16. CSV and JSON
In Chapter 15, you learned how to extract text from PDF and Word documents. These files were in a binary format, which required special Python modules to access their data. CSV and JSON files, on the other hand, are just plaintext files. You can view them in a text editor, such as Mu. But Python also comes with the special csv and json modules, each providing functions to help you work with these file formats.
CSV stands for “comma-separated values,” and CSV files are simplified spreadsheets stored as plaintext files. Python’s csv module makes it easy to parse CSV files.
JSON (pronounced “JAY-sawn” or “Jason”—it doesn’t matter how because either way people will say you’re pronouncing it wrong) is a format that stores information as JavaScript source code in plaintext files. (JSON is short for JavaScript Object Notation.) You don’t need to know the JavaScript programming language to use JSON files, but the JSON format is useful to know because it’s used in many web applications.
The csv Module
Each line in a CSV file represents a row in the spreadsheet, and commas separate the cells in the row. For example, the spreadsheet example.xlsx from https://nostarch.com/automatestuff2/ would look like this in a CSV file:
I will use this file for this chapter’s interactive shell examples. You can download example.csv from https://nostarch.com/automatestuff2/ or enter the text into a text editor and save it as example.csv.
CSV files are simple, lacking many of the features of an Excel spreadsheet. For example, CSV files:
Don’t have types for their values—everything is a string
Don’t have settings for font size or color
Don’t have multiple worksheets
Can’t specify cell widths and heights
Can’t have merged cells
Can’t have images or charts embedded in them
The advantage of CSV files is simplicity. CSV files are widely supported by many types of programs, can be viewed in text editors (including Mu), and are a straightforward way to represent spreadsheet data. The CSV format is exactly as advertised: it’s just a text file of comma-separated values.
Since CSV files are just text files, you might be tempted to read them in as a string and then process that string using the techniques you learned in Chapter 9. For example, since each cell in a CSV file is separated by a comma, maybe you could just call split(',') on each line of text to get the comma-separated values as a list of strings. But not every comma in a CSV file represents the boundary between two cells. CSV files also have their own set of escape characters to allow commas and other characters to be included as part of the values. The split() method doesn’t handle these escape characters. Because of these potential pitfalls, you should always use the csv module for reading and writing CSV files.
reader Objects
To read data from a CSV file with the csv module, you need to create a reader object. A reader object lets you iterate over lines in the CSV file. Enter the following into the interactive shell, with example.csv in the current working directory:
The csv module comes with Python, so we can import it ➊ without having to install it first.
To read a CSV file with the csv module, first open it using the open() function ➋, just as you would any other text file. But instead of calling the read() or readlines() method on the File object that open() returns, pass it to the csv.reader() function ➌. This will return a reader object for you to use. Note that you don’t pass a filename string directly to the csv.reader() function.
The most direct way to access the values in the reader object is to convert it to a plain Python list by passing it to list() ➍. Using list() on this reader object returns a list of lists, which you can store in a variable like exampleData. Entering exampleData in the shell displays the list of lists ➎.
Now that you have the CSV file as a list of lists, you can access the value at a particular row and column with the expression exampleData[row][col], where row is the index of one of the lists in exampleData, and col is the index of the item you want from that list. Enter the following into the interactive shell:
As you can see from the output, exampleData[0][0] goes into the first list and gives us the first string, exampleData[0][2] goes into the first list and gives us the third string, and so on.
Reading Data from reader Objects in a for Loop
For large CSV files, you’ll want to use the reader object in a for loop. This avoids loading the entire file into memory at once. For example, enter the following into the interactive shell:
After you import the csv module and make a reader object from the CSV file, you can loop through the rows in the reader object. Each row is a list of values, with each value representing a cell.
The print() function call prints the number of the current row and the contents of the row. To get the row number, use the reader object’s line_num variable, which contains the number of the current line.
The reader object can be looped over only once. To reread the CSV file, you must call csv.reader to create a reader object.
writer Objects
A writer object lets you write data to a CSV file. To create a writer object, you use the csv.writer() function. Enter the following into the interactive shell:
First, call open() and pass it 'w' to open a file in write mode ➊. This will create the object you can then pass to csv.writer() ➋ to create a writer object.
