and death valleycalifornia later in the we'll use the json module to access population data stored in the json format and use pygal to draw population map by country by the end of this you'll be prepared to work with different types and formats of data setsand you'll have deeper understanding of how to build complex visualizations the ability to access and visualize online data of different types and formats is essential to working with wide variety of real-world data sets the csv file format one simple way to store data in text file is to write the data as series of values separated by commascalled comma-separated values the resulting files are called csv files for examplehere' one line of weather data in csv format, , , , , ,- , , , , , , ,,,, , ,, , ,, this is weather data for january in sitkaalaska it includes the day' high and low temperaturesas well as number of other measurements from that day csv files can be tricky for humans to readbut they're easy for programs to process and extract values fromwhich speeds up the data analysis process we'll begin with small set of csv-formatted weather data recorded in sitkawhich is available in the book' resources through nostarch com/pythoncrashcoursecopy the file sitka_weather_ - csv to the folder where you're writing this programs (once you download the book' resourcesyou'll have all the files you need for this project note the weather data in this project was originally downloaded from wunderground com/historyparsing the csv file headers python' csv module in the standard library parses the lines in csv file and allows us to quickly extract the values we're interested in let' start by examining the first line of the filewhich contains series of headers for the datahighs_lows py import csv filename 'sitka_weather_ - csvu with open(filenameas fv reader csv reader(fw header_row next(readerprint(header_row