filename string | title string | code string | libraries string | chart_types string | metric_count int64 | has_dataframe int64 | file_size int64 | token_count int64 |
|---|---|---|---|---|---|---|---|---|
ab-testing.py | Ab Testing | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
st.set_page_config(page_title="A/B Testing Dashboard", layout="wide")
st.title("A/B Testing Dashboard")
st.markdown("Statistical significance, confidence intervals & experiment analysis")
np.ran... | plotly | Bar;annotations;bar;error-bars;violin | 4 | 1 | 2,757 | 927 |
agriculture-analytics.py | Agriculture Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Agriculture Analytics", layout="wide")
st.title("Agriculture Analytics")
st.markdown("Crop yields, soil health & farm operations")
np.random.seed(72)
crops = ["Corn", "Wheat", "Rice", "Soybeans", "... | plotly | annotations;bar;pie | 4 | 1 | 2,178 | 734 |
ai-metrics.py | Ai Metrics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="AI Metrics Dashboard", layout="wide")
st.title("AI Metrics Dashboard")
st.markdown("Model performance, training metrics & inference analytics")
np.random.seed(86)
models = ["GPT-4", "Claude 3", "Ge... | plotly | line;pie;scatter | 4 | 1 | 2,543 | 865 |
airline-traffic.py | Airline Traffic | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Airline Traffic Dashboard", layout="wide")
st.title("Airline Traffic Dashboard")
st.markdown("Stacked area charts, load factor trends & airline perfor... | plotly | Scatter;bar;multi-axis;stacked | 4 | 1 | 2,905 | 968 |
astronomy-dashboard.py | Astronomy Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Astronomy Dashboard", layout="wide")
st.title("Astronomy Dashboard")
st.markdown("Observatory data, celestial discoveries & space research")
np.random.seed(91)
yea... | plotly | Scatter;bar;pie;stacked | 4 | 1 | 2,824 | 938 |
box-office.py | Box Office | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Box Office Analytics", layout="wide")
st.title("Box Office Analytics")
st.markdown("Film industry revenue, audience trends & studio performance")
np.random.seed(77)
genres = ["Action", "Comedy", "D... | plotly | area;bar;pie | 4 | 1 | 2,422 | 815 |
climate-change.py | Climate Change | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Climate Change Dashboard", layout="wide")
st.title("Climate Change Dashboard")
st.markdown("Multi-axis time series, stacked area & annotated climate i... | plotly | Bar;Scatter;annotations;multi-axis;stacked | 4 | 0 | 2,875 | 965 |
climate-dashboard.py | Climate Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Climate Dashboard", layout="wide")
st.title("Climate Dashboard")
st.markdown("Global temperature, CO2 levels & climate indicators")
np.random.seed(73... | plotly | Scatter;area;multi-axis;pie | 4 | 0 | 2,203 | 740 |
cohort-analysis.py | Cohort Analysis | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Cohort Analysis", layout="wide")
st.title("Cohort Analysis")
st.markdown("Retention heatmaps, customer lifetime value & cohort tracking")
np.random.seed(99)
cohort... | plotly | Heatmap;Scatter;bar | 4 | 0 | 2,166 | 734 |
covid-dashboard.py | Covid Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="COVID Dashboard", layout="wide")
st.title("COVID Dashboard")
st.markdown("Pandemic trends: logarithmic scales, stacked area, annotation zones & Rt val... | plotly | Scatter;annotations;multi-axis | 4 | 1 | 3,280 | 1,089 |
crm-analytics.py | Crm Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="CRM Analytics", layout="wide")
st.title("CRM Analytics")
st.markdown("Sales pipeline, customer lifecycle & account management")
np.random.seed(68)
stages = ["Lead", "Qualified", "Demo", "Proposal",... | plotly | annotations;bar;funnel;histogram | 4 | 1 | 2,444 | 812 |
crypto-tracker.py | Crypto Tracker | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Crypto Tracker", layout="wide")
