Here are the files to download the example Visualization Using the 2014 American Community Survey Data. Users will need to download Tableau Reader (http://www.tableau.com/products/reader) to view the Dashboard, but it is Free to Download and allows users the interactivity that dashboards provide.

This file is used by the SampleQC tableau workbook to provide insights on which samples passed QC. It is a subset of the file that is generated by the RNASeq pipeline where all the genes are dropped out.

Dataset Overview

Dataset Name: "Nuestro Amazon" E-Commerce Dataset

General Description: This dataset represents an e-commerce database containing information about products, categories, customers, orders, and more. The data is structured to facilitate analysis and insights into various aspects of an e-commerce business.

Structure and Attributes: The dataset consists of eight tables: categories, customers, employees, orders, ordersdetails, products, shippers, and suppliers. These tables encompass key information such as product details, customer information, order details.

Data Source: The data was generated for educational and demonstration purposes to simulate an e-commerce environment. It is not sourced from a real-world e-commerce platform.

Usage and Applications: This dataset can be utilized for various purposes, including market basket analysis, customer segmentation, sales trends analysis, and supply chain optimization. Analysts and data scientists can derive valuable insights to improve business strategies.

Acknowledgments and References: The dataset was created for educational use. No specific external sources were referenced for this dataset.

Explore Interactive Visualizations

"Quantity per country" in this Kaggle notebook or on Tableau.

"Orders by country" in this Kaggle notebook or on Tableau.

Data Analysis

"Data Analysis of Online Orders" in this Kaggle notebook

"Data Visualization and Analysis in R" in this Kaggle notebook

This data set contains a list of Olympic medals won by USA athletes between 2000 and 2012. The data elements are Athlete name, Year, Age, Country, Gold, Silver, Bronze, Total medals. Source of this data set from http://www.tableau.com/public/community/sample-data-sets.

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

Data for replication package Paper Title: Drivers of firm-government engagement for technology ventures Authors: Lauren Lanahan; Iman Hemmatian; Amol M. Joshi; Evan E. Johnson Lead Author of Data Curation, Code, & Analysis: Lauren Lanahan (llanahan@uoregon.edu) Software: Stata 18; Tableau Computational Requirements: We utilized a powerful server (32-core processors, 384 GB memory, 32 TB storage) to construct the sample and run the analyses. We provide the code and data for all empirical assessments. And for additional transparency, we provide the log file for the set of empirical assessments (i.e., descriptive statistics and regression assessments). Replication package includes: [1] do file Do File_READ FIRST.do [3] dta files Descriptive Statistics Data File.dta (Table 4, Table 5; S1 Table; S3 Table) Regression Data File.dta (Table 6; S2 Table; S4 Table; S5 Table; S6 Table; Table 7; Table 8) Data for Tableau.dta (Figure 2) [1] log file Replication Log.smcl Comment: Code is organized in manner that reflects ordering of the empirical results presented in paper. Note, we report the raw data for Table 1 in the table itself.

Statistical profiles of the transportation infrastructure and mobility patterns found in each county in the United States.

There are the data currently shown on the deprecated, Tableau-based version of the BTS County Transportation Profiles formerly available at https://www.bts.gov/ctp.

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

https://i.imgur.com/6UEqejq.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

Introduction: The depth-focal points of earthquakes can determine the likelihood of how light or strong, in terms of magnitude, an earthquake is likely to be. This may be a factor in determining where or how critical-infrastructure buildings, such as hospitals or prisons, can be better built to withstand stronger earthquakes, should they lie on or near deeper-earthquake starting focal points. Objectives: The primary objective of this project is to see if there is a correlation between earthquake depth and intensity of earthquakes in both Northern and Southern California, using a sample data of earthquakes from the last 20 years. With the same data available, our secondary objective will be to compare the number and intensities of earthquakes for Northern and Southern California. Methods: Upon obtaining the dataset of earthquakes from the Incorporated Research Institutions for Seismology (IRIS), we will query earthquakes greater than 4.0, and within the last 20 years. IRIS provides a map visual of the area of interest for data to be downloaded. In this case, we will zoom into the western United States. OpenRefine (v. 3.5) will be used to filter only earthquakes in Northern California and Southern California, by applying a text facet to the data. Descriptive analysis will be displayed using Tableau to see if there are any correlations, as well as to make comparisons between Northern and Southern California. Results: As a result of plotting the data, we can see that there is a statistically slight positive relationship between earthquake depth and magnitude. The majority of the smaller earthquakes seem to suggest that they have a more-shallow focal point as compared to the larger earthquakes, which have a deeper focal point. Conclusions: Critical infrastructure, as well as general housing, should be avoided in areas where deeper earthquakes are likely to occur, as these most likely will result in stronger, more-intense earthquakes.

