update (componentization): step2 and step3 with Claude

notebooks/componentization/claude.md

@@ -4,7 +4,6 @@
 
 Consider the following code:
 
-
 ```
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -79,4 +78,247 @@ def main():
 if __name__ == "__main__":
     main()
 
+~~~
+
+## Passo 2 - Múltiplos Serviços
+
+Consider the following code:
+
+```
+import matplotlib.pyplot as plt
+
+# Create a scatter plot
+plt.scatter(df['age'], df['intake_bw'], c=df['intake_bw'], cmap='viridis')
+
+# Add labels to the plot
+plt.xlabel('Age')
+plt.ylabel('Intake BW')
+plt.title('Age vs. Intake BW')
+
+# Show the plot
+plt.show()
+```
+
+How can I transform it in a Python class whose objects receive four parameters (df, xlabel, ylabel, and title) and plots a scatter plot?
+
+<hr>
+
+### Answer
+
+~~~python
+
+import matplotlib.pyplot as plt
+
+class ScatterPlot:
+    def __init__(self, df, xlabel, ylabel, title):
+        self.df = df
+        self.xlabel = xlabel
+        self.ylabel = ylabel
+        self.title = title
+
+    def plot(self):
+        # Create a scatter plot
+        plt.figure(figsize=(10, 6))
+        scatter = plt.scatter(self.df['age'], self.df['intake_bw'], c=self.df['intake_bw'], cmap='viridis')
+
+        # Add labels to the plot
+        plt.xlabel(self.xlabel)
+        plt.ylabel(self.ylabel)
+        plt.title(self.title)
+
+        # Add a colorbar
+        plt.colorbar(scatter, label='Intake BW')
+
+        # Show the plot
+        plt.show()
+
+# Example usage:
+# scatter_plot = ScatterPlot(df, 'Age', 'Intake BW', 'Age vs. Intake BW')
+# scatter_plot.plot()
+
+~~~
+
+<hr>
+
+Consider this code:
+
+```
+import pandas as pd
+
+# Read the CSV file
+df = pd.read_csv('../../../data/food-intake/analysis/prepared/intake-person-demo(potato-chips).csv')
+```
+
+Can you transform it in a class whose object receives a data file path as a parameter and reads the file?
+After that, provide an example of this class running together to the previous one.
+
+<hr>
+
+### Answer
+
+~~~python
+import pandas as pd
+
+class DataLoader:
+    def __init__(self, file_path):
+        self.file_path = file_path
+        self.data = None
+
+    def load_data(self):
+        try:
+            self.data = pd.read_csv(self.file_path)
+            print(f"Data loaded successfully from {self.file_path}")
+        except FileNotFoundError:
+            print(f"Error: File not found at {self.file_path}")
+        except pd.errors.EmptyDataError:
+            print(f"Error: The file at {self.file_path} is empty")
+        except pd.errors.ParserError:
+            print(f"Error: Unable to parse the file at {self.file_path}")
+
+    def get_data(self):
+        if self.data is None:
+            print("Data has not been loaded. Call load_data() first.")
+            return None
+        return self.data
+~~~
+
+#### Updated ScatterPlot with Example
+
+~~~python
+import matplotlib.pyplot as plt
+
+class ScatterPlot:
+    def __init__(self, df, xlabel, ylabel, title):
+        self.df = df
+        self.xlabel = xlabel
+        self.ylabel = ylabel
+        self.title = title
+
+    def plot(self):
+        # Create a scatter plot
+        plt.figure(figsize=(10, 6))
+        scatter = plt.scatter(self.df['age'], self.df['intake_bw'], c=self.df['intake_bw'], cmap='viridis')
+
+        # Add labels to the plot
+        plt.xlabel(self.xlabel)
+        plt.ylabel(self.ylabel)
+        plt.title(self.title)
+
+        # Add a colorbar
+        plt.colorbar(scatter, label='Intake BW')
+
+        # Show the plot
+        plt.show()
+
+# Example usage of DataLoader and ScatterPlot classes together:
+
+# Import the DataLoader class (assuming it's in a file named data_loader.py)
+from data_loader import DataLoader
+
+# Create a DataLoader instance
+data_loader = DataLoader('../../../data/food-intake/analysis/prepared/intake-person-demo(potato-chips).csv')
+
+# Load the data
+data_loader.load_data()
+
+# Get the loaded data
+df = data_loader.get_data()
+
+# Check if data was loaded successfully
+if df is not None:
+    # Create a ScatterPlot instance
+    scatter_plot = ScatterPlot(df, 'Age', 'Intake BW', 'Age vs. Intake BW for Potato Chips')
+
+    # Generate the plot
+    scatter_plot.plot()
+else:
+    print("Unable to create scatter plot due to data loading issues.")
+~~~
+
+## Passo 3 - Representando Relações
+
+Can you modify The DataLoader and ScatterPlot classes (and the respective example) in such a way that the DataLoader has a connect method that receives the ScatterPlot as a parameter and keeps it. Whenever the DataLoader reads a new table it automatically sends it to the ScatterPlot.
+
+<hr>
+
+### Answer
+
+~~~python
+import pandas as pd
+
+class DataLoader:
+    def __init__(self):
+        self.data = None
+        self.observers = []
+
+    def connect(self, observer):
+        self.observers.append(observer)
+
+    def notify_observers(self):
+        for observer in self.observers:
+            observer.update(self.data)
+
+    def load_data(self, file_path):
+        try:
+            self.data = pd.read_csv(file_path)
+            print(f"Data loaded successfully from {file_path}")
+            self.notify_observers()
+        except FileNotFoundError:
+            print(f"Error: File not found at {file_path}")
+        except pd.errors.EmptyDataError:
+            print(f"Error: The file at {file_path} is empty")
+        except pd.errors.ParserError:
+            print(f"Error: Unable to parse the file at {file_path}")
+
+    def get_data(self):
+        return self.data
+~~~
+
+#### Updated ScatterPlot with Example
+
+~~~python
+import matplotlib.pyplot as plt
+
+class ScatterPlot:
+    def __init__(self, xlabel, ylabel, title):
+        self.df = None
+        self.xlabel = xlabel
+        self.ylabel = ylabel
+        self.title = title
+
+    def update(self, new_data):
+        self.df = new_data
+        self.plot()
+
+    def plot(self):
+        if self.df is None:
+            print("No data to plot.")
+            return
+
+        plt.figure(figsize=(10, 6))
+        scatter = plt.scatter(self.df['age'], self.df['intake_bw'], c=self.df['intake_bw'], cmap='viridis')
+
+        plt.xlabel(self.xlabel)
+        plt.ylabel(self.ylabel)
+        plt.title(self.title)
+
+        plt.colorbar(scatter, label='Intake BW')
+        plt.show()
+
+# Example usage of the updated DataLoader and ScatterPlot classes:
+
+# Assuming both classes are in the same file or properly imported
+
+# Create instances
+data_loader = DataLoader()
+scatter_plot = ScatterPlot('Age', 'Intake BW', 'Age vs. Intake BW for Potato Chips')
+
+# Connect the ScatterPlot to the DataLoader
+data_loader.connect(scatter_plot)
+
+# Load data - this will automatically update and plot the scatter plot
+data_loader.load_data('../../../data/food-intake/analysis/prepared/intake-person-demo(potato-chips).csv')
+
+# If you want to load another file, just call load_data again
+# data_loader.load_data('path/to/another/file.csv')
 ~~~