st.title("Crypto Tracker")
st.markdown("Real-time cryptocurrency prices, volumes & portfolio tracking")
np.random.seed(44)
dates = ... | plotly | Scatter;bar;pie | 4 | 0 | 2,051 | 693 |
customer-segments.py | Customer Segments | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Customer Segmentation", layout="wide")
st.title("Customer Segmentation")
st.markdown("Scatter plots, cluster analysis & demographic insights")
np.random.seed(95)
n... | plotly | Table;scatter;violin | 4 | 1 | 2,734 | 891 |
demographic-data.py | Demographic Data | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Demographic Data", layout="wide")
st.title("Demographic Data")
st.markdown("Population distribution, income levels & socioeconomic indicators")
np.random.seed(64)
age_groups = ["0-14", "15-24", "25... | plotly | bar;pie | 4 | 1 | 2,419 | 815 |
ecommerce-dashboard.py | Ecommerce Dashboard | import streamlit as st
import pandas as pd
import numpy as np
st.set_page_config(page_title="Ecommerce Dashboard", layout="wide")
st.title("Ecommerce Dashboard")
st.markdown("Online store analytics, sales funnel & customer insights")
np.random.seed(47)
months = pd.date_range("2025-07-01", periods=12, freq="ME")
categ... | streamlit-builtin | basic | 4 | 1 | 1,699 | 573 |
education-analytics.py | Education Analytics | import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
st.set_page_config(page_title="Education Analytics", layout="wide")
st.title("Education Analytics")
st.markdown("Student performance, enrollment trends & institutional KPIs")
np.random.seed(50)
depts = ["Engineering", "Business", "Arts... | altair | basic | 4 | 1 | 2,214 | 739 |
election-polls.py | Election Polls | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
st.set_page_config(page_title="Election Polls Dashboard", layout="wide")
st.title("Election Polls Dashboard")
st.markdown("Polling data, swing states & electoral projections with error margins")
... | plotly | Scatter;annotations;bar | 4 | 1 | 3,142 | 1,050 |
email-marketing.py | Email Marketing | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Email Marketing", layout="wide")
st.title("Email Marketing")
st.markdown("Campaign analytics, deliverability & engagement metrics")
np.random.seed(67)
campaigns = [f"Campaign {chr(65+i)}" for i in ... | plotly | bar;funnel;line | 4 | 1 | 2,127 | 716 |
energy-grid.py | Energy Grid | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Energy Grid Dashboard", layout="wide")
st.title("Energy Grid Dashboard")
st.markdown("Mixed chart types: stacked area, multi-axis bar+line, gauge indi... | plotly | Scatter;multi-axis;stacked | 4 | 1 | 3,233 | 1,087 |
energy-monitor.py | Energy Monitor | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Energy Monitor", layout="wide")
st.title("Energy Monitor")
st.markdown("Generation, consumption & grid performance analytics")
np.random.seed(61)
hou... | plotly | Scatter;annotations;multi-axis;stacked | 4 | 1 | 2,845 | 952 |
enterprise-demo.py | Enterprise Demo | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
st.set_page_config(page_title="Enterprise Command Center", layout="wide")
st.title("Enterprise Command Center")
st.markdown("Executive dashboard: all cha... | plotly | Bar;Indicator;Pie;Scatter;Table;annotations;multi-axis | 4 | 0 | 3,636 | 1,201 |
environment-monitor.py | Environment Monitor | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Environment Monitor", layout="wide")
st.title("Environment Monitor")
st.markdown("Air quality, pollution levels & environmental indicators")
np.random.seed(56)
cit... | plotly | Scatter;bar | 4 | 1 | 2,492 | 835 |
esports-dashboard.py | Esports Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Esports Dashboard", layout="wide")
st.title("Esports Dashboard")
st.markdown("Competitive gaming stats, team rankings & tournament data")
np.random.seed(81)
