EDS 217 Cheatsheet

Introduction to Seaborn

Seaborn is a powerful Python data visualization library built on top of Matplotlib. It provides a high-level interface for drawing attractive statistical graphics. Seaborn is particularly useful for creating complex visualizations with just a few lines of code.

Key features: - Built-in themes for attractive plots - Statistical plot types - Integration with Pandas DataFrames - Automatic estimation and plotting of statistical models

Setting up Seaborn

To use Seaborn, you need to import it along with other necessary libraries:

Code

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Set the style for all plots
sns.set_style("whitegrid")

Common Plot Types in Environmental Data Science

For these examples, we’ll use various built-in datasets provided by Seaborn. These datasets are included with the library and are commonly used for demonstration purposes.

1. Scatter Plots

Useful for showing relationships between two continuous variables.

Code

# Load the tips dataset
tips = sns.load_dataset("tips")
print(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

Code

# Basic scatter plot
plt.figure(figsize=(10, 6))
sns.scatterplot(x='total_bill', y='tip', data=tips)
plt.title('Relationship between Total Bill and Tip')
plt.show()

Code

# Add hue for a third variable
plt.figure(figsize=(10, 6))
sns.scatterplot(x='total_bill', y='tip', hue='time', data=tips)
plt.title('Relationship between Total Bill and Tip, colored by Time of Day')
plt.show()

The ‘tips’ dataset contains information about restaurant bills and tips. It’s a classic dataset used for demonstrating various plotting techniques.

2. Line Plots

Ideal for time series data or showing trends.

Code

# Load the flights dataset
flights = sns.load_dataset("flights")
print(flights.head())

   year month  passengers
0  1949   Jan         112
1  1949   Feb         118
2  1949   Mar         132
3  1949   Apr         129
4  1949   May         121

Code

# Basic line plot (uncertainty bounds calculated auto-magically by grouping rows containing the same year!)
plt.figure(figsize=(12, 6))
sns.lineplot(x='year', y='passengers', data=flights)
plt.title('Number of Passengers over Time')
plt.show()

Code

# Multiple lines with confidence intervals
plt.figure(figsize=(12, 6))
sns.lineplot(x='year', y='passengers', hue='month', data=flights)
plt.title('Number of Passengers over Time, by Month')
plt.show()

The ‘flights’ dataset contains information about passenger numbers for flights over time. It’s useful for demonstrating time series visualizations.

3. Bar Plots

Great for comparing quantities across different categories.

Code

# Load the titanic dataset
titanic = sns.load_dataset("titanic")
print(titanic.head())

   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \
0         0       3    male  22.0      1      0   7.2500        S  Third   
1         1       1  female  38.0      1      0  71.2833        C  First   
2         1       3  female  26.0      0      0   7.9250        S  Third   
3         1       1  female  35.0      1      0  53.1000        S  First   
4         0       3    male  35.0      0      0   8.0500        S  Third   

     who  adult_male deck  embark_town alive  alone  
0    man        True  NaN  Southampton    no  False  
1  woman       False    C    Cherbourg   yes  False  
2  woman       False  NaN  Southampton   yes   True  
3  woman       False    C  Southampton   yes  False  
4    man        True  NaN  Southampton    no   True

Code

# Basic bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x='class', y='fare', data=titanic)
plt.title('Average Fare by Passenger Class')
plt.show()

Code

# Grouped bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x='class', y='fare', hue='sex', data=titanic)
plt.title('Average Fare by Passenger Class and Sex')
plt.show()

The ‘titanic’ dataset contains information about passengers on the Titanic, including their class, sex, age, and survival status.

4. Box Plots

Useful for showing distribution of data across categories.

Code

# Basic box plot
plt.figure(figsize=(10, 6))
sns.boxplot(x='class', y='age', data=titanic)
plt.title('Distribution of Age by Passenger Class')
plt.show()

Code

# Add individual data points
plt.figure(figsize=(10, 6))
sns.boxplot(x='class', y='age', data=titanic)
sns.swarmplot(x='class', y='age', data=titanic, color=".25", size=3)
plt.title('Distribution of Age by Passenger Class with Individual Points')
plt.show()

5. Violin Plots

Similar to box plots but show the full distribution of data.

