leoneperdigao / csv-parser

Basic implementation of a CSV-Reader Using Basic Python File I/O

CSV Parser

A Python class that provides methods for parsing CSV files.

Requirements

The following packages are required to run the code:

• python-dateutil

• You can install these packages by running the following command:

pip install -r requirements.txt

Usage

Import the CsvParser class and use the parse method to parse a CSV file:

from csv_parser.parser import CsvParser

csv_parser = CsvParser()
data = csv_parser.parse('path/to/file.csv')

The class takes several optional arguments:

• encoding: The encoding used in the CSV file. Default is utf-8-sig.
• delimiter: The delimiter used in the CSV file. Default is ,.
• quotechar: The character used to quote fields in the CSV file. Default is ".
• quoting: The quoting mode used in the CSV file. Default is False (no quoting).
• date_format: The format used to parse dates in the CSV file. Default is None.
• return_errors: Whether to return a list of errors or not. Default is False.

If return_errors is True, a tuple is returned with the list of dictionaries and a list of errors.

If the file cannot be opened or does not exist, an FileNotFoundError error will be raised.

Examples

Parsing a CSV file with default settings:

from csv_parser.parser import CsvParser

csv_parser = CsvParser()
data = csv_parser.parse('data.csv')

Parsing a CSV file with a custom delimiter and quoting:

from csv_parser.parser import CsvParser

csv_parser = CsvParser(delimiter=';', quotechar="'", quoting=True)
data = csv_parser.parse('data.csv')

Parsing a CSV file with custom date format:

from csv_parser.parser import CsvParser

csv_parser = CsvParser(date_format='%Y-%m-%d %H:%M:%S')
data = csv_parser.parse('data.csv')

Parsing a CSV file and returning errors:

from csv_parser.parser import CsvParser

csv_parser = CsvParser(return_errors=True)
data, errors = csv_parser.parse('data.csv')

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CSV Statistics Calculator

This Python code computes statistics for each numeric column in a given CSV file. The statistics calculated are the minimum value, maximum value, mean and standard deviation.

Requirements

The following packages are required to run the code:

• numpy

• python-dateutil

• You can install these packages by running the following command:

pip install -r requirements.txt

Usage

Import the compute_statistics function from the CsvStatisticsCalculator module and pass the file path of the CSV file as an argument to the function:

from csv_parser.stats import compute_statistics

stats = compute_statistics('path/to/file.csv')

The function returns a dictionary of statistics for each numeric column in the CSV file. The keys of the outer dictionary are the column names, and the values are inner dictionaries containing the following keys: 'min', 'max', 'mean', and 'stdev'.

Example

Given the following CSV file:

Name, Age, Weight
John, 25, 70.5
Jane, 30, 65.2
Bob, 40, 80.0

The following code computes the statistics for each numeric column in the file:

from csv_parser.stats import compute_statistics

stats = compute_statistics('data.csv')
print(stats)

Output:

{
    'Age': {'min': 25.0, 'max': 40.0, 'mean': 31.666666666666668, 'stdev': 7.637626260691952},
    'Weight': {'min': 65.2, 'max': 80.0, 'mean': 71.23333333333333, 'stdev': 6.236621979668926}
}

License

This code is licensed under the MIT License.