On Windows, you’ll also need to pass a blank string for the open() function’s newline keyword argument. For technical reasons beyond the scope of this book, if you forget to set the newline argument, the rows in output.csv will be double-spaced, as shown in Figure 16-1.
The writerow() method for writer objects takes a list argument. Each value in the list is placed in its own cell in the output CSV file. The return value of writerow() is the number of characters written to the file for that row (including newline characters).
This code produces an output.csv file that looks like this:
Notice how the writer object automatically escapes the comma in the value 'Hello, world!' with double quotes in the CSV file. The csv module saves you from having to handle these special cases yourself.
The delimiter and lineterminator Keyword Arguments
Say you want to separate cells with a tab character instead of a comma and you want the rows to be double-spaced. You could enter something like the following into the interactive shell:
This changes the delimiter and line terminator characters in your file. The delimiter is the character that appears between cells on a row. By default, the delimiter for a CSV file is a comma. The line terminator is the character that comes at the end of a row. By default, the line terminator is a newline. You can change characters to different values by using the delimiter and lineterminator keyword arguments with csv.writer().
Passing delimiter='\t' and lineterminator='\n\n' ➊ changes the character between cells to a tab and the character between rows to two newlines. We then call writerow() three times to give us three rows.
This produces a file named example.tsv with the following contents:
Now that our cells are separated by tabs, we’re using the file extension .tsv, for tab-separated values.
DictReader and DictWriter CSV Objects
For CSV files that contain header rows, it’s often more convenient to work with the DictReader and DictWriter objects, rather than the reader and writer objects.
The reader and writer objects read and write to CSV file rows by using lists. The DictReader and DictWriter CSV objects perform the same functions but use dictionaries instead, and they use the first row of the CSV file as the keys of these dictionaries.
Go to https://nostarch.com/automatestuff2/ and download the exampleWithHeader.csv file. This file is the same as example.csv except it has Timestamp, Fruit, and Quantity as the column headers in the first row.
To read the file, enter the following into the interactive shell:
Inside the loop, DictReader object sets row to a dictionary object with keys derived from the headers in the first row. (Well, technically, it sets row to an OrderedDict object, which you can use in the same way as a dictionary; the difference between them is beyond the scope of this book.) Using a DictReader object means you don’t need additional code to skip the first row’s header information, since the DictReader object does this for you.
If you tried to use DictReader objects with example.csv, which doesn’t have column headers in the first row, the DictReader object would use '4/5/2015 13:34', 'Apples', and '73' as the dictionary keys. To avoid this, you can supply the DictReader() function with a second argument containing made-up header names:
Because example.csv’s first row doesn’t have any text for the heading of each column, we created our own: 'time', 'name', and 'amount'.
DictWriter objects use dictionaries to create CSV files.
If you want your file to contain a header row, write that row by calling writeheader(). Otherwise, skip calling writeheader() to omit a header row from the file. You then write each row of the CSV file with a writerow() method call, passing a dictionary that uses the headers as keys and contains the data to write to the file.
The output.csv file this code creates looks like this:
Notice that the order of the key-value pairs in the dictionaries you passed to writerow() doesn’t matter: they’re written in the order of the keys given to DictWriter(). For example, even though you passed the Phone key and value before the Name and Pet keys and values in the fourth row, the phone number still appeared last in the output.
Notice also that any missing keys, such as 'Pet' in {'Name': 'Bob', 'Phone': '555-9999'}, will simply be empty in the CSV file.
Project: Removing the Header from CSV Files
Say you have the boring job of removing the first line from several hundred CSV files. Maybe you’ll be feeding them into an automated process that requires just the data and not the headers at the top of the columns. You could open each file in Excel, delete the first row, and resave the file—but that would take hours. Let’s write a program to do it instead.
The program will need to open every file with the .csv extension in the current working directory, read in the contents of the CSV file, and rewrite the contents without the first row to a file of the same name. This will replace the old contents of the CSV file with the new, headless contents.
WARNING
As always, whenever you write a program that modifies files, be sure to back up the files first, just in case your program does not work the way you expect it to. You don’t want to accidentally erase your original files.
At a high level, the program must do the following:
Find all the CSV files in the current working directory.
Read in the full contents of each file.
Write out the contents, skipping the first line, to a new CSV file.
At the code level, this means the program will need to do the following:
Loop over a list of files from os.listdir(), skipping the non-CSV files.
