Create report_generator.py

report_generator.py

@@ -0,0 +1,45 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import StringIO
+
+class ReportGenerator:
+    def generate(self, data):
+        report = StringIO()
+        report.write("Data Analysis Report\n\n")
+
+        # Data summary
+        report.write("1. Data Summary\n")
+        report.write(data.describe().to_string())
+        report.write("\n\n")
+
+        # Missing values
+        report.write("2. Missing Values\n")
+        missing_values = data.isnull().sum()
+        report.write(missing_values.to_string())
+        report.write("\n\n")
+
+        # Correlation analysis
+        report.write("3. Correlation Analysis\n")
+        numeric_columns = data.select_dtypes(include=[pd.np.number]).columns
+        if len(numeric_columns) > 1:
+            correlation_matrix = data[numeric_columns].corr()
+            report.write(correlation_matrix.to_string())
+        else:
+            report.write("Not enough numeric columns for correlation analysis.")
+        report.write("\n\n")
+
+        # Categorical data analysis
+        categorical_columns = data.select_dtypes(include=['object']).columns
+        if len(categorical_columns) > 0:
+            report.write("4. Categorical Data Analysis\n")
+            for column in categorical_columns:
+                report.write(f"{column} value counts:\n")
+                report.write(data[column].value_counts().to_string())
+                report.write("\n\n")
+
+        # Data visualizations
+        report.write("5. Data Visualizations\n")
+        report.write("Please refer to the generated plots in the Streamlit app for visual representations of the data.\n\n")
+
+        return report.getvalue()