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| 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. | |
| """) |