scatter_matrix¶

The pd.plotting.scatter_matrix() function creates a grid of scatter plots showing pairwise relationships between numeric columns. Diagonal plots show the distribution of each variable.

Basic Usage¶

```python import pandas as pd import matplotlib.pyplot as plt

url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv' df = pd.read_csv(url)

Create scatter matrix¶

pd.plotting.scatter_matrix(df[['beer_servings', 'spirit_servings', 'wine_servings']], figsize=(10, 10)) plt.tight_layout() plt.show() ```

Understanding the Output¶

For n numeric columns, the scatter matrix creates an n×n grid:

• Off-diagonal cells: Scatter plots showing relationship between two variables
• Diagonal cells: Distribution of each variable (histogram by default)

beer spirit wine ┌────────┬────────┬────────┐ beer │ hist │ scatter│ scatter│ ├────────┼────────┼────────┤ spirit │ scatter│ hist │ scatter│ ├────────┼────────┼────────┤ wine │ scatter│ scatter│ hist │ └────────┴────────┴────────┘

Key Parameters¶

figsize - Figure Size¶

python pd.plotting.scatter_matrix(df, figsize=(12, 12))

diagonal - Distribution Plot Type¶

```python

Histogram (default)¶

pd.plotting.scatter_matrix(df, diagonal='hist')

Kernel density estimate¶

pd.plotting.scatter_matrix(df, diagonal='kde') ```

alpha - Point Transparency¶

python pd.plotting.scatter_matrix(df, alpha=0.5)

marker - Point Style¶

python pd.plotting.scatter_matrix(df, marker='o') pd.plotting.scatter_matrix(df, marker='.') pd.plotting.scatter_matrix(df, marker='+')

s - Point Size¶

python pd.plotting.scatter_matrix(df, s=50) # Larger points pd.plotting.scatter_matrix(df, s=10) # Smaller points

hist_kwds - Histogram Customization¶

python pd.plotting.scatter_matrix( df, diagonal='hist', hist_kwds={'bins': 20, 'edgecolor': 'black'} )

density_kwds - KDE Customization¶

python pd.plotting.scatter_matrix( df, diagonal='kde', density_kwds={'linewidth': 2} )

ax - Specify Axes Array¶

python fig, axes = plt.subplots(3, 3, figsize=(12, 12)) pd.plotting.scatter_matrix(df[['col1', 'col2', 'col3']], ax=axes) plt.show()

Practical Example: Iris Dataset¶

```python from sklearn.datasets import load_iris import pandas as pd import matplotlib.pyplot as plt

Load iris data¶

iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names)

Create scatter matrix¶

fig = pd.plotting.scatter_matrix( df, figsize=(12, 12), diagonal='kde', alpha=0.6, marker='o', s=30 )

plt.suptitle('Iris Dataset - Pairwise Relationships', y=1.02) plt.tight_layout() plt.show() ```

Practical Example: Financial Data¶

```python import yfinance as yf import pandas as pd import matplotlib.pyplot as plt

Download multiple stock prices¶

tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN'] df = yf.download(tickers, start='2023-01-01', end='2024-01-01')['Close']

Calculate returns¶

returns = df.pct_change().dropna()

Scatter matrix of returns¶

pd.plotting.scatter_matrix( returns, figsize=(10, 10), diagonal='kde', alpha=0.3, marker='.' ) plt.suptitle('Stock Return Correlations', y=1.02) plt.tight_layout() plt.show() ```

Practical Example: Drinks Dataset¶

```python url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv' df = pd.read_csv(url)

Select numeric columns for alcohol consumption¶

alcohol_cols = ['beer_servings', 'spirit_servings', 'wine_servings', 'total_litres_of_pure_alcohol']

fig, ax = plt.subplots(figsize=(12, 12)) pd.plotting.scatter_matrix( df[alcohol_cols], diagonal='hist', alpha=0.5, hist_kwds={'bins': 15, 'edgecolor': 'black'} ) plt.tight_layout() plt.show() ```

Color by Category¶

To color points by a categorical variable, use matplotlib directly:

```python from sklearn.datasets import load_iris import pandas as pd import matplotlib.pyplot as plt

iris = load_iris() df = pd.DataFrame(iris.data, columns=['sepal_l', 'sepal_w', 'petal_l', 'petal_w']) df['species'] = iris.target

Create figure¶

fig, axes = plt.subplots(4, 4, figsize=(12, 12))

colors = ['red', 'green', 'blue'] columns = ['sepal_l', 'sepal_w', 'petal_l', 'petal_w']

for i, col1 in enumerate(columns): for j, col2 in enumerate(columns): ax = axes[i, j] if i == j: # Diagonal: histogram for species in range(3): mask = df['species'] == species ax.hist(df.loc[mask, col1], alpha=0.5, color=colors[species]) else: # Off-diagonal: scatter for species in range(3): mask = df['species'] == species ax.scatter(df.loc[mask, col2], df.loc[mask, col1], alpha=0.5, color=colors[species], s=10)

    if j == 0:
        ax.set_ylabel(col1)
    if i == 3:
        ax.set_xlabel(col2)

plt.tight_layout() plt.show() ```

Interpreting Scatter Matrices¶

What to Look For¶

1. Linear relationships: Points forming a line indicate correlation
2. Clusters: Groups of points may indicate categories
3. Outliers: Isolated points away from the main cluster
4. Distribution shape: Diagonal plots show if data is normal, skewed, etc.

Correlation Patterns¶

Method Signature¶

python pandas.plotting.scatter_matrix( frame, # DataFrame alpha=0.5, # Point transparency figsize=None, # Figure size ax=None, # Axes array grid=False, # Show grid diagonal='hist', # 'hist' or 'kde' marker='.', # Point marker density_kwds=None, # KDE kwargs hist_kwds=None, # Histogram kwargs range_padding=0.05, # Padding around axis limits **kwargs # Additional scatter kwargs )

Summary¶

```python

Basic scatter matrix¶

pd.plotting.scatter_matrix(df)

With KDE on diagonal¶

pd.plotting.scatter_matrix(df, diagonal='kde')

Customized¶

pd.plotting.scatter_matrix( df[['col1', 'col2', 'col3']], figsize=(10, 10), diagonal='hist', alpha=0.5, marker='o', hist_kwds={'bins': 20} ) ```

Alternatives¶

For more advanced pairwise plots with categorical coloring:

```python

Using seaborn (if available)¶

import seaborn as sns sns.pairplot(df, hue='category_column') ```

Exercises¶

Exercise 1. Write code that creates a scatter matrix (pair plot) using pd.plotting.scatter_matrix(df).

Exercise 2. Explain what information a scatter matrix provides. What is shown on the diagonal?

Exercise 3. Create a scatter matrix with diagonal='kde' instead of the default histogram.

Exercise 4. Write code that creates a scatter matrix for a subset of columns and customizes the figure size.