games = ["Valorant", "CS2", "LoL", "Dota... | plotly | bar;histogram | 4 | 1 | 2,145 | 717 |
ev-adoption.py | Ev Adoption | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="EV Adoption Dashboard", layout="wide")
st.title("EV Adoption Dashboard")
st.markdown("Stacked area projections, annotation lines & adoption S-curves")... | plotly | Pie;Scatter;annotations;multi-axis | 4 | 1 | 2,988 | 989 |
financial-report.py | Financial Report | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Financial Report Dashboard", layout="wide")
st.title("Financial Report Dashboard")
st.markdown("Comprehensive: waterfall charts, multi-axis, decomposi... | plotly | Bar;Scatter;Waterfall;multi-axis | 4 | 1 | 3,574 | 1,163 |
fitness-dashboard.py | Fitness Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Fitness Dashboard", layout="wide")
st.title("Fitness Dashboard")
st.markdown("Activity tracking, workout analytics & health goals")
np.random.seed(82)
days = pd.date_range("2026-06-01", periods=14,... | plotly | annotations;bar;pie | 4 | 1 | 2,337 | 775 |
food-industry.py | Food Industry | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Food Industry Analytics", layout="wide")
st.title("Food Industry Analytics")
st.markdown("Restaurant performance, menu analytics & supply chain")
np.random.seed(55... | plotly | Scatter;annotations;bar;pie | 4 | 1 | 2,502 | 824 |
gaming-dashboard.py | Gaming Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
st.set_page_config(page_title="Gaming Dashboard", layout="wide")
st.title("Gaming Dashboard")
st.markdown("Player analytics, game performance & community metrics")
np.random.seed(53)
games = ["Valorant", "CS2", "LoL", "Dota 2", "Apex",... | altair | basic | 4 | 1 | 2,120 | 712 |
gdp-happiness.py | Gdp Happiness | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="GDP vs Happiness", layout="wide")
st.title("GDP vs Happiness")
st.markdown("Scatter plots with trendlines, bubble charts & cross-country analysis")
np.random.seed(... | plotly | bar;scatter | 4 | 1 | 2,802 | 927 |
global-market-share.py | Global Market Share | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Global Market Share", layout="wide")
st.title("Global Market Share")
st.markdown("Pie charts, donut charts & proportional data visualization")
np.random.seed(92)
s... | plotly | Bar;Barpolar;Pie;annotations;multi-axis;scatter | 4 | 1 | 2,811 | 915 |
health-metrics.py | Health Metrics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Health Metrics", layout="wide")
st.title("Health Metrics")
st.markdown("Patient vitals, lab results & health outcome tracking")
np.random.seed(45)
dates = pd.date_... | plotly | Scatter;histogram;pie | 4 | 1 | 2,301 | 770 |
housing-market.py | Housing Market | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Housing Market Dashboard", layout="wide")
st.title("Housing Market Dashboard")
st.markdown("Mixed chart: price index bar + volume line, multi-axis & s... | plotly | Bar;Pie;Scatter;multi-axis | 4 | 1 | 2,840 | 938 |
hr-analytics.py | Hr Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="HR Analytics", layout="wide")
st.title("HR Analytics")
st.markdown("Workforce metrics, attrition analysis & talent management")
np.random.seed(57)
depts = ["Engineering", "Sales", "Marketing", "HR"... | plotly | bar;scatter | 4 | 1 | 2,309 | 761 |
inventory-management.py | Inventory Management | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Inventory Management", layout="wide")
st.title("Inventory Management")
st.markdown("Stock levels, turnover rates & warehouse operations")
np.random.seed(69)
categories = ["Electronics", "Fashion", ... | plotly | annotations;bar;pie | 4 | 1 | 2,461 | 823 |
iot-sensors.py | Iot Sensors | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="IoT Sensors Dashboard", layout="wide")
st.title("IoT Sensors Dashboard")