Create a CSV reader object and read in the contents of the file, using the line_num attribute to figure out which line to skip.
Create a CSV writer object and write out the read-in data to the new file.
For this project, open a new file editor window and save it as removeCsvHeader.py.
Step 1: Loop Through Each CSV File
The first thing your program needs to do is loop over a list of all CSV filenames for the current working directory. Make your removeCsvHeader.py look like this:
The os.makedirs() call will create a headerRemoved folder where all the headless CSV files will be written. A for loop on os.listdir('.') gets you partway there, but it will loop over all files in the working directory, so you’ll need to add some code at the start of the loop that skips filenames that don’t end with .csv. The continue statement ➊ makes the for loop move on to the next filename when it comes across a non-CSV file.
Just so there’s some output as the program runs, print out a message saying which CSV file the program is working on. Then, add some TODO comments for what the rest of the program should do.
Step 2: Read in the CSV File
The program doesn’t remove the first line from the CSV file. Rather, it creates a new copy of the CSV file without the first line. Since the copy’s filename is the same as the original filename, the copy will overwrite the original.
The program will need a way to track whether it is currently looping on the first row. Add the following to removeCsvHeader.py.
The reader object’s line_num attribute can be used to determine which line in the CSV file it is currently reading. Another for loop will loop over the rows returned from the CSV reader object, and all rows but the first will be appended to csvRows.
As the for loop iterates over each row, the code checks whether readerObj.line_num is set to 1. If so, it executes a continue to move on to the next row without appending it to csvRows. For every row afterward, the condition will be always be False, and the row will be appended to csvRows.
Step 3: Write Out the CSV File Without the First Row
Now that csvRows contains all rows but the first row, the list needs to be written out to a CSV file in the headerRemoved folder. Add the following to removeCsvHeader.py:
The CSV writer object will write the list to a CSV file in headerRemoved using csvFilename (which we also used in the CSV reader). This will overwrite the original file.
Once we create the writer object, we loop over the sublists stored in csvRows and write each sublist to the file.
After the code is executed, the outer for loop ➊ will loop to the next filename from os.listdir('.'). When that loop is finished, the program will be complete.
To test your program, download removeCsvHeader.zip from https://nostarch.com/automatestuff2/ and unzip it to a folder. Run the removeCsvHeader.py program in that folder. The output will look like this:
This program should print a filename each time it strips the first line from a CSV file.
Ideas for Similar Programs
The programs that you could write for CSV files are similar to the kinds you could write for Excel files, since they’re both spreadsheet files. You could write programs to do the following:
Compare data between different rows in a CSV file or between multiple CSV files.
Copy specific data from a CSV file to an Excel file, or vice versa.
Check for invalid data or formatting mistakes in CSV files and alert the user to these errors.
Read data from a CSV file as input for your Python programs.
JSON and APIs
JavaScript Object Notation is a popular way to format data as a single human-readable string. JSON is the native way that JavaScript programs write their data structures and usually resembles what Python’s pprint() function would produce. You don’t need to know JavaScript in order to work with JSON-formatted data.
Here’s an example of data formatted as JSON:
JSON is useful to know, because many websites offer JSON content as a way for programs to interact with the website. This is known as providing an application programming interface (API). Accessing an API is the same as accessing any other web page via a URL. The difference is that the data returned by an API is formatted (with JSON, for example) for machines; APIs aren’t easy for people to read.
Many websites make their data available in JSON format. Facebook, Twitter, Yahoo, Google, Tumblr, Wikipedia, Flickr, Data.gov, Reddit, IMDb, Rotten Tomatoes, LinkedIn, and many other popular sites offer APIs for programs to use. Some of these sites require registration, which is almost always free. You’ll have to find documentation for what URLs your program needs to request in order to get the data you want, as well as the general format of the JSON data structures that are returned. This documentation should be provided by whatever site is offering the API; if they have a “Developers” page, look for the documentation there.
Using APIs, you could write programs that do the following:
Scrape raw data from websites. (Accessing APIs is often more convenient than downloading web pages and parsing HTML with Beautiful Soup.)
Automatically download new posts from one of your social network accounts and post them to another account. For example, you could take your Tumblr posts and post them to Facebook.
