Datasets:
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Text Generation
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language-modeling
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License:
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 |
End of preview. Expand in Data Studio
Streamlit Dashboard Dataset
80 Streamlit dashboard Python scripts for training code generation models.
Structure
data/train-00000-of-00001.parquet— Main dataset in Parquet formatREADME.md— Dataset card
Columns
| Column | Type | Description |
|---|---|---|
| filename | string | File name of the dashboard |
| title | string | Human-readable title |
| code | string | Full Python source code |
| libraries | string | Semi-colon separated libraries used |
| chart_types | string | Semi-colon separated chart types |
| metric_count | int32 | Number of st.metric() calls |
| has_dataframe | bool | Whether st.dataframe() is used |
| file_size | int32 | File size in bytes |
| token_count | int32 | Approximate token count |
Usage
from datasets import load_dataset
dataset = load_dataset("sanjaymalladi/streamlit-dataset", split="train")
print(dataset[0]["filename"])
print(dataset[0]["code"][:200])
Domains
Sales, Marketing, Crypto, Health, Weather, Ecommerce, Social Media, Sports, Education, Real Estate, Music, Gaming, Travel, Food, Environment, HR, Projects, Support, Supply Chain, Energy, Transport, Public Health, Demographics, Lab, SEO, Email, CRM, Inventory, Manufacturing, Logistics, Agriculture, Climate, Ocean, Wildlife, Space, Film, Publishing, Podcast, YouTube, Esports, Fitness, Sleep, Mindfulness, NFT, AI, Security, News, University, Restaurant, Astronomy, Stocks, VC, Retail, Pharma, IoT, COVID, Elections, ML Models
Chart Libraries
- Plotly — 75 files (scatter, bar, line, pie, funnel, heatmap, waterfall, polar, etc.)
- Altair — 3 files (declarative statistical charts)
- Streamlit built-in — 2 files (st.line_chart, st.bar_chart, st.area_chart)
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