🔧 utilita
a utility library
Quick install
pip install utilita
Basic usage
[decent pitch]. Let's dive in.
Out-of-the-box you get some stuff you can do with utilita:
import datetime
from utilita import date_fns
w52d7 = datetime.date(2020,12,27)
w53d1 = datetime.date(2020,12,28)
date_fns.is_in_leap_week(w52d7) # => False
date_fns.is_in_leap_week(w53d1) # => True
date_fns.days_since_same_date_last_year(w52d7) # => 364 (days in non-leep-week years)
date_fns.days_since_same_date_last_year(w53d1) # => 371 (days in leep-week years)
more about ISO 8601 leap weeks: https://en.wikipedia.org/wiki/ISO_week_date
Examples:
Working with sendgrid
from utilita import sendgridhelper as sghelper
eml = sghelper.SendgridHelper(
sendgrid_api_key=os.getenv('sendgrid_key'),
from_email={"email": 'test@example.com', "name": "Test BI"}
)
email_subject = f'Testing email from Test BI sent on {int(datetime.datetime.now().timestamp())}'
email_body = '''
<html>
<body>
Hello, <br />
This is a test email<br />
<i>Derp</i>
<br><br>
Thank you,<br>
Test BI
</body>
'''
eml.config_email(subject=email_subject,
body=email_body,
recipients={
"to": "user1@example.com, user2@example.com",
# "cc": "test@example.com",
# "bcc": "bcc@example.com"
}
)
# Attach excel files from disk:
eml.attach_excel_file_from_path('files/1.xlsx')
eml.attach_excel_file_from_path('files/2.xlsx')
# attach dataframe as a csv file.
import pandas as pd
headers = ['date', 'department', 'sales']
data = [
('2023-12-20', 'Bread', 100),
('2023-12-20', 'Deli', 1),
('2023-12-20', 'Frozen', 400)
]
df = pd.DataFrame(columns=headers, data=data)
# compressed will compress to a zip file.
eml.attach_single_df_as_csv(df=df, df_filename='sales.csv', compressed=True)
# Send email
eml.send_email()
Example for working with excel workbooks
from utilita import excel
import pandas as pd
import openpyxl as xl
wb = xl.load_workbook(filename='files/tables.xlsx')
# Load dataframe into an excel table:
df = excel.excel_table_to_df(wb=wb, sheet_name='Sheet1', table_name='Table2')
# Get data from an excel table into a dataframe:
# Export table into excel workbook
headers = ['date', 'department', 'sales']
data = [
('2023-12-20', 'Bread', 100),
('2023-12-20', 'Deli', 1),
('2023-12-20', 'Frozen', 400)
]
df = pd.DataFrame(columns=headers, data=data)
excel.df_to_excel_table_resize(df=df, wb=wb, sheet_name='Sheet1', table_name='Table2')
Release files for utilita 1.0.14
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| utilita-1.0.14.tar.gz | 36.1 kB | Details |
Release files / utilita-1.0.14.tar.gz
| Download URL | utilita-1.0.14.tar.gz |
|---|---|
| Size | 36.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a01f6c2f683bd76104328f54b291bedbefe30db06a4b9abdf09207987457c52f
|
|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.9
|