Create a “movie encyclopedia” for your personal movie collection by pulling data from IMDb, Rotten Tomatoes, and Wikipedia and putting it into a single text file on your computer.
You can see some examples of JSON APIs in the resources at https://nostarch.com/automatestuff2/.
JSON isn’t the only way to format data into a human-readable string. There are many others, including XML (eXtensible Markup Language), TOML (Tom’s Obvious, Minimal Language), YML (Yet another Markup Language), INI (Initialization), or even the outdated ASN.1 (Abstract Syntax Notation One) formats, all of which provide a structure for representing data as human-readable text. This book won’t cover these, because JSON has quickly become the most widely used alternate format, but there are third-party Python modules that readily handle them.
The json Module
Python’s json module handles all the details of translating between a string with JSON data and Python values for the json.loads() and json.dumps() functions. JSON can’t store every kind of Python value. It can contain values of only the following data types: strings, integers, floats, Booleans, lists, dictionaries, and NoneType. JSON cannot represent Python-specific objects, such as File objects, CSV reader or writer objects, Regex objects, or Selenium WebElement objects.
Reading JSON with the loads() Function
To translate a string containing JSON data into a Python value, pass it to the json.loads() function. (The name means “load string,” not “loads.”) Enter the following into the interactive shell:
After you import the json module, you can call loads() and pass it a string of JSON data. Note that JSON strings always use double quotes. It will return that data as a Python dictionary. Python dictionaries are not ordered, so the key-value pairs may appear in a different order when you print jsonDataAsPythonValue.
Writing JSON with the dumps() Function
The json.dumps() function (which means “dump string,” not “dumps”) will translate a Python value into a string of JSON-formatted data. Enter the following into the interactive shell:
The value can only be one of the following basic Python data types: dictionary, list, integer, float, string, Boolean, or None.
Project: Fetching Current Weather Data
Checking the weather seems fairly trivial: Open your web browser, click the address bar, type the URL to a weather website (or search for one and then click the link), wait for the page to load, look past all the ads, and so on.
Actually, there are a lot of boring steps you could skip if you had a program that downloaded the weather forecast for the next few days and printed it as plaintext. This program uses the requests module from Chapter 12 to download data from the web.
Overall, the program does the following:
Reads the requested location from the command line
Downloads JSON weather data from OpenWeatherMap.org
Converts the string of JSON data to a Python data structure
Prints the weather for today and the next two days
So the code will need to do the following:
Join strings in sys.argv to get the location.
Call requests.get() to download the weather data.
Call json.loads() to convert the JSON data to a Python data structure.
Print the weather forecast.
For this project, open a new file editor window and save it as getOpenWeather.py. Then visit https://openweathermap.org/api/ in your browser and sign up for a free account to obtain an API key, also called an app ID, which for the OpenWeatherMap service is a string code that looks something like '30144aba38018987d84710d0e319281e'. You don’t need to pay for this service unless you plan on making more than 60 API calls per minute. Keep the API key secret; anyone who knows it can write scripts that use your account’s usage quota.
Step 1: Get Location from the Command Line Argument
The input for this program will come from the command line. Make getOpenWeather.py look like this:
In Python, command line arguments are stored in the sys.argv list. The APPID variable should be set to the API key for your account. Without this key, your requests to the weather service will fail. After the #! shebang line and import statements, the program will check that there is more than one command line argument. (Recall that sys.argv will always have at least one element, sys.argv[0], which contains the Python script’s filename.) If there is only one element in the list, then the user didn’t provide a location on the command line, and a “usage” message will be provided to the user before the program ends.
The OpenWeatherMap service requires that the query be formatted as the city name, a comma, and a two-letter country code (like “US” for the United States). You can find a list of these codes at https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2. Our script displays the weather for the first city listed in the retrieved JSON text. Unfortunately, cities that share a name, like Portland, Oregon, and Portland, Maine, will both be included, though the JSON text will include longitude and latitude information to differentiate between the cities.
Command line arguments are split on spaces. The command line argument San Francisco, US would make sys.argv hold ['getOpenWeather.py', 'San', 'Francisco,', 'US']. Therefore, call the join() method to join all the strings except for the first in sys.argv. Store this joined string in a variable named location.
