Easy CSV

Working with CSV files is common when storing or exchanging structured data. Whether you're reading information, creating CSV files, checking columns, or filtering rows, easy_csv provides simple helpers that make working with CSV data easy to read and understand.

A small real-world example

Imagine you're managing a list of people stored in a CSV file. You want to read the data, check which columns are available, and find a specific person without writing the CSV parsing logic yourself.

from py_simple import read_csv_to_list, get_csv_columns, filter_csv_rows

columns = get_csv_columns("people.csv")
print(columns)

people = read_csv_to_list("people.csv")
print(people)

alice = filter_csv_rows(
    filepath="people.csv",
    column="Name",
    value="Alice",
)
print(alice)

Example output:

['Name', 'Age']
[{'Name': 'Alice', 'Age': '24'}, {'Name': 'Bob', 'Age': '31'}]
[{'Name': 'Alice', 'Age': '24'}]

What happened?

get_csv_columns() retrieves the column names from the first row of the CSV file.

read_csv_to_list() reads the CSV file and returns its contents as a list of dictionaries by default. You can also set return_dict=False to receive the rows as lists.

filter_csv_rows() finds rows where a specific column matches the value you provide.

You can also use write_csv_from_list() to create a CSV file from a list of dictionaries or lists.

from py_simple import write_csv_from_list

people = [
    {"Name": "Alice", "Age": "24"},
    {"Name": "Bob", "Age": "31"},
]

write_csv_from_list("people.csv", people)

Why use these helpers?

Instead of repeatedly opening files, creating CSV readers and writers, handling headers, and filtering rows manually, you can simply write:

people = read_csv_to_list("people.csv")

alice = filter_csv_rows(
    "people.csv",
    column="Name",
    value="Alice",
)

These helpers keep working with CSV files simple, readable, and beginner-friendly while making common tasks such as reading, writing, inspecting, and filtering CSV data easier.