st.markdown("Real-time sensor monitoring, threshold alerts & time series anom... | plotly | Pie;Scatter;Table;multi-axis | 4 | 1 | 3,218 | 1,080 |
kpi-dashboard.py | Kpi Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
st.set_page_config(page_title="Enterprise KPI Dashboard", layout="wide")
st.title("Enterprise KPI Dashboard")
st.markdown("Comprehensive business metrics: gauge charts, sparklines & target tracki... | plotly | Bar;Scatter;annotations | 4 | 1 | 3,198 | 1,068 |
lab-dashboard.py | Lab Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Lab Dashboard", layout="wide")
st.title("Lab Dashboard")
st.markdown("Experiment tracking, sample analysis & research metrics")
np.random.seed(65)
ex... | plotly | Histogram;Scatter;annotations;multi-axis | 4 | 1 | 2,670 | 876 |
logistics-dashboard.py | Logistics Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Logistics Dashboard", layout="wide")
st.title("Logistics Dashboard")
st.markdown("Fleet tracking, delivery performance & route analytics")
np.random.seed(71)
regions = ["Northeast", "Southeast", "M... | plotly | area;pie;scatter | 4 | 1 | 2,666 | 892 |
manufacturing-dashboard.py | Manufacturing Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Manufacturing Dashboard", layout="wide")
st.title("Manufacturing Dashboard")
st.markdown("Production output, machine efficiency & quality control")
n... | plotly | Scatter;bar;multi-axis;scatter | 4 | 1 | 2,580 | 863 |
manufacturing-oee.py | Manufacturing Oee | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
st.set_page_config(page_title="Manufacturing OEE Dashboard", layout="wide")
st.title("Manufacturing OEE Dashboard")
st.markdown("Overall Equipment Effectiveness: gauge charts, waterfall & pareto ... | plotly | Bar;Indicator;Scatter;annotations;multi-axis | 4 | 1 | 3,238 | 1,058 |
marketing-analytics.py | Marketing Analytics | import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
st.set_page_config(page_title="Marketing Analytics", layout="wide")
st.title("Marketing Analytics")
st.markdown("Campaign performance, channel attribution & ROI tracking")
np.random.seed(43)
channels = ["Search", "Social", "Email", "Di... | altair | basic | 4 | 1 | 1,855 | 618 |
mindfulness.py | Mindfulness | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Mindfulness Dashboard", layout="wide")
st.title("Mindfulness Dashboard")
st.markdown("Meditation practice, mood tracking & wellness metrics")
np.random.seed(84)
days = pd.date_range("2026-06-01", p... | plotly | annotations;line;pie;scatter | 4 | 1 | 2,103 | 701 |
ml-model-comparison.py | Ml Model Comparison | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
st.set_page_config(page_title="ML Model Comparison", layout="wide")
st.title("ML Model Comparison")
st.markdown("Model performance metrics, confusion mat... | plotly | Heatmap;Scatter;Scatterpolar;multi-axis;scatter | 4 | 1 | 2,818 | 964 |
music-streaming.py | Music Streaming | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Music Streaming Analytics", layout="wide")
st.title("Music Streaming Analytics")
st.markdown("Play counts, listener demographics & content performance")
np.random.seed(52)
genres = ["Pop", "Hip Hop... | plotly | area;bar;pie | 4 | 1 | 2,289 | 773 |
netflix-content.py | Netflix Content | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Netflix Content Analytics", layout="wide")
st.title("Netflix Content Analytics")
st.markdown("Content catalog, genre distribution & viewer engagement metrics")
np.... | plotly | bar;scatter;treemap | 4 | 1 | 2,609 | 872 |
news-dashboard.py | News Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="News Dashboard", layout="wide")
st.title("News Dashboard")
st.markdown("Editorial analytics, audience engagement & content performance")
np.random.seed(88)
sections = ["Politics", "Tech", "Sports",... | plotly | area;bar;pie | 4 | 1 | 2,391 | 791 |
nft-market.py | Nft Market | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="NFT Market Dashboard", layout="wide")
