Update pages/RoadMap.py

pages/RoadMap.py

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+import streamlit as st
+from PIL import Image, ImageDraw, ImageFont
+
+# Set page configuration
+st.set_page_config(page_title="Data Analysis Roadmap", layout="centered")
+
+# Title and description
+st.title("Roadmap for Data Analysis with Python")
+st.write("""
+This roadmap guides you through the essential steps and tools for mastering data analysis with Python.
+Each step builds upon the previous one to develop your skills progressively.
+""")
+
+# Define the sequence of topics
+topics = [
+    "Statistics",
+    "Numpy",
+    "Pandas",
+    "Matplotlib",
+    "Seaborn",
+    "Plotly",
+    "Graph Visualization"
+]
+
+# Create a roadmap visualization
+def create_roadmap_image(topics):
+    # Image dimensions
+    width, height = 800, 600
+    # Box dimensions
+    box_width, box_height = 200, 80
+    # Gap between boxes
+    gap = 20
+
+    # Create a blank image with white background
+    img = Image.new("RGB", (width, height), "white")
+    draw = ImageDraw.Draw(img)
+
+    # Load a font
+    try:
+        font = ImageFont.truetype("arial.ttf", 16)
+    except IOError:
+        font = ImageFont.load_default()
+
+    # Calculate starting positions
+    start_x = (width - box_width) // 2
+    start_y = 50
+
+    # Draw boxes with topics
+    for i, topic in enumerate(topics):
+        box_x = start_x
+        box_y = start_y + i * (box_height + gap)
+        draw.rectangle(
+            [box_x, box_y, box_x + box_width, box_y + box_height],
+            outline="black", width=2
+        )
+        text_width, text_height = draw.textsize(topic, font=font)
+        text_x = box_x + (box_width - text_width) // 2
+        text_y = box_y + (box_height - text_height) // 2
+        draw.text((text_x, text_y), topic, fill="black", font=font)
+
+    return img
+
+# Create and display the roadmap image
+roadmap_image = create_roadmap_image(topics)
+st.image(roadmap_image, caption="Roadmap for Data Analysis with Python", use_column_width=True)
+
+# Provide detailed content for each topic
+st.header("Detailed Roadmap")
+
+st.subheader("1. Statistics")
+st.write("""
+Statistics is the foundation of data analysis. Learn descriptive statistics, probability, distributions, hypothesis testing, and regression analysis.
+""")
+
+st.subheader("2. Numpy")
+st.write("""
+NumPy is the fundamental package for numerical computation in Python. It provides support for arrays, matrices, and many mathematical functions.
+""")
+
+st.subheader("3. Pandas")
+st.write("""
+Pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames, which allow you to handle and analyze structured data efficiently.
+""")
+
+st.subheader("4. Matplotlib")
+st.write("""
+Matplotlib is a plotting library that provides tools to create static, animated, and interactive visualizations in Python.
+""")
+
+st.subheader("5. Seaborn")
+st.write("""
+Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics.
+""")
+
+st.subheader("6. Plotly")
+st.write("""
+Plotly is a graphing library that makes interactive, publication-quality graphs online. It supports many types of charts and visualizations.
+""")
+
+st.subheader("7. Graph Visualization")
+st.write("""
+Graph visualization involves the representation of data as nodes and edges. Libraries like NetworkX and Graphviz help in visualizing complex networks and relationships.
+""")
+
+# Footer
+st.write("---")
+st.write("Created by [Your Name] - A Roadmap to Becoming a Data Analysis Expert with Python")