caradoc 0.0.2

A suite of tools to help with data analysis in python.

data-analysis

Python utilities for doing data analysis

Currently includes two utilities:

FinancialYear

Represents a Financial Year, from a string in the format "2020-21".

Usage:

from caradoc import FinancialYear

fy = FinancialYear("2020-21")

fy + 1  # FinancialYear("2021-22")
fy - 1  # FinancialYear("2019-20")

str(fy)  # "2020-21"
int(fy)  # 2020

Create from date or year

from datetime import date
from caradoc import FinancialYear

fy = FinancialYear.from_date(date(2020, 1, 1))
str(fy)  # "2019-20"

fy = FinancialYear.from_int(2020)
str(fy)  # "2020-21"

Useful utilities:

from caradoc import FinancialYear

fy = FinancialYear("2020-21")

fy.previous_n_years(4)  # [
#    FinancialYear("2016-17"),
#    FinancialYear("2017-18"),
#    FinancialYear("2018-19"),
#    FinancialYear("2019-20"),
#    FinancialYear("2020-21")
# ]

FinancialYear.range("2018-19", "2020-21")  # [
#    FinancialYear("2018-19"),
#    FinancialYear("2019-20"),
#    FinancialYear("2020-21"),
# ]

d = date(2021, 6, 1)
d in FinancialYear("2021-22")  # True
d in FinancialYear("2020-22")  # False

Currently years are hardcoded to end on 31st March but this will be changed.

ExcelTable

Represents a table in an Excel workbook.

The table itself is a pandas DataFrame. The DataFrame index is not written to the Excel file.

Allows for specifying a title, summary and notes for the table.

Parameters

• df: pandas DataFrame
• title: Optional title for the table
• summary: Optional summary for the table
• notes: Optional notes for the table

Methods

• to_excel_table(): Writes just the datatable (df) to an Excel file as a Table (with filters)
• to_excel(): Writes the table to an Excel file as a Table, with the title and summary as a header and the notes as a footer.

Usage

from caradoc import ExcelTable
import pandas as pd

df = pd.DataFrame({"alice": [1, 2, 3], "bob": [4, 5, 6]})
et = ExcelTable(
    df,
    title="Test Table"
)
with pd.ExcelWriter("test_file.xlsx", engine="auto") as writer:
    et.to_excel(writer, "test_sheet")

Output looks something like:

	A	B
1	Test Table
2
3	Alice	Bob
4	1	4
5	2	5
6	3	6

You can also include a summary (underneath the title) and notes (underneath the table) using summary= and notes=

DataOutput

Represents a collection of ExcelTables to be written to an Excel file.

Methods

• add_table(): Adds a table to the DataOutput
• write(): Writes the DataOutput to an Excel file

Usage

from caradoc import DataOutput, ExcelTable
import pandas as pd

output = DataOutput()

df1 = pd.DataFrame({"alice": [1, 2, 3], "bob": [4, 5, 6]})
table1 = ExcelTable(
    df1,
    title="Test Table"
)

output.add_table("test_sheet", table1)

df2 = pd.DataFrame({"alice": [1, 2, 3], "bob": [4, 5, 6]})
output.add_table("test_sheet", df2, title="Test Table 2")

output.write("test_file.xlsx")

Output of test_file.xlsx will be an excel workbook with a sheet called "test_sheet". The sheet will have two tables, each table with a title and spacing between them.

Development

Run tests

Tests can be run with pytest:

hatch run test

Test coverage

hatch run cov-html

Run typing checks

hatch run lint:typing

Linting

Black and ruff should be run before committing any changes.

hatch run lint:style

Run all checks at once

hatch run lint:all

Publish to pypi

python -m build
twine upload dist/*
git tag v<VERSION_NUMBER>
git push origin v<VERSION_NUMBER>

Install development version

The development requirements are installed using pip install -r dev-requirements.txt.

Any additional requirements for the module itself must be added to install_requires in setup.py. You should then generate a new requirements.txt using using pip-tools (pip-compile). You can then run pip-sync to install the requirement.

Any additional development requirements must be added to dev-requirements.in and then the dev-requirements.txt should be generated using pip-compile dev-requirements.in. You can then install the development requirements using pip-sync dev-requirements.txt.