# Parse

> Interpret and convert data from one format to another.

Source: https://dagster.io/glossary/data-parsing

## Data parsing definition:

*Data parsing* is the process of interpreting and converting data from one format to another. It involves taking data, typically structured in a specific manner such as CSV, JSON, XML, etc., and transforming it into a format that is more useful or easier to process and analyze. This process usually involves a set of rules or patterns to understand the input data and to determine how the transformation should occur. Data parsing is a common task in areas like data preprocessing, web scraping, and data integration.

## Data parsing example using Python:

Let's look at an example of parsing CSV (Comma-Separated Values) data, which is a common task in data analysis. Please note that you need to [have the 'csv' library installed in your Python environment](https://dagster.io/glossary) to run this code.

Assume we have a CSV file named `data.csv` with the following content:

```plaintext
Name,Age,Occupation
Alice,30,Engineer
Bob,25,Doctor
Charlie,35,Teacher
```

Here is a Python script that parses this file using the `csv` module. Please ensure that the 'data.csv' file is in the same directory as your Python script, or provide an absolute path to the file.

```python trailing-newline
import csv

def parse_csv_file(file_name):
    with open(file_name, 'r') as file:
        reader = csv.reader(file)
        header = next(reader)  # This will store the header row (['Name', 'Age', 'Occupation'])
        rows = []
        for row in reader:
            # Convert each row to a dictionary with keys as the header and values as the row values
            rows.append(dict(zip(header, row)))
        return rows

data = parse_csv_file('data.csv')

for person in data:
    print(f"Name: {person['Name']}, Age: {person['Age']}, Occupation: {person['Occupation']}")
```

This script first opens the file and creates a CSV reader. It then stores the header row (which contains the field names) and iterates over the rest of the rows, converting each one to a dictionary where the keys are the field names and the values are the corresponding values from the row. This way, you get a list of dictionaries where each dictionary represents a person.

Run the script, and your parsed data will show as follows:

```plaintext
Name: Alice, Age: 30, Occupation: Engineer
Name: Bob, Age: 25, Occupation: Doctor
Name: Charlie, Age: 35, Occupation: Teacher
```