Upload durham_trees_analysis.py

durham_trees_analysis.py

@@ -0,0 +1,78 @@
+# -*- coding: utf-8 -*-
+"""durham_trees_analysis
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1OjlRC7F_UICGJM59jzSoy1o2crZxqXCl
+"""
+
+# Save this code as a file named 'durham_trees_analysis.py'
+# Import display function and Image class
+from IPython.display import display, Image
+# Install required packages
+import subprocess
+import seaborn as sns
+import matplotlib.pyplot as plt
+# Install datasets package
+subprocess.run(["pip", "install", "datasets", "geopandas", "seaborn", "matplotlib", "mplcursors", "pandas"])
+
+# Import libraries
+from datasets import load_dataset
+import seaborn as sns
+import matplotlib.pyplot as plt
+import mplcursors
+import pandas as pd
+import geopandas as gpd
+
+# Load dataset
+dataset = load_dataset("Ziyuan111/DurhamTrees")
+
+# Convert dataset to pandas DataFrame
+df = pd.DataFrame(dataset['train'])
+
+# Interactive scatter plot with seaborn and mplcursors
+def plot_interactive_scatter():
+    scatter = sns.scatterplot(data=df, x='X', y='Y', hue='species')
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    plt.legend([],[], frameon=False)
+    cursor = mplcursors.cursor(hover=True)
+
+    @cursor.connect("add")
+    def on_add(sel):
+        sel.annotation.set_text(df.iloc[sel.target.index]['species'])
+    plt.show()
+    plt.savefig('interactive_scatter.png')
+    display(Image('interactive_scatter.png'))
+
+# Plot tree planting sites with geopandas
+def plot_tree_sites():
+    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.X, df.Y))
+    durham_center = {'x': -78.898619, 'y': 35.994033}  # Durham, NC coordinates
+    fig, ax = plt.subplots(figsize=(10, 10))
+    gdf.plot(ax=ax, color='green')
+    buffer = 0.05
+    ax.set_xlim([durham_center['x'] - buffer, durham_center['x'] + buffer])
+    ax.set_ylim([durham_center['y'] - buffer, durham_center['y'] + buffer])
+    ax.set_title('Tree Planting Sites in Durham')
+    ax.set_xlabel('Longitude')
+    ax.set_ylabel('Latitude')
+    ax.set_axis_off()
+    plt.show()
+    plt.savefig('tree_sites.png')
+    display(Image('tree_sites.png'))
+
+# Print correlation matrix
+def print_correlation_matrix():
+    correlation_matrix = df[['vocs', 'coremoved_ozperyr', 'coremoved_dolperyr']].corr()
+    print(correlation_matrix)
+
+# Call the functions
+plot_interactive_scatter()
+plot_tree_sites()
+print_correlation_matrix()
+
+
+# Show the plot
+plt.show()