WHO report on ambient air quality: Download air quality no2.csv data

, , , and elements. - How to download a CSV file: Where to find free datasets for data science projects, and how to download them as CSV files. - How to import a CSV file into an HTML table: How to use Excel or Google Sheets to open and convert a CSV file into an HTML table, and how to copy and paste it into your HTML file. - How to style and analyze your table data: How to use CSS to make your table more readable and attractive, and how to use basic formulas and functions in Excel or Google Sheets to perform some simple data analysis on your table data. - Conclusion: A summary of what you learned in this article, and some resources for further learning. - FAQs: Some frequently asked questions about HTML tables and CSV files, with answers and links. Article with HTML formatting: How to Create Tables in HTML and Download CSV Files

air quality no2.csv download

What are HTML tables and CSV files?

An HTML table is a structured set of data made up of rows and columns. You can use HTML tables to present information such as statistics, schedules, prices, comparisons, etc. on your web pages. HTML tables are created using the <table> element, along with other elements such as <tr> (table row), <th> (table header), and <td> (table data).

A CSV file is a text file that contains data separated by commas (or sometimes other characters). CSV stands for Comma Separated Values. A CSV file can store tabular data in a simple and compact way. For example, a CSV file might look like this:

Name,Age,Country Alice,25,USA Bob,32,UK Charlie,28,Australia

You can open a CSV file in various programs such as Excel or Google Sheets, or import it into an HTML table. This way, you can easily manipulate and analyze the data in the CSV file.

How to create a basic HTML table

To create a basic HTML table, you need to use the following elements:

• <table>: This element defines the table. It contains all the other elements related to the table.

• <tr>: This element defines a table row. It contains one or more <th> or <td> elements.

• <th>: This element defines a table header cell. It is usually used to display the title or label of a column or row.

• <td>: This element defines a table data cell. It is used to display the content of a column or row.

Here is an example of a simple HTML table that shows some information about planets:

<table> <tr> <th>Name</th> <th>Type</th> <th>Moons</th> </tr> <tr> <td>Mercury</td> <td>Terrestrial</ td>Terrestrial</td> <td>0</td> </tr> <tr> <td>Venus</td> <td>Terrestrial</td> <td>0</td> </tr> <tr> <td>Earth</td> <td>Terrestrial</td> <td>1</td> </tr> <tr> <td>Mars</td> <td>Terrestrial</td> <td>2</td> </tr> <tr> <td>Jupiter</td> <td>Gas giant</td> <td>79</td> </tr> <tr> <td>Saturn</td> <td>Gas giant</td> <td>82</td> </tr> <tr> <td>Uranus</td> <td>Ice giant</td> <td>27</td> </tr> <tr> <td>Neptune</td> <td>Ice giant</td> <td>14</td> </tr> </table>

If you save this code as an HTML file and open it in a browser, you will see something like this:

As you can see, the table has a header row with three columns: Name, Type, and Moons. The rest of the rows contain the data for each planet. The data is aligned to the left by default.

How to download a CSV file

Now that you know how to create a basic HTML table, you might want to populate it with some more interesting and relevant data. One way to do that is to download a CSV file from the internet and import it into your table. There are many sources of free datasets for data science projects, such as Kaggle, UCI Machine Learning Repository, Google Dataset Search, etc. You can browse these websites and find a dataset that suits your interest and needs.

For this example, I will use a dataset from Kaggle that contains the air quality index (AQI) for various cities in India. The AQI is a measure of how polluted the air is, based on several parameters such as PM2.5, PM10, NO2, SO2, CO, O3, etc. The dataset has monthly data from January 2015 to April 2020. You can download the dataset from this link: .

air quality no2.csv data source

air quality no2.csv file format

air quality no2.csv pandas tutorial

air quality no2.csv EPA pre-generated files

air quality no2.csv WHO database

air quality no2.csv analysis python

air quality no2.csv visualization R

air quality no2.csv station locations

air quality no2.csv time series

air quality no2.csv missing values

air quality no2.csv correlation with AQI

air quality no2.csv comparison by city

air quality no2.csv health effects

air quality no2.csv emission sources

air quality no2.csv reduction strategies

air quality no2.csv monitoring methods

air quality no2.csv standards and guidelines

air quality no2.csv historical trends

air quality no2.csv seasonal variations

air quality no2.csv impact of COVID-19

air quality no2.csv machine learning models

air quality no2.csv forecasting methods

air quality no2.csv spatial interpolation

air quality no2.csv satellite data

air quality no2.csv sensor networks

air quality no2.csv open access datasets

air quality no2.csv metadata description

air quality no2.csv data cleaning steps

air quality no2.csv data transformation techniques

air quality no2.csv data exploration tools

air quality no2.csv descriptive statistics

air quality no2.csv inferential statistics

air quality no2.csv hypothesis testing

air quality no2.csv regression analysis

air quality no2.csv classification analysis

air quality no2.csv clustering analysis

air quality no2.csv anomaly detection

air quality no2.csv feature engineering

air quality no2.csv feature selection

air quality no2.csv model evaluation metrics

air quality no2.csv model validation methods

air quality no2.csv model optimization algorithms

air quality no2.csv model deployment options

air quality no2.csv dashboard design principles

air quality no2.csv interactive plots and maps

air quality no2.csv storytelling with data

air quality no2.csv report writing tips

air quality no2.csv presentation skills

air quality no2.csv online courses and resources

After you download the dataset, you will get a zip file that contains several CSV files. Each CSV file corresponds to a city in India. For this example, I will use the file named city_hour.csv, which has hourly data for all the cities. You can unzip the file and open it in Excel or Google Sheets.

How to import a CSV file into an HTML table

Once you have opened the CSV file in Excel or Google Sheets, you will see something like this:

The CSV file has many columns and rows of data. For this example, I will only use a subset of the data to make it easier to display and analyze. I will select the columns City, Datetime, AQI, PM2.5, PM10, NO2, and CO. I will also filter the data by City and Datetime, so that I only get the data for Delhi in January 2020. You can use the filter function in Excel or Google Sheets to do this. After applying the filter, I will get something like this:

Now, I want to convert this data into an HTML table, so that I can display it on my web page. To do this, I will use the following steps:

• Select the data that I want to include in the table, and copy it.

• Go to a website that can convert CSV data into HTML table, such as .

• Paste the data into the input box, and click on the Convert button.

• Copy the output HTML code from the output box.

• Paste the HTML code into my HTML file, inside the <body> element.

Here is the HTML code that I get from the website:

<table border="1" cellpadding="1" cellspacing="1"> <tr><td>City</td><td>Datetime</td><td>AQI</td><td>PM2.5</td><td>PM10</td><td>NO2</td><td>CO</td></tr> <tr><td>Delhi</td><td>2020-01-01 00:00:00</td><td>472</td><td>269.0</td><td>448.0</td><td>43.0</td><td>1.7</td></tr> <tr><td>Delhi</td><td>2020-01-01 01:00:00</td><td>454</td><td>264.0</td><td>-999.0</td><td>-999.0</td>< td>-999.0</td><td>1.7</td></tr> <tr><td>Delhi</td><td>2020-01-01 02:00:00</td><td>435</td><td>253.0</td><td>-999.0</td><td>-999.0</td><td>1.7</td