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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")
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