14.3 Files in different locations and working with CSV files

File handling and CSV processing are crucial skills for working with data in Python. You'll learn to open, read, and write files, as well as parse CSV data. These techniques are essential for data analysis and manipulation tasks.

Understanding file paths, using the open() function, and working with the csv module are key concepts. You'll also explore reading and writing CSV files, parsing data, and handling different CSV formats. These skills will help you manage and process data effectively in your Python projects.

File Handling and CSV Processing

File paths and open() function

• File paths specify the location of a file on the computer's file system
  • Absolute file paths provide the complete path from the root directory to the file (C:\Users\username\Documents\file.txt)
  • Relative file paths specify the path relative to the current working directory (data\file.txt)
• Opening files using the open() function allows access to the file's contents (file I/O)
  • Syntax:

    open(file_path, mode)

    • file_path

      represents the path to the file as a string

    • mode

      specifies how the file should be opened ('r' for read, 'w' for write, 'a' for append)
  • Example:

    file = open('data.txt', 'r')

    opens the file 'data.txt' in read mode
• Closing files using the close() method ensures proper resource management
  • Syntax:

    file.close()

  • Example:

    file.close()

    closes the previously opened file
• Using the with statement provides automatic file closure and resource management
  • Syntax:

    with open(file_path, mode) as file:

  • Example:

    with open('data.txt', 'r') as file:

    opens the file and automatically closes it after the block

Reading and parsing CSV data

• Reading CSV files using the csv module simplifies the process of working with comma-separated values
  • Import the csv module:

    import csv

  • Opening a CSV file:

    with open('data.csv', 'r') as file:

  • Creating a CSV reader object:

    csv_reader = csv.reader(file)

• Parsing CSV data involves extracting values from each row of the CSV file
  • Iterating over rows:

    for row in csv_reader:

  • Accessing individual values within a row:

    value = row[index]

• Extracting data from CSV files allows for further processing and analysis
  • Storing data in lists or dictionaries
    • Example:

      data = [row for row in csv_reader]

      stores all rows in a list
  • Filtering and processing data based on specific criteria
    • Example:

      filtered_data = [row for row in csv_reader if int(row[1]) > 10]

      filters rows based on a condition
• Handling different CSV dialects and delimiters ensures compatibility with various CSV formats
  • Specifying the delimiter:

    csv_reader = csv.reader(file, [delimiter](https://www.fiveableKeyTerm:Delimiter)=';')

    sets the delimiter to a semicolon
  • Using the csv.Dialect class to define custom dialects for non-standard CSV formats

Writing data to CSV files

• Writing CSV files using the csv module simplifies the process of creating comma-separated value files
  • Opening a CSV file in write mode:

    with open('output.csv', 'w') as file:

  • Creating a CSV writer object:

    csv_writer = csv.writer(file)

• Formatting data for CSV files involves preparing the data in a suitable format for writing
  • Creating a list of values to write as a row:

    row = [value1, value2, value3]

  • Writing a row to the CSV file:

    csv_writer.writerow(row)

• Writing multiple rows to a CSV file efficiently writes a large amount of data
  • Creating a list of rows:

    rows = [[value1, value2], [value3, value4]]

  • Writing all rows at once:

    csv_writer.writerows(rows)

• Specifying CSV file formatting options customizes the output format
  • Delimiter:

    csv_writer = csv.writer(file, delimiter=';')

    sets the delimiter to a semicolon
  • Quotechar:

    csv_writer = csv.writer(file, quotechar='"')

    sets the quote character to double quotes
  • Lineterminator:

    csv_writer = csv.writer(file, lineterminator='\n')

    sets the line terminator to a newline character
• Handling file permissions and exceptions ensures proper file access and error handling
  • Ensuring proper file permissions before writing to avoid permission-related errors
  • Catching and handling exceptions (IOError, PermissionError) gracefully handles errors during file operations

Data Exchange and Serialization

• CSV is a common data exchange format for tabular data
• Serialization converts complex data structures into a format suitable for storage or transmission
• Encoding specifies how characters are represented in binary form (e.g., UTF-8, ASCII)
• Other data exchange formats include JSON and XML, which offer more structured data representation