![]() īegin by setting the working directory to your earth-analytics directory using the os package and the HOME attribute of the earthpy package.Īs you learned in the chapter on working with paths and directories, this will provide you with the flexibility to specify files to import from various subdirectories that you might have within the earth-analytics directory. csv) using the loadtxt() function from numpy (which you imported with the alias np). You can easily create new numpy arrays by importing numeric data from text files (.txt and. Import Numeric Data from Text Files Into Numpy Arrays When you click on Run to execute it, it will open the text file that you just created, read the one-line message from it, and print the message to the Command Output pane. Type the following program into your text editor and save it as file-input.py. On the other hand, monthly-precip-2002-2013.csv contains rows and columns of data, with each year of data on a separate line and each month of data within a specific year separated by commas. Python also has methods which allow you to get information from files. While avg-monthly-precip.txt contains numeric values and months.txt contains text string values, both files are plain text files with a separate line for each month’s value. home/your-username/earth-analytics/data/earthpy-downloads/ Recall that these files have been downloaded to: Now that you have downloaded these files, you can take a look at them by opening the files from your file explorer. '/root/earth-analytics/data/earthpy-downloads/monthly-precip-2002-2013.csv' ![]()
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