excellaint 0.1.0

A Python toolbox for dealing with some of the oddities that Excel can introduce into your dates.

ExcellAint

A Python toolbox for dealing with some of the oddities that Excel can introduce into your dates.

** Under Construction : Library may not work as described **

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One of the major issues with trying to import data from Excel into Python is that Excel routinely fucks dates, due to its decision to store dates as floats and then present them in whichever format it is set to. This can lead to all sorts of downstream issues, most commonly (in my experience) the switching of month and day. For example, you might wind up with something like the following:

Date	Very Important Data
...	...
01/11/2020	abc
01/12/2020	ijk
13/01/2020	pqrs
14/01/2020	xyz
...	...

The goal of excellaint is to provide some tools which aim to fix these issues.

Installation

pip install excellaint

Usage

Currently, excellaint exposes a configuration class, accessible through excellaint.config, and a single function: excellaint.parse_datetime_column

Configuration is global and aims to take as much of the hassle of setting up how data ought to be treated out of the function call. It is assumed that generally, the datetime issues excellaint addresses will be common to all excel spreadsheets in a dataset, as they are generally a function of locale.

As such, it is recommended to set up the configuration at the start of your script, like so:

import excellaint as ea
import pandas as pd

ea.config.date_sep = "/"
ea.config.datetime_sep = " "


test_file = "./test/test_data/2_digit_yr.xlsx"

test_df = pd.read_excel(test_file)

print(test_df)

          mangled_dates  row_id
0        01/01/20 12:00       0
1        01/01/20 01:00       1
2        01/01/20 02:00       2
3        01/01/20 03:00       3
4        01/01/20 04:00       4
...                 ...     ...
17539  31/12/2021 19:00   17539
17540  31/12/2021 20:00   17540
17541  31/12/2021 21:00   17541
17542  31/12/2021 22:00   17542
17543  31/12/2021 23:00   17543

[17544 rows x 2 columns]

The package should be relatively performant - future versions will be quite significantly faster as the current approach is not very sophisticated.

%%timeit 
df = ea.parse_datetime_column(test_df,"mangled_dates")
print(df)

shape: (17_544, 2)
┌────────┬─────────────────────┐
│ row_id ┆ Datetime            │
│ ---    ┆ ---                 │
│ i64    ┆ datetime[μs]        │
╞════════╪═════════════════════╡
│ 0      ┆ 2020-01-01 12:00:00 │
│ 1      ┆ 2020-01-01 01:00:00 │
│ 2      ┆ 2020-01-01 02:00:00 │
│ 3      ┆ 2020-01-01 03:00:00 │
│ 4      ┆ 2020-01-01 04:00:00 │
│ …      ┆ …                   │
│ 17539  ┆ 2021-12-31 19:00:00 │
│ 17540  ┆ 2021-12-31 20:00:00 │
│ 17541  ┆ 2021-12-31 21:00:00 │
│ 17542  ┆ 2021-12-31 22:00:00 │
│ 17543  ┆ 2021-12-31 23:00:00 │
└────────┴─────────────────────┘

63 ms ± 1.86 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In the future, options to use per-call configuration will be added - for now if it needs to change multiple times in a script you will need to set it each time.