python_env3.qmd

---
title: "Python Analysis with Conda env3"
jupyter: python3
kernel: python3
execute:
  env: env3
  activate: true
format:
  html:
    code-fold: true
    code-tools: true
    code-link: true
    toc: true
    toc-depth: 3
    number-sections: true
    theme: cosmo
    embed-resources: true
    self-contained: true
---

# Python Analysis with Conda env3

This document demonstrates how to use Python in a Quarto document with the conda env3 environment.

## Setup

First, let's verify our Python environment:

```{python}
import sys
print(f"Python version: {sys.version}")
print(f"Python executable: {sys.executable}")
```

## Data Analysis Example

Let's create a simple example using pandas and matplotlib:

```{python}
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Create some sample data
data = pd.DataFrame({
    'x': np.linspace(0, 10, 100),
    'y': np.sin(np.linspace(0, 10, 100))
})

# Plot the data
plt.figure(figsize=(10, 6))
plt.plot(data['x'], data['y'])
plt.title('Sine Wave Example')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True)
plt.show()
```

## Conclusion

This document demonstrates the basic setup for using Python in Quarto with your conda env3 environment. 



```{python}
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

# Load the Iris dataset
iris = load_iris()
iris_df = sns.load_dataset('iris')

# Set the style for better visualization
sns.set(style="whitegrid")

# Create a figure with multiple subplots
plt.figure(figsize=(15, 10))

# 1. Scatter plot of sepal length vs sepal width
plt.subplot(2, 2, 1)
sns.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=iris_df)
plt.title('Sepal Length vs Sepal Width')

# 2. Scatter plot of petal length vs petal width
plt.subplot(2, 2, 2)
sns.scatterplot(x='petal_length', y='petal_width', hue='species', data=iris_df)
plt.title('Petal Length vs Petal Width')

# 3. Box plot of all features
plt.subplot(2, 2, 3)
sns.boxplot(data=iris_df.drop('species', axis=1))
plt.title('Box Plot of All Features')
plt.xticks(rotation=45)

# 4. Pair plot
plt.subplot(2, 2, 4)
sns.pairplot(iris_df, hue='species')
plt.suptitle('Iris Dataset Visualization', y=1.02)

# Adjust layout and show plot
plt.tight_layout()
plt.show()

# Additional: Correlation heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(iris_df.drop('species', axis=1).corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.tight_layout()
plt.show()
```



```{python}

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris

# Load the Iris dataset
iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = [iris.target_names[i] for i in iris.target]
iris_df['species_num'] = iris.target  # Add numeric species column

# Create a 3D scatter plot
fig_3d = px.scatter_3d(iris_df, 
                      x='sepal length (cm)', 
                      y='sepal width (cm)', 
                      z='petal length (cm)',
                      color='species',
                      title='3D Visualization of Iris Dataset')

# Create a parallel coordinates plot
fig_parallel = px.parallel_coordinates(iris_df, 
                                     dimensions=['sepal length (cm)', 'sepal width (cm)',
                                               'petal length (cm)', 'petal width (cm)'],
                                     color='species_num',
                                     color_continuous_scale=px.colors.qualitative.Set1,
                                     title='Parallel Coordinates Plot of Iris Features')

# Create a violin plot
fig_violin = px.violin(iris_df, 
                      y=['sepal length (cm)', 'sepal width (cm)', 
                         'petal length (cm)', 'petal width (cm)'],
                      color='species',
                      title='Violin Plot of Iris Features')

# Create a correlation heatmap
corr_matrix = iris_df.drop(['species', 'species_num'], axis=1).corr()
fig_heatmap = go.Figure(data=go.Heatmap(
    z=corr_matrix.values,
    x=corr_matrix.columns,
    y=corr_matrix.columns,
    colorscale='RdBu',
    zmin=-1,
    zmax=1
))
fig_heatmap.update_layout(title='Correlation Heatmap of Iris Features')

# Create a scatter matrix
fig_scatter = px.scatter_matrix(iris_df,
                              dimensions=['sepal length (cm)', 'sepal width (cm)',
                                        'petal length (cm)', 'petal width (cm)'],
                              color='species',
                              title='Scatter Matrix of Iris Features')

```


```{python}
fig_scatter.show()
# Show all plots
fig_3d.show()
fig_parallel.show()
fig_violin.show()
fig_heatmap.show()
```