Updated template

pages/Intro.py

@@ -0,0 +1,91 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+
+image_path_1 = 'C:/Users/DELL E5490/Desktop/project/data/.env/DataAnalysis.jpg'
+image_path_2 = 'C:/Users/DELL E5490/Desktop/project/data/.env/Data-Analytics-Life-Cycle.jpg'
+
+
+st.image(image_path_1, caption=None, use_column_width=True, clamp=True, channels="RGB", output_format="auto")
+
+
+st.title("Intuition of Data Analysis")
+
+if 'show_data' not in st.session_state:
+    st.session_state.show_data = False
+if 'show_analysis' not in st.session_state:
+    st.session_state.show_analysis = False
+if 'show_python' not in st.session_state:
+    st.session_state.show_python = False
+if 'show_life_cycle' not in st.session_state:
+    st.session_state.show_life_cycle = False
+
+
+def toggle(section):
+    st.session_state.show_data = False
+    st.session_state.show_analysis = False
+    st.session_state.show_python = False
+    st.session_state.show_life_cycle = False
+
+    if section == 'data':
+        st.session_state.show_data = True
+    elif section == 'analysis':
+        st.session_state.show_analysis = True
+    elif section == 'python':
+        st.session_state.show_python = True
+    elif section == 'life_cycle':
+        st.session_state.show_life_cycle = True
+
+
+if st.button('What is Data 📊?'):
+    toggle('data')
+
+if st.session_state.show_data:
+    st.write("Data consists of facts and information that are used to generate insights and conclusions.")
+
+
+if st.button('What is Data Analysis 📈?'):
+    toggle('analysis')
+
+if st.session_state.show_analysis:
+    st.write("""Data analysis is a comprehensive process that involves collecting, cleaning, transforming, integrating, reducing, and validating data to uncover meaningful insights. Python is a versatile and powerful tool for data analysis due to its extensive libraries, automation capabilities, and strong community support. By following a structured roadmap for data analysis, including univariate, bivariate, and multivariate analyses, you can effectively explore and understand your data to make informed decisions.""")
+
+
+if st.button('Why Use Python for Data Analysis 🤔?'):
+    toggle('python')
+
+if st.session_state.show_python:
+    st.markdown("""
+    While tools like Excel and Power BI are commonly used for data visualization and analysis, Python is a powerful language for data analysis due to several reasons:
+
+    - *Versatility*: Python can handle a wide variety of data types and sources, making it suitable for diverse data analysis tasks.
+    - *Libraries*: Python has extensive libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn that facilitate data manipulation, analysis, and visualization.
+    - *Automation*: Python allows for automation of repetitive tasks and complex computations, improving efficiency and productivity.
+    - *Community Support*: Python has a large and active community which means abundant resources, tutorials, and support.
+    """)
+
+    advantages = ['Versatility', 'Libraries', 'Automation', 'Community Support']
+    scores = [8, 9, 7, 9] 
+
+    fig, ax = plt.subplots()
+    ax.barh(advantages, scores, color=['blue', 'green', 'orange', 'red'])
+    ax.set_xlabel('Importance')
+    ax.set_title('Advantages of Python for Data Analysis')
+
+    st.pyplot(fig)
+
+
+if st.button('Data Analysis Life Cycle 🔄'):
+    toggle('life_cycle')
+
+if st.session_state.show_life_cycle:
+    st.markdown("""
+    The data analysis life cycle includes the following steps:
+
+    - **Case Scenario**: Define the problem statement and the objectives of the analysis.
+    - **Collect the Data**: If no data is available, fetch the data from relevant sources.
+    - **Data Understanding**: Preprocess the data by understanding its shape, data types, and description.
+    - **Data Cleaning**: Clean the null values in the data using imputation methods to ensure accurate analysis.
+    - **Data Visualization**: Understand the underlying patterns of the data through univariate and bivariate analyses.
+    - **Outlier Detection**: Remove outliers that create biases in the results.
+    - **Data Transformation**: Apply transformations for better understanding and analysis of the data.
+    """)