Update pages/Simple EDA.py

pages/Simple EDA.py

@@ -1,33 +1,101 @@
 import streamlit as st
 import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
 
-# EDA and Feature Engineering Page
-st.title("Simple EDA")
+# Configure the Streamlit app
+st.title("Exploratory Data Analysis (EDA) App")
 st.markdown("""
-
-By performing simple Exploratory Data Analysis (EDA), we can examine the data, identify patterns, and detect anomalies or inconsistencies. This process allows us to clean and preprocess the dataset effectively, ensuring it is well-structured and ready for further analysis or modeling..
+This app allows you to perform basic EDA on your dataset. 
+Upload your dataset to explore, clean, and visualize your data interactively.
 """)
 
-# File uploader for dataset
-uploaded_file = st.file_uploader("Upload your dataset (CSV format):", type=["csv"])
+# File upload
+uploaded_file = st.file_uploader("Upload your dataset (CSV format):", type="csv")
+
+if uploaded_file:
+    # Load dataset
+    df = pd.read_csv(uploaded_file)
+    st.subheader("Dataset Overview")
+    st.write("First 5 Rows of the Dataset:")
+    st.write(df.head())
+
+    # Basic Information
+    st.subheader("Basic Information about the Dataset")
+    st.write("Structure of the Dataset:")
+    buffer = []
+    df.info(buf=buffer)
+    st.text("".join(buffer))
+    
+    st.write("Summary of Numeric Columns:")
+    st.write(df.describe())
+
+    st.write("Data Types of Each Column:")
+    st.write(df.dtypes)
+
+    # Missing Values
+    st.subheader("Missing Values")
+    st.write("Number of Missing Values per Column:")
+    st.write(df.isnull().sum())
+
+    # Duplicate Rows
+    st.subheader("Duplicate Rows")
+    st.write(f"Number of Duplicate Rows: {df.duplicated().sum()}")
+
+    # Visualize Numeric Columns
+    st.subheader("Numeric Column Visualizations")
+    st.write("Histograms:")
+    fig, ax = plt.subplots(figsize=(10, 8))
+    df.hist(ax=ax)
+    st.pyplot(fig)
+
+    st.write("Boxplot:")
+    fig, ax = plt.subplots()
+    sns.boxplot(data=df, orient='h', ax=ax)
+    st.pyplot(fig)
+
+    # Categorical Column Analysis
+    categorical_columns = df.select_dtypes(include=['object', 'category']).columns
+    if len(categorical_columns) > 0:
+        st.subheader("Categorical Column Analysis")
+        selected_cat_col = st.selectbox("Select a Categorical Column to Analyze", categorical_columns)
 
-if uploaded_file is not None:
-    # Read and display the dataset
-    data = pd.read_csv(uploaded_file)
-    st.write("### Uploaded Dataset:")
-    st.dataframe(data)
+        st.write(f"Value Counts for {selected_cat_col}:")
+        st.write(df[selected_cat_col].value_counts())
 
-    # Overview of the dataset
-    st.write("### Dataset Overview:")
-    st.write(data.describe())
+        st.write(f"Bar Plot for {selected_cat_col}:")
+        fig, ax = plt.subplots()
+        sns.countplot(x=selected_cat_col, data=df, ax=ax)
+        st.pyplot(fig)
+    else:
+        st.write("No categorical columns available in the dataset.")
 
-    # Missing values in the dataset
-    st.write("### Missing Values:")
-    st.write(data.isnull().sum())
+    # Correlation Matrix
+    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns
+    if len(numeric_columns) > 1:
+        st.subheader("Correlation Analysis")
+        st.write("Correlation Matrix:")
+        correlation_matrix = df[numeric_columns].corr()
+        st.write(correlation_matrix)
 
-    # Correlation matrix
-    st.write("### Correlation Matrix:")
-    st.write(data.corr())
+        st.write("Heatmap of Correlation Matrix:")
+        fig, ax = plt.subplots()
+        sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', ax=ax)
+        st.pyplot(fig)
+    else:
+        st.write("Not enough numeric columns for correlation analysis.")
 
+    # Save Cleaned Data
+    st.subheader("Save Cleaned Dataset")
+    if st.button("Save Dataset (after removing duplicates)"):
+        cleaned_df = df.drop_duplicates()
+        cleaned_csv = cleaned_df.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="Download Cleaned Dataset",
+            data=cleaned_csv,
+            file_name="cleaned_dataset.csv",
+            mime="text/csv"
+        )
+        st.success("Cleaned dataset is ready for download!")
 else:
-    st.warning("Please upload a dataset to proceed with EDA.")
+    st.info("Please upload a CSV file to get started.")