Analysis of ‘🤰 Pregnancy, Birth & Abortion Rates (1973 - 2016)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pregnancy-birth-abortion-rates-in-the-united-stae on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Source: OSF | Downloaded on 29 October 2020

This data source is a subset of the original data source. The data has been split by State, Metric and Age Range. It has been limited to pregnancy rate, birth rate and abortion rate per 1,000 women. The original data contains many more measures.

The data was prepared with Tableau Prep.

Summary via OSF -

A data set of comprehensive historical statistics on the incidence of pregnancy, birth and abortion for people of all reproductive ages in the United States. National statistics cover the period from 1973 to 2016, the most recent year for which comparable data are available; state-level statistics are for selected years from 1988 to 2016. For a report describing key highlights from these data, as well as a methodology appendix describing our methods of estimation and data sources used, see https://guttmacher.org/report/pregnancies-births-abortions-in-united-states-1973-2016.

​

This dataset was created by Andy Kriebel and contains around 20000 samples along with Age Range, Events Per 1,000 Women, technical information and other features such as: - State - Year - and more.

How to use this dataset

• Analyze Metric in relation to Age Range
• Study the influence of Events Per 1,000 Women on State
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Health and Wellbeing of 15-year-olds in England - results from What About Youth Survey. Data has been collected on general health, diet, use of free time, physical activity, smoking, drinking, emotional wellbeing, drugs and bullying. What About YOUth? 2014 (WAY 2014) is a newly-established survey designed to collect robust local authority (LA) level data on a range of health behaviours amongst 15 year-olds.WAY 2014 is the first survey to be conducted of its kind and it is hoped that the survey will be repeated in order to form a time series of comparable data on a range of indicators for 15 year-olds across England. Questionnaire packs were sent to 295,245 young people in England and 120,115 of these responded with usable data, giving an unadjusted response rate of 40 per cent (based on the issued sample) and an adjusted response rate of 41 per cent.Participants for WAY 2014 were sampled from the Department for Education’s National Pupil Database (NPD). The NPD is a near full population database (with the exception that independent schools are not included). See this data visualised in this Tableau report. More Information from The Health and Social Care Information Centre (HSCIC) website and data downloads available from PHE Fingertips.

Snapshot img

The visual analytics market has the potential to grow by USD 4.39 billion during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 11.32%.

This visual analytics market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by end-user (BFSI, CPG and retail, healthcare, manufacturing, and others) and geography (North America, APAC, Europe, MEA, and South America). The visual analytics market report also offers information on several market vendors, including Altair Engineering Inc., Alteryx Inc., Arcadia Data Inc., Datameer Inc., International Business Machines Corp., Microsoft Corp., QlikTech international AB, SAP SE, SAS Institute Inc., and Tableau Software LLC among others.

What will the Visual Analytics Market Size be in 2021?

Browse TOC and LoE with selected illustrations and example pages of Visual Analytics Market

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Visual Analytics Market: Key Drivers and Trends

The growing availability and complexity of data are notably driving the visual analytics market growth, although factors such as data privacy and security concerns may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the visual analytics industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

      The growing availability and complexity of data will fuel the growth of the visual analytics market size.
      The availability of a large volume of data and rapidly growing data complexity in organizations are the major drivers for the development of various intelligence-based data analysis techniques.
      Intelligent techniques involving technologies such as ML and AI can help companies retrieve the huge amount of complex data in a useful manner and use that data to enhance their services and business processes. This, in turn, is expected to drive the growth of the market for visual analytics.




      The increased dependency on Internet for critical operations will drive the visual analytics market growth during the forecast period.
      E-commerce vendors are posting advertisements on search engines and other websites to attract several customers. This will increase the demand for visual analytics to help e-commerce vendors track customers, analyze customer behavior, and ensure proper decision-making.
      With the rising popularity and use of e-commerce, the number of digital media advertisements by e-commerce vendors is expected to increase, which will drive the growth of the market during the forecast period.

This visual analytics market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Visual Analytics Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Altair Engineering Inc.
Alteryx Inc.
Arcadia Data Inc.
Datameer Inc.
International Business Machines Corp.
Microsoft Corp.
QlikTech international AB
SAP SE
SAS Institute Inc.
Tableau Software LLC

This statistical study of the visual analytics market encompasses successful business strategies deployed by the key vendors. The visual analytics market is fragmented and the vendors are deploying growth strategies such as providing customized solutions to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The visual analytics market forecast report offers in-depth insights into key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Which are the Key Regions for Visual Analytics Market?

