pages/4Simple EDA.py

import streamlit as st
import pandas as pd

st.markdown("""
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    """, unsafe_allow_html=True)

st.header(":red[📊 Simple EDA 💬]")

# Introduction to Simple EDA
st.markdown("<div class='section'>", unsafe_allow_html=True)
st.markdown("<h2 class='title'>🔍 Understanding Simple EDA</h2>", unsafe_allow_html=True)
st.markdown("<p class='subtitle'>Evaluating raw text data quality before processing</p>", unsafe_allow_html=True)

st.info("📌 **Simple EDA is a crucial step in the NLP lifecycle:**\n\n✅ Ensures raw data quality\n\n✅ Not dependent on problem statement\n\n✅ Helps in better data exploration")

st.markdown("</div>", unsafe_allow_html=True)

st.subheader(":violet[📃 Major Simple EDA Steps]")

st.markdown("✅ **Check Text Case** – Identify if text is in **lowercase, uppercase, or mixed case**.")
st.markdown("✅ **Detect HTML & URL Tags** – Analyze if text contains unwanted elements.")
st.markdown("✅ **Identify URLs** – Ensure URLs are either preserved or removed based on problem statement.")
st.markdown("✅ **Detect Mentions & Hashtags** – Find occurrences of `@mentions` or `#hashtags`.")
st.markdown("✅ **Identify Numeric Data** – Detect if text includes **digits or numerical data**.")
st.markdown("✅ **Analyze Punctuation Usage** – Check whether punctuation marks affect text clarity.")
st.markdown("✅ **Detect Emojis** – Ensure **emoji-based sentiments** are not lost.")
st.markdown("✅ **Analyze Date/Time Formats** – Identify the presence of date/time-related text.")

st.success("🚀 Performing **Simple EDA** ensures structured and high-quality text data, leading to better NLP model performance!")


st.code('''
            import pandas as pd
            import numpy as np
            import re
            import emoji
            
            def simple_eda(data,column):
                lower_upper = data[column].apply(lambda x:True if (x.lower()) or (x.upper()) else False).sum()
                tags = data[column].apply(lambda x:True if re.search("<.*?>",x) else False).sum()
                urls = data[column].apply(lambda x:True if re.search("https://\S+",x) else False).sum()
                mails = data[column].apply(lambda x:True if re.search("\S+@\S+",x) else False).sum()
                mentions = data[column].apply(lambda x:True if re.search("\B[@#]\S+",x) else False).sum()
                emojis = data[column].apply(lambda x:True if emoji.emoji_count(x) else False).sum()
                digit = data[column].apply(lambda x:True if re.search("\d",x) else False).sum()
                punc = data[column].apply(lambda x:True if re.search('[!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~]',x) else False).sum()
                dates = data[column].apply(lambda x:True if re.search(r"^[0-9]{1,2}\/[0-9]{1,2}\/[0-9]{4}$",x) else False).sum()
    
                if lower_upper >0:
                    print("text have combination")
                if tags > 0:
                    print("text have tags")
                if urls >0:
                    print("text have urls")
                if mails > 0:
                    print("text have mails")
                if mentions >0:
                    print("text have mentions")
                if emojis > 0:
                    print("text have emojis")
                if digit >0:
                    print("text have digit")
                if punc > 0:
                    print("text have punctuations")
                if dates >0:
                    print("text have dates")
        
''')

st.markdown('''
- By the following code we will check the exploration of the data 
- Basically it gives the quality of collected text data
- After the simple eda we will perform pre-processing on text based on problem statement after knowing quality of the data
''')