st.title("NFT Market Dashboard")
st.markdown("Floor prices, collection stats & marketplace trends")
np.random.seed(85)
collections = ["Azuki", "Bored Ape", "Pu... | plotly | bar;line;pie | 4 | 1 | 2,267 | 760 |
ocean-health.py | Ocean Health | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Ocean Health", layout="wide")
st.title("Ocean Health")
st.markdown("Marine ecosystem data, sea temperatures & biodiversity")
np.random.seed(74)
oceans = ["Pacific", "Atlantic", "Indian", "Southern"... | plotly | annotations;bar;line | 4 | 1 | 2,425 | 816 |
olympic-medals.py | Olympic Medals | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Olympic Medals Dashboard", layout="wide")
st.title("Olympic Medals Dashboard")
st.markdown("Stacked bar charts, medal distribution & country performance")
np.rando... | plotly | bar;scatter;treemap | 4 | 1 | 2,311 | 783 |
pharma-trials.py | Pharma Trials | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
st.set_page_config(page_title="Pharma Trials Dashboard", layout="wide")
st.title("Pharma Trials Dashboard")
st.markdown("Bar charts with error ranges, trial phases & drug pipeline analytics")
np... | plotly | Bar;annotations;error-bars;funnel;scatter | 4 | 1 | 2,710 | 913 |
podcast-analytics.py | Podcast Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Podcast Analytics", layout="wide")
st.title("Podcast Analytics")
st.markdown("Episode performance, listener stats & content analytics")
np.random.seed(79)
episodes = [f"Episode {i}" for i in range(... | plotly | bar;line;pie | 4 | 1 | 2,153 | 721 |
population-pyramid.py | Population Pyramid | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Population Pyramid", layout="wide")
st.title("Population Pyramid")
st.markdown("Horizontal stacked bar charts for age-sex demographic distributions")
np.random.see... | plotly | Bar;Scatter;bar | 4 | 0 | 2,879 | 961 |
portfolio-risk.py | Portfolio Risk | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Portfolio Risk Matrix", layout="wide")
st.title("Portfolio Risk Matrix")
st.markdown("Bubble matrix, correlation heatmap & risk-return profiling")
np.random.seed(1... | plotly | Heatmap;Pie;annotations;scatter | 4 | 1 | 2,450 | 823 |
project-management.py | Project Management | import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
st.set_page_config(page_title="Project Management", layout="wide")
st.title("Project Management")
st.markdown("Portfolio tracking, sprint burndown & resource allocation")
np.random.seed(58)
projects = ["Atlas", "Nebula", "Phoenix", "Od... | altair | basic | 4 | 1 | 2,394 | 799 |
public-health.py | Public Health | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Public Health Dashboard", layout="wide")
st.title("Public Health Dashboard")
st.markdown("Population health metrics, disease surveillance & vaccination rates")
np.random.seed(63)
regions = ["Northe... | plotly | annotations;bar;line;pie | 4 | 1 | 2,490 | 835 |
publishing-analytics.py | Publishing Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Publishing Analytics", layout="wide")
st.title("Publishing Analytics")
st.markdown("Book sales, readership trends & publishing metrics")
np.random.seed(78)
genres = ["Fiction", "Non-Fiction", "Myst... | plotly | bar;line;pie | 4 | 1 | 2,425 | 813 |
real-estate-dashboard.py | Real Estate Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Real Estate Dashboard", layout="wide")
st.title("Real Estate Dashboard")
st.markdown("Property listings, market trends & investment analytics")
np.random.seed(51)
cities = ["San Francisco", "New Yo... | plotly | bar;scatter | 4 | 1 | 2,270 | 749 |
restaurant-pos.py | Restaurant Pos | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Restaurant POS Dashboard", layout="wide")
st.title("Restaurant POS Dashboard")
st.markdown("Daily sales, menu performance & operational metrics")
