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Data_Analysis
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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.
    """)