Code

plt.figure(figsize=(10, 6))
sns.violinplot(x='class', y='age', data=titanic)
plt.title('Distribution of Age by Passenger Class (Violin Plot)')
plt.show()

6. Heatmaps

Excellent for visualizing correlation matrices or gridded data.

Code

# Load the penguins dataset
penguins = sns.load_dataset("penguins")
print(penguins.head(5))

  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \
0  Adelie  Torgersen            39.1           18.7              181.0   
1  Adelie  Torgersen            39.5           17.4              186.0   
2  Adelie  Torgersen            40.3           18.0              195.0   
3  Adelie  Torgersen             NaN            NaN                NaN   
4  Adelie  Torgersen            36.7           19.3              193.0   

   body_mass_g     sex  
0       3750.0    Male  
1       3800.0  Female  
2       3250.0  Female  
3          NaN     NaN  
4       3450.0  Female

Code

# Correlation heatmap
plt.figure(figsize=(10, 8))
correlation = penguins.select_dtypes(include=[np.number]).corr()
sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap of Penguin Measurements')
plt.show()

The ‘penguins’ dataset contains size measurements for three penguin species observed on three islands in the Palmer Archipelago, Antarctica.

Using Seaborn for Data Exploration with DataFrames

Seaborn is particularly powerful when working with Pandas DataFrames, as it can automatically infer variable types and choose appropriate plot types.

Quick Data Overview

Recall the structure of the penguins dataframe, which has a combination of measured and categorical values:

Code

print(penguins.head())

  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \
0  Adelie  Torgersen            39.1           18.7              181.0   
1  Adelie  Torgersen            39.5           17.4              186.0   
2  Adelie  Torgersen            40.3           18.0              195.0   
3  Adelie  Torgersen             NaN            NaN                NaN   
4  Adelie  Torgersen            36.7           19.3              193.0   

   body_mass_g     sex  
0       3750.0    Male  
1       3800.0  Female  
2       3250.0  Female  
3          NaN     NaN  
4       3450.0  Female

We can explore the distribution of every numerical variable as well as the pair-wise relationship between all the variables in a dataframe using pairplot and can use a categorical variable to further organize the data within each plot using the hue argument.

Code

# Get a quick overview of numerical variables
sns.pairplot(penguins, hue='species')
plt.suptitle('Overview of Penguin Measurements by Species', y=1.02)
plt.show()

Code

# Visualize distributions of all numerical variables
sns.displot(penguins['bill_length_mm'], kind='kde')
plt.suptitle('Distribution of Penguin Bill Lengths',y=1.02)
plt.show()

Exploring Relationships

Code

# Explore relationship between variables
sns.relplot(data=penguins, x='bill_length_mm', y='bill_depth_mm', hue='species', style='sex')
plt.title('Relationship between Bill Length and Depth')
plt.show()

Code

# Facet plots for multi-dimensional exploration
g = sns.FacetGrid(penguins, col='species', row='sex')
g.map(sns.scatterplot, 'bill_length_mm', 'bill_depth_mm')
g.add_legend()
plt.suptitle('Bill Measurements by Species and Sex', y=1.02)
plt.show()

Categorical Data Exploration

Code

# Compare distributions across categories
sns.catplot(data=penguins, kind='box', x='species', y='body_mass_g', hue='sex')
plt.title('Body Mass Distribution by Species and Sex')
plt.show()

Code

# Count plots for categorical variables
sns.countplot(data=penguins, x='island', hue='species')
plt.title('Penguin Species Count by Island')
plt.show()

Time Series Exploration

Code

# Visualize trends over time
sns.lineplot(data=flights, x='year', y='passengers', hue='month')
plt.title('Passenger Numbers by Year and Month')
plt.show()

Key Points

Seaborn works seamlessly with Pandas DataFrames, making it easy to create plots directly from your data.
The library offers a wide range of plot types suitable for various environmental data science tasks.
Seaborn’s statistical plotting functions (like regplot and lmplot) can automatically fit and visualize linear regressions.
The pairplot function and FacetGrid class are powerful for creating multi-panel plots to explore complex datasets.
Seaborn’s themes and color palettes help create publication-quality figures with minimal customization.

Resources

Remember, while Seaborn is powerful, it’s built on matplotlib. For more customization options, you might need to use matplotlib functions directly.