np.random.seed(90)
days = pd.date_range("2026-06-01... | plotly | annotations;bar;line;pie | 4 | 1 | 2,526 | 854 |
retail-analytics.py | Retail Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
st.set_page_config(page_title="Retail Analytics Dashboard", layout="wide")
st.title("Retail Analytics Dashboard")
st.markdown("Complex multi-chart: mixed... | plotly | Scatter;bar;line;multi-axis | 4 | 1 | 3,080 | 1,040 |
sales-dashboard.py | Sales Dashboard | import streamlit as st
import pandas as pd
import numpy as np
st.set_page_config(page_title="Sales Dashboard", layout="wide")
st.title("Sales Dashboard")
st.markdown("Real-time revenue metrics and sales performance tracking")
np.random.seed(42)
months = pd.date_range("2025-07-01", periods=12, freq="ME")
sales = np.ra... | streamlit-builtin | basic | 4 | 1 | 1,633 | 560 |
sales-funnel.py | Sales Funnel | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
st.set_page_config(page_title="Sales Funnel Dashboard", layout="wide")
st.title("Sales Funnel Dashboard")
st.markdown("Funnel charts, conversion tracking & lead progression analytics")
np.random... | plotly | Funnel;bar | 4 | 1 | 2,357 | 794 |
seo-analytics.py | Seo Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="SEO Analytics", layout="wide")
st.title("SEO Analytics")
st.markdown("Search rankings, organic traffic & keyword performance")
np.random.seed(66)
keywords = ["data analytics", "cloud computing", "A... | plotly | area;line;pie | 4 | 1 | 2,297 | 767 |
sleep-analytics.py | Sleep Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Sleep Analytics", layout="wide")
st.title("Sleep Analytics")
st.markdown("Sleep quality, duration tracking & circadian rhythm")
np.random.seed(83)
days = pd.date_range("2026-06-01", periods=14, fre... | plotly | annotations;bar;line;pie | 4 | 1 | 2,352 | 782 |
soc-dashboard.py | Soc Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Security Operations Dashboard", layout="wide")
st.title("Security Operations Dashboard")
st.markdown("Threat detection, incident response & vulnerabil... | plotly | Bar;Scatter;bar;multi-axis;pie | 4 | 1 | 2,781 | 915 |
social-media-dashboard.py | Social Media Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
st.set_page_config(page_title="Social Media Dashboard", layout="wide")
st.title("Social Media Dashboard")
st.markdown("Cross-platform engagement, audience growth & content performance")
np.random.seed(48)
platforms = ["Instagram", "Tik... | altair;streamlit-builtin | basic | 4 | 1 | 2,190 | 731 |
social-sentiment.py | Social Sentiment | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Social Sentiment Dashboard", layout="wide")
st.title("Social Sentiment Dashboard")
st.markdown("Multi-metric sentiment tracking, volume & trending top... | plotly | Bar;Scatter;Scatterpolar;annotations;multi-axis | 4 | 1 | 2,774 | 920 |
sp500-analysis.py | Sp500 Analysis | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="S&P 500 Analysis", layout="wide")
st.title("S&P 500 Analysis")
st.markdown("Time series with recession annotations, volatility bands & risk metrics")
... | plotly | Scatter;annotations;bar;multi-axis | 4 | 0 | 2,920 | 964 |
space-mission.py | Space Mission | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Space Mission Dashboard", layout="wide")
st.title("Space Mission Dashboard")
st.markdown("Launch data, mission stats & astronomical discoveries")
np.random.seed(76)
agencies = ["NASA", "SpaceX", "E... | plotly | annotations;area;bar;pie | 4 | 1 | 2,559 | 860 |
sports-stats.py | Sports Stats | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Sports Analytics", layout="wide")
st.title("Sports Analytics")
st.markdown("Team performance, player stats & season trends")
np.random.seed(49)
teams = ["Lakers", "Celtics", "Warriors", "Bucks", "N... | plotly | bar;histogram;scatter | 4 | 1 | 2,228 | 747 |
