Water when it freezes can’t get any colder. Ice in pure water is never colder then about 30 degrees. Add salt and you can reduce the freezing point to 20 or 25 degrees so the ice is colder. Colder ice keeps the cooler colder and therefore the cooler lasts longer.
Same principle of why they use salt in ice cream. Not just flavor but also to make ice cream extra cold.
I turns out that there is a lot of fat ladies out there. Now pass another cold one my way. 🍺 🤠
The number of miles you need to ride a bicycle to burn the same amount of calories as walking depends on several factors, including your weight, the speed at which you’re walking and cycling, and the terrain. Generally, cycling tends to burn more calories per unit of distance traveled compared to walking, primarily due to the increased effort and muscle engagement required.
On average, a person burns around 30-40 calories per mile when walking at a moderate pace (about 3-4 miles per hour). Cycling, on the other hand, can burn around 40-60 calories per mile when riding at a moderate speed (about 12-14 miles per hour). These calorie estimates can vary based on individual factors.
So, to provide a rough comparison:
Remember that this is a general estimation and the actual calories burned can vary based on your personal characteristics, intensity of exercise, and other factors.
I discovered the tabula-py library, which is an alternative to converting PDFs externally to Python using pdftotext -layout from the poppler set of utilities. This might be a good alternative to having to install and run a seperate command line program.
import tabula
import pandas as pd
pdf="/home/andy/nov16-Enrollment/AlbanyED_nov16.pdf"
df=pd.concat(tabula.read_pdf(pdf,pages='all', stream=True))
df.query("STATUS=='Active'")
| COUNTY | ELECTION DIST | STATUS | DEM | REP | CON | GRE | WOR | IND | WEP | REF | OTH | BLANK | TOTAL | COUNTY ELECTION DIST | Unnamed: 0 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Albany | ALBANY 001001 | Active | 67 | 19 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 13 | 101 | NaN | NaN |
| 3 | Albany | ALBANY 001002 | Active | 286 | 20 | 3 | 0 | 1 | 19 | 0 | 0 | 0 | 45 | 374 | NaN | NaN |
| 6 | Albany | ALBANY 001003 | Active | 468 | 32 | 9 | 3 | 1 | 19 | 0 | 0 | 0 | 81 | 613 | NaN | NaN |
| 9 | Albany | ALBANY 001004 | Active | 449 | 25 | 6 | 1 | 4 | 15 | 1 | 0 | 1 | 74 | 576 | NaN | NaN |
| 12 | Albany | ALBANY 001005 | Active | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | NaN | NaN |
| … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … |
| 7 | NaN | NaN | Active | 291 | 59 | 4 | 0 | 0 | 31 | 0 | 1 | 1 | 89 | 476 | Albany WATERVLIET 004004 | NaN |
| 10 | NaN | NaN | Active | 339 | 213 | 29 | 4 | 3 | 57 | 0 | 0 | 0 | 202 | 847 | Albany WESTERLO 000001 | NaN |
| 13 | NaN | NaN | Active | 348 | 119 | 17 | 1 | 5 | 43 | 0 | 0 | 0 | 160 | 693 | Albany WESTERLO 000002 | NaN |
| 16 | NaN | NaN | Active | 314 | 137 | 26 | 2 | 2 | 35 | 0 | 0 | 0 | 196 | 712 | Albany WESTERLO 000003 | NaN |
| 19 | NaN | NaN | Active | 91,658 | 35,821 | 2,991 | 525 | 608 | 9,612 | 49 | 14 | 198 | 41,926 | 183,402 | Albany County Total | NaN |
There also poppler bindings for python, which you can use after installing the poppler-cpp developer library. This should work but currently it’s not parsing things correctly.
from poppler import load_from_file, PageRenderer
pdf_document = load_from_file(pdfFile)
page_1 = pdf_document.create_page(0)
page_1_text = page_1.text()
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(page_1_text),
skiprows=3,
sep='\s{2,}',
engine='python',
error_bad_lines=False)
df
| COUNTY ELECTION DIST | STATUS | DEM | REP | CON | GRE | WOR | IND | WEP REF OTH BLANK TOTAL | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Albany ALBANY 001001 | Active | 67.0 | 19.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 13.0 | 101.0 |
| Inactive | 5.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 9.0 | |
| Total | 72.0 | 21.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 15.0 | 110.0 | |
| Albany ALBANY 001002 | Active | 286.0 | 20.0 | 3.0 | 0.0 | 1.0 | 19.0 | 0.0 | 0.0 | 0.0 | 45.0 | 374.0 |
| Inactive | 27.0 | 8.0 | 0.0 | 0.0 | 0.0 | 3.0 | 0.0 | 0.0 | 0.0 | 12.0 | 50.0 | |
| Total | 313.0 | 28.0 | 3.0 | 0.0 | 1.0 | 22.0 | 0.0 | 0.0 | 0.0 | 57.0 | 424.0 | |
| Albany ALBANY 001003 | Active | 468.0 | 32.0 | 9.0 | 3.0 | 1.0 | 19.0 | 0.0 | 0.0 | 0.0 | 81.0 | 613.0 |
| Inactive | 56.0 | 5.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 19.0 | 82.0 | |
| Total | 524.0 | 37.0 | 9.0 | 3.0 | 1.0 | 21.0 | 0.0 | 0.0 | 0.0 | 100.0 | 695.0 | |
| Albany ALBANY 001004 | Active | 449.0 | 25.0 | 6.0 | 1.0 | 4.0 | 15.0 | 1.0 | 0.0 | 1.0 | 74.0 | 576.0 |
| Inactive | 64.0 | 0.0 | 1.0 | 0.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0.0 | 13.0 | 82.0 | |
| Total | 513.0 | 25.0 | 7.0 | 1.0 | 4.0 | 19.0 | 1.0 | 0.0 | 1.0 | 87.0 | 658.0 | |
| Albany ALBANY 001005 | Active | 5.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 |
| Total | 5.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 | |
| Albany ALBANY 001006 | Active | 462.0 | 11.0 | 5.0 | 2.0 | 4.0 | 14.0 | 0.0 | 0.0 | 1.0 | 57.0 | 556.0 |
| Inactive | 63.0 | 3.0 | 0.0 | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 1.0 | 12.0 | 84.0 | |
| Total | 525.0 | 14.0 | 5.0 | 2.0 | 4.0 | 19.0 | 0.0 | 0.0 | 2.0 | 69.0 | 640.0 | |
| Albany ALBANY 001007 | Active | 435.0 | 19.0 | 1.0 | 0.0 | 2.0 | 19.0 | 0.0 | 0.0 | 0.0 | 62.0 | 538.0 |
| Inactive | 60.0 | 3.0 | 0.0 | 0.0 | 2.0 | 3.0 | 0.0 | 0.0 | 0.0 | 7.0 | 75.0 | |
| Total | 495.0 | 22.0 | 1.0 | 0.0 | 4.0 | 22.0 | 0.0 | 0.0 | 0.0 | 69.0 | 613.0 | |
| Albany ALBANY 001008 | Active | 160.0 | 7.0 | 1.0 | 0.0 | 2.0 | 2.0 | 0.0 | 0.0 | 1.0 | 37.0 | 210.0 |
| Page 1 of 46 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
