import streamlit as st
import matplotlib.pyplot as plt
import pandas as  pd
import plotly.graph_objects as go

image_path_1 = 'images/DataAnalysis.jpg'
image_path_2 = 'images/Data-Analytics-Life-Cycle.jpg'
st.image(image_path_1,caption=None, width=int, use_column_width=True, clamp=True, channels="RGB", output_format="auto")

st.title("Steps to get Intution of Data Analysis")

st.header("What is Data 📊?")
st.markdown("Data consists of facts and information that are used to generate insights and conclusions.")

st.header("What is Data Analysi📈?")
st.markdown("""
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.""")


st.header("Why Use Python for Data Analysis 🤔?")

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)

st.header("Data Analysis 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.
""")