Step 2: Download the JSON Data
OpenWeatherMap.org provides real-time weather information in JSON format. First you must sign up for a free API key on the site. (This key is used to limit how frequently you make requests on their server, to keep their bandwidth costs down.) Your program simply has to download the page at https://api.openweathermap.org/data/2.5/forecast/daily?q=<Location>&cnt=3&APPID=<APIkey>, where <Location> is the name of the city whose weather you want and <API key> is your personal API key. Add the following to getOpenWeather.py.
We have location from our command line arguments. To make the URL we want to access, we use the %s placeholder and insert whatever string is stored in location into that spot in the URL string. We store the result in url and pass url to requests.get(). The requests.get() call returns a Response object, which you can check for errors by calling raise_for_status(). If no exception is raised, the downloaded text will be in response.text.
Step 3: Load JSON Data and Print Weather
The response.text member variable holds a large string of JSON-formatted data. To convert this to a Python value, call the json.loads() function. The JSON data will look something like this:
You can see this data by passing weatherData to pprint.pprint(). You may want to check https://openweathermap.org/ for more documentation on what these fields mean. For example, the online documentation will tell you that the 302.29 after 'day' is the daytime temperature in Kelvin, not Celsius or Fahrenheit.
The weather descriptions you want are after 'main' and 'description'. To neatly print them out, add the following to getOpenWeather.py.
Notice how the code stores weatherData['list'] in the variable w to save you some typing ➊. You use w[0], w[1], and w[2] to retrieve the dictionaries for today, tomorrow, and the day after tomorrow’s weather, respectively. Each of these dictionaries has a 'weather' key, which contains a list value. You’re interested in the first list item, a nested dictionary with several more keys, at index 0. Here, we print the values stored in the 'main' and 'description' keys, separated by a hyphen.
When this program is run with the command line argument getOpenWeather.py San Francisco, CA, the output looks something like this:
(The weather is one of the reasons I like living in San Francisco!)
Ideas for Similar Programs
Accessing weather data can form the basis for many types of programs. You can create similar programs to do the following:
Collect weather forecasts for several campsites or hiking trails to see which one will have the best weather.
Schedule a program to regularly check the weather and send you a frost alert if you need to move your plants indoors. (Chapter 17 covers scheduling, and Chapter 18 explains how to send email.)
Pull weather data from multiple sites to show all at once, or calculate and show the average of the multiple weather predictions.
Summary
CSV and JSON are common plaintext formats for storing data. They are easy for programs to parse while still being human readable, so they are often used for simple spreadsheets or web app data. The csv and json modules greatly simplify the process of reading and writing to CSV and JSON files.
The last few chapters have taught you how to use Python to parse information from a wide variety of file formats. One common task is taking data from a variety of formats and parsing it for the particular information you need. These tasks are often specific to the point that commercial software is not optimally helpful. By writing your own scripts, you can make the computer handle large amounts of data presented in these formats.
In Chapter 18, you’ll break away from data formats and learn how to make your programs communicate with you by sending emails and text messages.
Practice Questions
1. What are some features Excel spreadsheets have that CSV spread-sheets don’t?
2. What do you pass to csv.reader() and csv.writer() to create reader and writer objects?
3. What modes do File objects for reader and writer objects need to be opened in?
4. What method takes a list argument and writes it to a CSV file?
5. What do the delimiter and lineterminator keyword arguments do?
6. What function takes a string of JSON data and returns a Python data structure?
7. What function takes a Python data structure and returns a string of JSON data?
Practice Project
For practice, write a program that does the following.
Excel-to-CSV Converter
Excel can save a spreadsheet to a CSV file with a few mouse clicks, but if you had to convert hundreds of Excel files to CSVs, it would take hours of clicking. Using the openpyxl module from Chapter 12, write a program that reads all the Excel files in the current working directory and outputs them as CSV files.
A single Excel file might contain multiple sheets; you’ll have to create one CSV file per sheet. The filenames of the CSV files should be <excel filename>_<sheet title>.csv, where <excel filename> is the filename of the Excel file without the file extension (for example, 'spam_data', not 'spam_data.xlsx') and <sheet title> is the string from the Worksheet object’s title variable.
This program will involve many nested for loops. The skeleton of the program will look something like this:
Download the ZIP file excelSpreadsheets.zip from https://nostarch.com/automatestuff2/ and unzip the spreadsheets into the same directory as your program. You can use these as the files to test the program on.
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