity_data, hue="feel_masculine", kind="point") plt.show()

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Let's practice!

IN TRODUCTION TO S EABORN

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Adding titles and labels: Part 1

IN TRODUCTION TO S EABORN

Erin Case

Data Scientist

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Creating informative visualizations

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FacetGrid vs. AxesSubplot objects

Seaborn plots create two different types of objects: FacetGrid and AxesSubplot

g = sns.scatterplot(x="height", y="weight", data=df) type(g) > matplotlib.axes._subplots.AxesSubplot

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An Empty FacetGrid

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FacetGrid vs. AxesSubplot objects

Object Type Plot Types Characteristics

FacetGrid relplot() , catplot()

Can create subplots

AxesSubplot scatterplot() , countplot() , etc. Only creates a single plot

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Adding a title to FacetGrid

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box") g.fig.suptitle("New Title") plt.show()

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Adjusting height of title in FacetGrid

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box") g.fig.suptitle("New Title", y=1.03) plt.show()

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Let's practice!

IN TRODUCTION TO S EABORN

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Adding titles and labels: Part 2

IN TRODUCTION TO S EABORN

Erin Case

Data Scientist

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Adding a title to AxesSubplot

FacetGrid

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box") g.fig.suptitle("New Title", y=1.03)

AxesSubplot

g = sns.boxplot(x="Region", y="Birthrate", data=gdp_data) g.set_title("New Title", y=1.03)

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Titles for subplots

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box", col="Group")

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Titles for subplots

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box", col="Group") g.fig.suptitle("New Title", y=1.03)

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Titles for subplots

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box", col="Group") g.fig.suptitle("New Title", y=1.03) g.set_titles("This is {col_name}")

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Adding axis labels

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box") ` g.set(xlabel="New X Label", ylabel="New Y Label") plt.show()

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Rotating x-axis tick labels

g = sns.catplot(x="Region", y="Birthrate", data=gdp_data, kind="box") plt.xticks(rotation=90) plt.show()

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Let's practice!

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Putting it all together

IN TRODUCTION TO S EABORN

Erin Case

Data Scientist

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Getting started

T

  • import Seaborn:

import seaborn as sns

T

  • import Matplotlib:

import matplotlib.pyplot as plt

T

  • show a plot:

plt.show()

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Relational plots

Show the relationship between two quantitative variables Examples: scatter plots, line plots

sns.relplot(x="x_variable_name", y="y_variable_name", data=pandas_df, kind="scatter")

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Categorical plots

Show the distribution of a quantitative variable within categories dened by a categorical variable Examples: bar plots, count plots, box plots, point plots

sns.catplot(x="x_variable_name", y="y_variable_name", data=pandas_df, kind="bar")

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Adding a third variable (hue)

Setting hue will create subgroups that are displayed as different colors on a single plot.

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Adding a third variable (row/col)

Setting row and/or col in relplot() or

catplot() will create subgroups that are

displayed on separate subplots.

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Customization

Change the background: sns.set_style() Change the main element colors: sns.set_palette() Change the scale: sns.set_context()

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Adding a title

Object Type Plot Types How to Add Title

FacetGrid relplot() , catplot() g.fig.suptitle() AxesSubplot scatterplot() , countplot() , etc. g.set_title()

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Final touches

Add x- and y-axis labels:

g.set(xlabel="new x-axis label", ylabel="new y-axis label")

Rotate x-tick labels:

plt.xticks(rotation=90)

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Let's practice!

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Well done! What's next?

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Erin Case

Data Scientist

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Where does Seaborn t in?

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Where does Seaborn t in?

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Next Steps: Explore and communicate results

Next steps: Seaborn advanced visualizations Matplotlib advanced customizations

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Next steps: Gather data

Next steps: Python SQL

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Next steps: Transform and clean

Next steps: Getting data into Pandas DataFrames Cleaning data Transforming into tidy format

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Next steps: Analyze and build models

Next steps: Statistical analysis Calculating and interpreting condence intervals

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Congratulations!

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