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35% of the market’s growth will originate from North America during the forecast period. The US is a key market for visual analytics in North America. Market growth in this region will be faster than the growth of the market in Europe, MEA, and South America.

This market research report entails detailed information on the competitive intelligence, marketing gaps, and regional opportunities in store for vendors, which will assist in creating efficient business plans.

What are the Revenue-generating End-user Segments

This dataset is associated with the study of the robust drivers of urban land surface temperature (ULST) dynamics across diverse landscape characters based on an augmented systematic literature review of 101 peer-reviewed articles. Some generic landscape drivers such as climate, parent material, topography, presence/proximity to urban thermal sinks were analysed as well as their impact on ULST. The study offers a novel perspective in understanding ULST dynamics, promoting interdisciplinary, participatory and democratic research approaches necessary for citizen and open science applications. The dataset includes: 1) Atlas.ti Files - A collection of 1,508 codes generated using Atlas.ti, relevant extracts in .csv and .xlsx formats, and coded articles; 2) Python Scripts - for deduplication of literature data, and dummy regression analysis of the relationship between ULST and landscape character elements; 3) Tableau Resources - Dataset used to develop the interactive dashboards developed to summarize and communicate the findings of our research; and 4) Supplementary Files - Comprising of a composite of all initial articles considered in the study, the deduplicated article list, results of the final screened/reviewed articles and other data that were utilized in the study, e.g. coded data from Atlas.ti, data used in the dummy regression analysis, amongst others.

Analysis of ‘💣 Historic Battles by Casualties’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/list-of-battles-by-casualtiese on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

List of battles by casualties and geolocation.
Map https://public.tableau.com/profile/tomek7068#!/vizhome/BattlebyCasualties/Sheet1

The following is a list of the casualties count in battles in world history. The list includes both sieges (not technically battles but usually yielding similar combat-related deaths) and civilian casualties during the battles.

Source https://en.wikipedia.org/wiki/List_of_battles_by_casualties

This dataset was created by Tomek and contains around 0 samples along with Lon, Siege, technical information and other features such as: - Year - Conflict - and more.

How to use this dataset

• Analyze Lat in relation to Casualties
• Study the influence of Lon on Siege
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Tomek

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Description of the REX3 database

This repository provides the Resolved EXIOBASE database version 3 (REX3) of the study "Biodiversity impacts of recent land-use change driven by increases in agri-food imports” published in Nature Sustainability. Also the REX3 database was used in Chapter 3 of the Global Resource Outlook 2024 from the UNEP International Resource Panel (IRP), including a data visualizer that allows for downscaling.

In REX3, Exiobase version 3.8 was merged with Eora26, production data from FAOSTAT, and bilateral trade data from the BACI database to create a highly-resolved MRIO database with comprehensive regionalized environmental impact assessment following the UNEP-SETAC guidelines and integrating land use data from the LUH2 database. REX3 distinguishes 189 countries, 163 sectors, time series from 1995 to 2022, and several environmental and socioeconomic extensions. The environmental impact assessment includes climate impacts, PM health impacts, water stress, and biodiversity impact from land occupation, land use change, and eutrophication.

The folders "REX3_Year" provide the database for each year. Each folder contains the following files (*.mat-files):
T_REX: the transaction matrix
Y_REX: the final demand matrix
Q_REX and Q_Y_REX: the satellite matrix of the economy and the final demand

The folder "REX3_Labels" provides the labels of the matrices, countries, sectors and extensions.

*The database is also available as textfiles --> contact livia.cabernard@tum.de

While Exiobase version 3.8.2 was used for the study "Biodiversity impacts of recent land-use change driven by increases in agri-food imports” and the Global Resource Outlook 2024, the REX3 database shared in this repository is based on Exiobase version 3.8, as this is the earliest exiobase version that can be still shared via a Creative Commons Attribution 4.0 International License. However, the matlab code attached to this repository allows to compile the REX3 database with earlier exiobase versions as well (e.g., version 3.8.2), as described in the section below.

