Delete geospatial_visualization.py

geospatial_visualization.py

@@ -1,55 +0,0 @@
-import pandas as pd
-import geopandas as gpd
-import matplotlib.pyplot as plt
-
-class GeospatialVisualizer:
-    def __init__(self):
-        # Replace the path below with the actual path where you've downloaded the shapefile
-        self.world = gpd.read_file("/path_to_your_downloaded_shapefile/naturalearth_lowres.shp")
-
-    def visualize(self, data, location_column, value_column):
-        # Merge data with world map
-        merged = self.world.merge(data, how='left', left_on=['name'], right_on=[location_column])
-
-        # Create the plot
-        fig, ax = plt.subplots(figsize=(15, 10))
-        merged.plot(column=value_column, ax=ax, legend=True, 
-                    legend_kwds={'label': value_column, 'orientation': 'horizontal'},
-                    missing_kwds={'color': 'lightgrey'})
-
-        # Customize the plot
-        ax.set_title(f'{value_column} by Country')
-        ax.axis('off')
-
-        return fig
-
-    def create_choropleth(self, data, location_column, value_column):
-        # Merge data with world map
-        merged = self.world.merge(data, how='left', left_on=['name'], right_on=[location_column])
-
-        # Create the choropleth map
-        fig, ax = plt.subplots(figsize=(15, 10))
-        merged.plot(column=value_column, ax=ax, legend=True, 
-                    legend_kwds={'label': value_column, 'orientation': 'horizontal'},
-                    cmap='YlOrRd', missing_kwds={'color': 'lightgrey'})
-
-        # Customize the plot
-        ax.set_title(f'Choropleth Map: {value_column} by Country')
-        ax.axis('off')
-
-        return fig
-
-    def create_bubble_map(self, data, lat_column, lon_column, size_column):
-        # Create a GeoDataFrame from the data
-        gdf = gpd.GeoDataFrame(data, geometry=gpd.points_from_xy(data[lon_column], data[lat_column]))
-
-        # Create the bubble map
-        fig, ax = plt.subplots(figsize=(15, 10))
-        self.world.plot(ax=ax, color='lightgrey')
-        gdf.plot(ax=ax, markersize=data[size_column]/data[size_column].max()*100, alpha=0.5)
-
-        # Customize the plot
-        ax.set_title(f'Bubble Map: {size_column} by Location')
-        ax.axis('off')
-
-        return fig