2.1 Auto Dashboarding using Python.ipynb

Kernel: Python 3 (ipykernel)

• numpy==1.26.4

• pandas==2.2.2

• matplotlib==3.7.5

• seaborn==0.13.2

• scikit-learn==1.5.1

• autoviz==0.1.905

• dtale==3.13.0

• streamlit==1.38.0

• plotly==5.24.1

• transformers==4.44.2

!conda create -n genai_demo python=3.10 -y
!conda activate genai_demo
!pip install -r requirements.txt

pip install faker

Requirement already satisfied: faker in c:\users\suyashi144893\appdata\local\anaconda3\lib\site-packages (37.8.0)Note: you may need to restart the kernel to use updated packages.

Requirement already satisfied: tzdata in c:\users\suyashi144893\appdata\local\anaconda3\lib\site-packages (from faker) (2023.3)

pip install streamlit

!streamlit run streamlit_app.py --server.port 8502 --server.headless true

%%writefile streamlit_app.py
import streamlit as st
import pandas as pd
import numpy as np

# Title
st.title("📊 Auto Dashboard Demo")

# Generate random dataset
df = pd.DataFrame({
    "age": np.random.randint(20, 60, 50),
    "salary": np.random.randint(30000, 120000, 50),
    "department": np.random.choice(["HR", "Finance", "IT", "Marketing"], 50)
})

# Show dataframe
st.subheader("Dataset")
st.dataframe(df)

# Basic plots
st.subheader("Salary Distribution")
st.bar_chart(df["salary"])

st.subheader("Department Count")
st.bar_chart(df["department"].value_counts())

Overwriting streamlit_app.py

## !cp streamlit_app.py streamlit_app_v1.py  # Linux/Mac
# OR for Windows:
!copy streamlit_app.py streamlit_app_v1.py

        1 file(s) copied.

A fully interactive demo with multiple visuals and automatic data description. This will make it feel like a real intelligent data handling dashboard.

%%writefile streamlit_app.py
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px

st.set_page_config(page_title="AI Data Handling Dashboard", layout="wide")
st.title("📊 Intelligent Data Handling Dashboard")

# -------------------------------
# Generate Random Dataset
# -------------------------------
np.random.seed(42)
df = pd.DataFrame({
    "CustomerID": [f"CUST{i:03d}" for i in range(1, 101)],
    "Age": np.random.randint(18, 65, 100),
    "Salary": np.random.randint(30000, 120000, 100),
    "Department": np.random.choice(["HR", "Finance", "IT", "Marketing"], 100),
    "PurchaseAmount": np.round(np.random.uniform(50, 2000, 100), 2)
})

# -------------------------------
# Dataset Info & Description
# -------------------------------
st.subheader("🔍 Dataset Overview")
st.write("Shape:", df.shape)
st.write("Columns:", df.columns.tolist())
st.write("Data Types:")
st.write(df.dtypes)
st.write("Missing Values:")
st.write(df.isnull().sum())

st.subheader("📊 Statistical Summary")
st.dataframe(df.describe().round(2))

# -------------------------------
# Interactive Filters
# -------------------------------
st.subheader("⚡ Interactive Filters")
department_filter = st.multiselect("Select Department(s):", options=df["Department"].unique(), default=df["Department"].unique())
filtered_df = df[df["Department"].isin(department_filter)]
st.write(f"Filtered Data ({len(filtered_df)} rows)")
st.dataframe(filtered_df.head(10))

# -------------------------------
# Visualizations
# -------------------------------
st.subheader("📈 Visualizations")

col1, col2 = st.columns(2)

with col1:
    st.write("**Age Distribution**")
    fig_age = px.histogram(filtered_df, x="Age", nbins=10, title="Age Distribution")
    st.plotly_chart(fig_age, use_container_width=True)

with col2:
    st.write("**Salary Distribution**")
    fig_salary = px.histogram(filtered_df, x="Salary", nbins=10, title="Salary Distribution")
    st.plotly_chart(fig_salary, use_container_width=True)

st.write("**Purchase Amount vs Age**")
fig_scatter = px.scatter(filtered_df, x="Age", y="PurchaseAmount", color="Department", hover_data=["CustomerID"])
st.plotly_chart(fig_scatter, use_container_width=True)

df_count = filtered_df["Department"].value_counts().reset_index()
df_count.columns = ["Department", "Count"]

fig_count = px.bar(
    df_count,
    x="Department",
    y="Count",
    labels={"Department": "Department", "Count": "Number of Employees"},
    title="Department Count"
)
st.plotly_chart(fig_count, use_container_width=True)


st.write("**Salary Boxplot by Department**")
fig_box = px.box(filtered_df, x="Department", y="Salary", color="Department")
st.plotly_chart(fig_box, use_container_width=True)

Overwriting streamlit_app.py

!streamlit run streamlit_app.py --server.port 8502 --server.headless true

Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.

2025-09-18 14:54:51.823 Port 8502 is already in use

!streamlit run streamlit_app.py --server.port 8502 --server.headless False