Delete pages/page2.py

pages/page2.py

@@ -1,135 +0,0 @@
-import streamlit as st
-
-def background():
-    st.markdown(f"""
-    <style>
-        /* Set the background image for the entire app */
-        .stApp {{
-            background-color:rgba(96, 155, 124, 0.5);
-            background-size: 1300px;
-            background-repeat: no-repeat;
-            background-attachment: fixed;
-            background-position: center;
-        }}
-        
-        </style>
-    """, unsafe_allow_html=True)
-def page2():
-    background()
-    st.title("Data Collection")
-    st.header("1. What is Data?")
-    st.write(
-        "Data refers to raw facts and figures that are collected, stored, and analyzed to derive insights. "
-        "It serves as the foundation for any machine learning model."
-    )
-    
-    st.header("2. Types of Data")
-    data_type = st.radio(
-        "Select a type of data to learn more:",
-        ("Structured", "Unstructured", "Semi-Structured")
-    )
-    
-    if data_type == "Structured":
-        st.subheader("Structured Data")
-        st.write(
-            "Structured data is highly organized and easily searchable within databases. "
-            "It includes rows and columns, such as in relational databases."
-        )
-        
-        st.write("Data Formats:")
-        format_selected = st.radio(
-            "Select a format to explore further:",
-            ("Excel", "CSV")
-        )
-        
-        if format_selected == "Excel":
-            # Excel Data Format Section
-            st.subheader("Excel Data Format")
-            st.write("*What is it?*")
-            st.write(
-                "Excel files are spreadsheets used to organize and analyze data in rows and columns. "
-                "They are widely used due to their user-friendly nature and support for various data types."
-            )
-            
-            st.write("*How to Read Excel Files?*")
-            st.code(
-                """
-import pandas as pd
-# Reading an Excel file
-df = pd.read_excel('file.xlsx')
-print(df.head())
-                """,
-                language="python"
-            )
-            
-            st.write("*Common Issues When Handling Excel Files*")
-            st.write(
-                """
-- Missing or corrupted files
-- Version incompatibilities
-- Incorrect file paths
-- Handling large Excel files
-                """
-            )
-            
-            st.write("*How to Overcome These Errors/Issues?*")
-            st.write(
-                """
-- Use proper error handling with try-except.
-- Convert Excel files to CSV for better compatibility.
-- Use libraries like openpyxl or xlrd for specific Excel versions.
-- Break large files into smaller chunks for processing.
-                """
-            )
-            
-            # Button to open Jupyter Notebook or PDF
-            if st.button("Open Excel Documentation"):
-                st.write("Download the [documentation notebook](path/to/excel_notebook.ipynb) or [PDF](path/to/excel_documentation.pdf).")
-
-        elif format_selected == "CSV":
-            # CSV Data Format Section
-            st.subheader("CSV Data Format")
-            st.write("*What is it?*")
-            st.write(
-                "CSV (Comma-Separated Values) files store tabular data in plain text, where each line represents a record, "
-                "and fields are separated by commas."
-            )
-            
-            st.write("*How to Read CSV Files?*")
-            st.code(
-                """
-import pandas as pd
-# Reading a CSV file
-df = pd.read_csv('file.csv')
-print(df.head())
-                """,
-                language="python"
-            )
-            
-            st.write("*Common Issues When Handling CSV Files*")
-            st.write(
-                """
-- Encoding issues (e.g., UTF-8, ISO-8859-1)
-- Inconsistent delimiters
-- Missing or corrupted files
-- Large file sizes causing memory errors
-                """
-            )
-            
-            st.write("*How to Overcome These Errors/Issues?*")
-            st.write(
-                """
-- Specify the correct encoding when reading files using encoding='utf-8' or similar.
-- Use libraries like csv or pandas to handle different delimiters.
-- Employ error handling to catch and manage missing/corrupted files.
-- Use chunking to read large files in smaller parts: pd.read_csv('file.csv', chunksize=1000).
-                """
-            )
-            
-            # Button to open Jupyter Notebook or PDF
-            if st.button("Open CSV Documentation"):
-                st.write("Download the [documentation notebook](path/to/csv_notebook.ipynb) or [PDF](path/to/csv_documentation.pdf).")
-
-    if st.button("Go to Home Page"):
-        st.session_state.page = 'Page1'
-        st.experimental_rerun()