Easy CSV

`py_simple.easy_csv`

easy_csv is built to simplify reading and writing CSV files.

`append_row_to_csv(filepath, row, delimiter=',')`

Append a single row (as a dictionary or list) to an existing CSV file.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the CSV file.	required
`row`	`dict or list`	The row data to append.	required
`delimiter`	`str`	Field delimiter (default is comma).	`','`

Raises:

Type	Description
`FileNotFoundError`	If filepath doesn't exist.
`ValueError`	If the file is empty.

`count_csv_rows(filepath, include_header=False, delimiter=',')`

Count rows in a CSV file.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the CSV file.	required
`include_header`	`bool`	If True, include the header row in the count.	`False`
`delimiter`	`str`	Field delimiter (default is comma).	`','`

Returns:

Name	Type	Description
`int`	`int`	Number of rows in the CSV file.

Raises:

Type	Description
`FileNotFoundError`	If filepath doesn't exist.
`ValueError`	If the file is empty.

Example

The Py_simple WayThe Traditional Way

from py_simple import count_csv_rows

row_count = count_csv_rows(filepath="people.csv")
print(row_count)  # 3

import csv

with open("people.csv", "r", newline="", encoding="utf-8") as f:
    row_count = sum(1 for _ in csv.reader(f)) - 1
print(row_count)  # 3

`delete_csv_rows(filepath, column, value, delimiter=',')`

Deletes rows from a CSV file where a column matches the given value.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	The path to the CSV file.	required
`column`	`str`	The column to check for the given value.	required
`value`	`str`	The value used to find rows to delete.	required
`delimiter`	`str`	The field delimiter. Defaults to a comma.	`','`

Raises:

Type	Description
`FileNotFoundError`	If the CSV file does not exist.
`ValueError`	If the file is empty, the column does not exist, or no row matches the given value.

Example

The Py_simple WayThe Traditional Way

from py_simple import delete_csv_rows

delete_csv_rows(
    filepath="people.csv",
    column="Name",
    value="Alice"
)

import csv
import os
import tempfile

with open("people.csv", "r", newline="", encoding="utf-8") as f:
    reader = csv.DictReader(f)
    rows = list(reader)

rows = [row for row in rows if row["Name"] != "Alice"]

with open("people.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=reader.fieldnames)
    writer.writeheader()
    writer.writerows(rows)

`filter_csv_rows(filepath, column, value, return_dict=True, delimiter=',')`

Filter rows where a specific column equals the given value.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the CSV file.	required
`column`	`str`	Column name to filter on.	required
`value`	`str`	Value to match.	required
`return_dict`	`bool`	Whether to return dicts or lists (see read_csv_to_list).	`True`
`delimiter`	`str`	Field delimiter (default is comma).	`','`

Returns:

Name	Type	Description
`list`	`list[dict[str, Any]] \| list[list[Any]]`	Filtered rows (dicts or lists).

Raises:

Type	Description
`FileNotFoundError`	If filepath doesn't exist.
`ValueError`	If the column is not found.

Example

The Py_simple WayThe Traditional Way

from py_simple import filter_csv_rows

data = filter_csv_rows(filepath="people.csv", column="Name", value="Alice")
print(data)  # [{'Name': 'Alice', 'Age': '24'}]

import csv

with open("people.csv", "r", newline="", encoding="utf-8") as f:
    reader = csv.DictReader(f)
    data = [row for row in reader if row["Name"] == "Alice"]
print(data)  # [{'Name': 'Alice', 'Age': '24'}]

`get_csv_columns(filepath, delimiter=',')`

Retrieve a CSV column names (headers) from a CSV file.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the CSV file.	required
`delimiter`	`str`	Field delimiter (default is comma).	`','`

Returns:

Name	Type	Description
`list`	`list[str]`	Column names.

Raises:

Type	Description
`FileNotFoundError`	If filepath doesn't exist.
`ValueError`	If the file is empty.

Example

The Py_simple WayThe Traditional Way

from py_simple import get_csv_columns

columns = get_csv_columns(filepath="people.csv")
print(columns)  # ['Name', 'Age']

import csv

with open("people.csv", "r", newline="", encoding="utf-8") as f:
    reader = csv.reader(f)
    columns = next(reader)
print(columns)  # ['Name', 'Age']

`read_csv_column(filepath=None, column=None, delimiter=',', *, file_path=None, column_name=None)`

Read all values from a specific column in a CSV file.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the CSV file. Also accepts file_path as alias.	`None`
`column`	`str`	Column name to extract. Also accepts column_name as alias.	`None`
`delimiter`	`str`	Field delimiter (default is comma).	`','`
`file_path`	`str`	Keyword alias for filepath.	`None`
`column_name`	`str`	Keyword alias for column.	`None`

Returns:

Name	Type	Description
`list`	`list[str]`	Values from the specified column.

Raises:

Type	Description
`FileNotFoundError`	If filepath doesn't exist.
`ValueError`	If the file is empty or if the column is not found.

Example

The Py_simple WayThe Traditional Way

from py_simple import read_csv_column

names = read_csv_column(filepath="people.csv", column="Name")
print(names)  # ['Alice', 'Bob', 'Carol']

import csv

with open("people.csv", "r", newline="", encoding="utf-8") as f:
    reader = csv.DictReader(f)
    names = [row["Name"] for row in reader]
print(names)  # ['Alice', 'Bob', 'Carol']

`read_csv_to_list(filepath, return_dict=True, delimiter=',')`

Read a CSV file and return its contents Args: filepath (str): The path to the CSV file. return_dict (bool): If True, return list of dicts (keys are headers). delimiter (str): Field delimiter (default is comma).

Returns:

Name	Type	Description
`list`	`list[dict[str, Any]] \| list[list[Any]]`	Rows as dicts (if return_dict=True) or lists.

Raises:

Type	Description
`FileNotFoundError`	If filepath doesn't exist.
`ValueError`	If the file is empty.

Example

The Py_simple WayThe Traditional Way

from py_simple import read_csv_to_list

data = read_csv_to_list(filepath="people.csv")
print(data[0])  # {'Name': 'Alice', 'Age': '24'}

rows = read_csv_to_list(filepath="people.csv", return_dict=False)
print(rows[0])  # ['Alice','24']

import csv

with open("people.csv", "r", newline="", encoding="utf-8") as f:
    reader = csv.reader(f)
    rows = list(reader)
headers = rows[0]
data = [dict(zip(headers, row)) for row in rows[1:]]
print(data[0])  # {'Name': 'Alice', 'Age': '24'}

with open("people.csv", "r", newline="", encoding="utf-8") as f:
    reader = csv.reader(f)
    rows = list(reader)
print(rows[1])  # ['Alice', '24']  # 0 is a header row

`write_csv_from_list(filepath, data, headers=None, delimiter=',')`

Write data to a CSV file.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Output path.	required
`data`	`list`	List of dicts or list of lists.	required
`headers`	`list`	Column names. Required if data is list of lists and you want headers. If data is dict, keys are used.	`None`
`delimiter`	`str`	Field delimiter (default is comma).	`','`

Raises:

Type	Description
`ValueError`	If data is empty or invalid.

Example

The Py_simple WayThe Traditional Way

from py_simple import write_csv_from_list

people = [
    {"Name": "Alice", "Age": "24"},
    {"Name": "Bob", "Age": "31"},
]
write_csv_from_list(filepath="people.csv", data=people)

import csv

people = [
    {"Name": "Alice", "Age": "24"},
    {"Name": "Bob", "Age": "31"},
]
headers = list(people[0].keys())
with open("people.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=headers)
    writer.writeheader()
    writer.writerows(people)