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Automate the Boring Stuff with Python. Working with CSV Files and JSON Data. In Chapter 1. 3, 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 IDLE’s file editor. 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 Java. Script source code in plaintext files.(JSON is short for Java. Script Object Notation.) You don’t need to know the Java.
Script programming language to use JSON files, but the JSON format is useful to know because it’s used in many web applications. 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. CSV file: 4/5/2. 01. Apples,7. 3. 4/5/2. Cherries,8. 5. 4/6/2. Pears,1. 4. 4/8/2.
Oranges,5. 2. 4/1. Apples,1. 52. 4/1. Bananas,2. 3. 4/1. Strawberries,9. 8I will use this file for this chapter’s interactive shell examples. You can download example.
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 IDLE’s file editor), 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 8. For example, since each cell in a CSV file is separated by a comma, maybe you could just call the split() method on each line of text to get the values. 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. 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. Mystery Agency A Vampires Kiss Setup Charter more. But instead of calling the read() or readlines() method on the File object that open() returns, pass it to the csv.
This will return a Reader object for you to use. Note that you don’t pass a filename string directly to the csv.
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() . Using list() on this Reader object returns a list of lists, which you can store in a variable like example.
Data. Entering example. Data in the shell displays the list of lists . Enter the following into the interactive shell: > > > example.
Data. This avoids loading the entire file into memory at once. For example, enter the following into the interactive shell: > > > import csv. File = open('example. Reader = csv. reader(example. File). > > > for row in example. Reader: print('Row #' + str(example. Reader. line. 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. To reread the CSV file, you must call csv.
Reader object. A Writer object lets you write data to a CSV file. To create a Writer object, you use the csv. Enter the following into the interactive shell: > > > import csv. This will create the object you can then pass to csv. For technical reasons beyond the scope of this book, if you forget to set the newline argument, the rows in output. Figure 1. 4- 1. Figure 1. If you forget the newline='' keyword argument in open(), the CSV file will be double- spaced.
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. 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: > > > import csv. File = open('example.
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. Passing delimeter='\t' and lineterminator='\n\n' . We then call writerow() three times to give us three rows.
This produces a file named example. Now that our cells are separated by tabs, we’re using the file extension . 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 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.
Note. 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. CSV files. Create a CSV Reader object and read in the contents of the file, using the line.
Make your remove. Csv. Header. py look like this: #! A for loop on os. The continue statement . 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 remove.
Csv. Header. py.#! Another for loop will loop over the rows returned from the CSV Reader object, and all rows but the first will be appended to csv. Rows. As the for loop iterates over each row, the code checks whether reader. Obj. line. If so, it executes a continue to move on to the next row without appending it to csv.