Codes to compile REX3 and reproduce the results of the study “Biodiversity impacts of recent land-use change driven by increases in agri-food imports”

The folder "matlab code to compile REX3" provides the code to compile the REX3 database. This can also be done by using an earlier exiobase version (e.g., version 3.8.2). For this purpose, the data from EXIOBASE3 need to be saved into the subfolder Files/Exiobase/…, while the data from Eora26 need to be saved into the subfolder Files/Eora26/bp/…

The folder "R code for regionalized BD impact assessment based on LUH2 data and maps (Figure 1)" contains the R code to weight the land use data from the LUH2 dataset with the species loss factors from UNEP-SETAC and to create the maps shown in Figure 1 of the paper. For this purpose, the data from the LUH2 dataset (transitions.nc) need to be stored in the subfolder "LUH2 data".

The folder "matlab code to calculate MRIO results (Figure 2-5)" contains the matlab code to calculate the MRIO Results for Figure 2-5 of the study.

The folder "R code to illustrate sankeys – Figure 3–5, S10" contains the R code to visualize the sankeys.

Data visualizer to downscale the results of the IRP Global Resource Outlook 2024 based on REX3:

A data visualizer that is based on REX3 and allows to downscale the results of the IRP Global Resource Outlook 2024 on a country level can be found here.

Earlier versions of REX:

An earlier version of this database (REX1) with time series from 1995–2015 is described in Cabernard & Pfister 2021.

An earlier version including GTAP and mining-related biodiversity impacts for the year 2014 (REX2) is described in Cabernard & Pfister 2022.

Download & conversion from .mat to .zarr files for efficient data handling:
A package for downloading, extracting, and converting REX3 data from MATLAB (.mat) to .zarr format has been provided by Yanfei Shan here: https://github.com/FayeShan/REX3_handler. Once the files are converted to .zarr format, the data can be explored and processed flexibly. For example, you can use pandas to convert the data into CSV, or export it as Parquet, which is more efficient for handling large datasets. Please note note that this package is still under development and that more functions for MRIO analysis will be added in the future.

About our monitoring index solution: We have developed a cost-effective methodology to create indices of economic activity in any open facility across the US.

Our solution uses satellite imagery to monitor surface changes over time. Our advanced Machine Learning models detect and report relevant changes in the area of interest.

Our methodology can help you analyze a significant number of locations without requiring manual supervision. It employs clustering image models and can detect changes within the same cluster over time. This feature lets you monitor as many locations as you need to make informed and strategic decisions.

Main use cases: - Monitor business and economic activity of any target company worldwide. - Understand and predict seasonal patterns from your target companies. - Make better decisions faster and more precisely. - Identify inventory threats and illicit or abnormal behaviors in target locations. - Get alerts of significant changes in the monitored facilities for a fraction of the cost compared to other solutions. - Detect changes on structures and buildings (For example, you can easily detect if a manufacturing plant is expanding its infraestructure).

Industries that are using our indices: - Investment funds - Construction & Real Estate - Transport & and Logistics - Agriculture

Considerations: - This solution measures relative changes as a percentage index. For example, it can measure parking occupancy percentage but will not indicate the number of parked cars (Learn more: https://docs.predikdata.com/Parking_Occupancy_Sample.pdf) - Objects must be at least 3x3m to be visible and measured.

Deliverables: Our index reports adapt to your specific requirements. We can provide a live database, Tableau reporting, or alert notifications.

Analysis of ‘Canada National & Provincial Per Capita Income’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/charlesluan/canada-national-provincial-capita-income-762019 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Filtered data which only remains 10 provinces and national capita income, median income, with 16 years old & over, both sexes, in 2019 constant dollar

• The raw data had been already adjusted by 2019 constant dollar, from 1976-2019

• Please be aware territories of Canada were not listed in the original dataset

• For example, the 2018 Canada national average income is not equal to the average of 10 provinces income, since territories are not in the list.

• My practicing data exploration of this dataset: Facts of Individuals Income in Canada, 1976 - 2019

Acknowledgements

Data source:
Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas **Raw data version: ** Table: 11-10-0239-01 (formerly CANSIM 206-0052) **Release date: ** 2021-03-23

Inspiration

I was really surprised when revealing these rows, it seems like there isn't much growth since 1976 Canada average income is 40,800 dollars while 2019 is 49,000 dollars. (Please be noticed these are adjusted by 2019 constant dollar)

Please correct me if I was wrong. Thank you

--- Original source retains full ownership of the source dataset ---

Monthly indexes for major components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the current month and previous four months. The base year for the index is 2002=100.

Ce tableau fait partie d'une série de tableaux qui présente un portrait du Canada selon les divers thèmes du recensement. Ces tableaux varient selon la complexité et les niveaux géographiques. Le contenu varie d'un simple aperçu du pays à des tableaux croisés plus complexes; les tableaux peuvent également présenter des données provenant de plusieurs recensements. This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.