stock-market.py | Stock Market | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Stock Market Dashboard", layout="wide")
st.title("Stock Market Dashboard")
st.markdown("Time series, candlestick-like visualization & multi-axis chart... | plotly | Bar;Histogram;Scatter;multi-axis | 4 | 1 | 2,846 | 923 |
supply-chain.py | Supply Chain | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
st.set_page_config(page_title="Supply Chain Dashboard", layout="wide")
st.title("Supply Chain Dashboard")
st.markdown("Inventory levels, logistics & supplier performance")
np.random.seed(60)
warehouses = ["Chicago", "Atlan... | plotly | Pie;Scatter;stacked | 4 | 1 | 2,645 | 875 |
support-dashboard.py | Support Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Support Dashboard", layout="wide")
st.title("Support Dashboard")
st.markdown("Ticket analytics, response times & customer satisfaction")
np.random.seed(59)
days = pd.date_range("2026-06-01", period... | plotly | annotations;histogram;line;pie | 4 | 1 | 2,406 | 801 |
transport-analytics.py | Transport Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Transport Analytics", layout="wide")
st.title("Transport Analytics")
st.markdown("Traffic patterns, fleet performance & route optimization")
np.random.seed(62)
routes = [f"Route {i}" for i in range... | plotly | area;bar;scatter | 4 | 1 | 2,525 | 848 |
travel-insights.py | Travel Insights | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Travel Insights", layout="wide")
st.title("Travel Insights")
st.markdown("Destination analytics, booking trends & tourism metrics")
np.random.seed(54)
destinations = ["Paris", "Tokyo", "Bali", "New... | plotly | bar;line;scatter | 4 | 1 | 2,351 | 774 |
university-dashboard.py | University Dashboard | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="University Dashboard", layout="wide")
st.title("University Dashboard")
st.markdown("Campus analytics, academic performance & institutional data")
np.random.seed(89)
depts = ["Engineering", "Busines... | plotly | annotations;bar;histogram;scatter | 4 | 1 | 2,361 | 783 |
vc-investments.py | Vc Investments | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="VC Investments Dashboard", layout="wide")
st.title("VC Investments Dashboard")
st.markdown("Bubble charts showing funding rounds, valuations & sector trends")
np.random.seed(94)
sectors = ["AI/ML",... | plotly | bar;scatter | 4 | 1 | 2,832 | 943 |
water-resources.py | Water Resources | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
st.set_page_config(page_title="Water Resources Dashboard", layout="wide")
st.title("Water Resources Dashboard")
st.markdown("Polar/radar charts, stacked bar & water quality monitoring")
np.rando... | plotly | Bar;Barpolar;Scatter;Scatterpolar | 4 | 1 | 2,612 | 854 |
weather-station.py | Weather Station | import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
st.set_page_config(page_title="Weather Station", layout="wide")
st.title("Weather Station")
st.markdown("Live meteorological data, forecasts & climate trends")
np.random.seed(46)
d... | plotly | Bar;Scatter;Scatterpolar;annotations;multi-axis | 4 | 1 | 2,504 | 819 |
wildlife-tracking.py | Wildlife Tracking | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="Wildlife Tracking", layout="wide")
st.title("Wildlife Tracking")
st.markdown("Animal populations, migration patterns & conservation status")
np.random.seed(75)
species_list = ["African Elephant", "... | plotly | bar;line;pie | 4 | 1 | 2,461 | 825 |
youtube-analytics.py | Youtube Analytics | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
st.set_page_config(page_title="YouTube Analytics", layout="wide")
st.title("YouTube Analytics")
st.markdown("Channel performance, video analytics & audience insights")
np.random.seed(80)
videos = [f"Video {chr(65+i)}" for i in r... | plotly | bar;line;pie | 4 | 1 | 2,164 | 735 |
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