Wikidata powered comparisons

howmany is a Python package that leverages the Wikidata REST API to easily find and compare the dimensions of any object. You can use howmany to find the answers to questions including:

• How tall is the Eiffel Tower?
• How many association football pitches would fit inside Germany (or that giant new wind farm)?
• And eventually anything else that Wikidata has dimensions for!

Of note is also that howmany can do unit conversions for users in comparison calculations.

Contents

• Installation
• Environment Setup
• Examples
• To-Do

Installation ⇧

howmany can be downloaded from PyPI via pip or sourced directly from this repository:

pip install howmany

git clone https://github.com/andrewtavis/howmany.git
cd howmany
python setup.py install

import howmany

Environment Setup ⇧

The development environment for howmany can be installed via the following steps:

1. Fork the howmany repo, clone your fork, and configure the remotes:

[!NOTE]

Consider using SSH

Alternatively to using HTTPS as in the instructions below, consider SSH to interact with GitHub from the terminal. SSH allows you to connect without a user-pass authentication flow.

To run git commands with SSH, remember then to substitute the HTTPS URL, https://github.com/..., with the SSH one, git@github.com:....

• e.g. Cloning now becomes git clone git@github.com:<your-username>/howmany.git

GitHub also has their documentation on how to Generate a new SSH key 🔑

# Clone your fork of the repo into the current directory.
git clone https://github.com/<your-username>/howmany.git
# Navigate to the newly cloned directory.
cd howmany
# Assign the original repo to a remote called "upstream".
git remote add upstream https://github.com/andrewtavis/howmany.git

• Now, if you run git remote -v you should see two remote repositories named:
  • origin (forked repository)
  • upstream (howmany repository)

2. Use Anaconda to create the local development environment within your howmany directory:

   conda env create -f environment.yml
   

Examples ⇧

See the example Jupyter notebook for full examples.

import howmany
from howmany.utils import (
    float_to_str,
    get_wd_ent_label,
    get_wd_ent_prop_amount,
    get_wd_ent_prop_unit,
)

Getting labels, amounts and units

eiffel_tower_qid = "Q243"
height_pid = "P2048"

eiffel_tower_label = get_wd_ent_label(qid=eiffel_tower_qid)
eiffel_tower_height = get_wd_ent_prop_amount(qid=eiffel_tower_qid, pid=height_pid)
eiffel_tower_height_unit = get_wd_ent_prop_unit(qid=eiffel_tower_qid, pid=height_pid)

print(
    f"The {eiffel_tower_label} is {round(eiffel_tower_height)} {eiffel_tower_height_unit}s tall."
)
# The Eiffel Tower is 324 metres tall.

Simple comparisons of Wikidata items

germany_qid = "Q183"
soccer_field_qid = "Q8524"
area_pid = "P2046"

soccer_fields_in_germany_dict = howmany.compare(
    containers=germany_qid, entities=soccer_field_qid, pid=area_pid
)

for k in soccer_fields_in_germany_dict.keys():
    amount = round(soccer_fields_in_germany_dict[k]["amount"], 2)
    print(
        f"You could fit {amount:,} {soccer_fields_in_germany_dict[k]['entity']}es inside {k}."
    )

# You could fit 50,453,300.88 association football pitches inside Germany.

germanies_in_soccer_fields_dict = howmany.compare(
    containers=soccer_field_qid, entities=germany_qid, pid=area_pid
)

for k in germanies_in_soccer_fields_dict.keys():
    amount = float_to_str(f=germanies_in_soccer_fields_dict[k]["amount"])
    print(
        f"You could fit {amount} {germanies_in_soccer_fields_dict[k]['entity']}s inside an {k}."
    )

# You could fit 0.0000000198 Germanys inside an association football pitch.

Comparisons of Wikidata items with predefined amounts

# Variables defined above...
giant_new_wind_farm_label = "giant new wind farm"
area_of_giant_new_wind_farm = 50
unit_of_giant_new_wind_farm_area = "square kilometre"

soccer_fields_in_giant_new_wind_farm_dict = howmany.compare(
    containers=giant_new_wind_farm_label,
    container_amounts=area_of_giant_new_wind_farm,
    container_units=unit_of_giant_new_wind_farm_area,
    entities=soccer_field_qid,
    pid=area_pid,
)

for k in soccer_fields_in_giant_new_wind_farm_dict.keys():
    amount = round(soccer_fields_in_giant_new_wind_farm_dict[k]["amount"], 2)
    print(
        f"You could fit {amount:,} {soccer_fields_in_giant_new_wind_farm_dict[k]['entity']}es inside the {k}."
    )

# You could fit 7,054.67 association football pitches inside the giant new wind farm.

Comparisons across lists of containers or entities

# Variables defined above...
import matplotlib.pyplot as plt
import seaborn as sns

all_german_state_qids = ["Q64", ...]
saarland_qid = "Q1201"

saarlands_in_german_states_dict = howmany.compare(
    containers=all_german_state_qids, entities=saarland_qid, pid=area_pid  # , iso="en"
)

# Code to order labels and area ratios...

ax = sns.barplot(
    x=german_states_desc_saarland_area, y=german_state_desc_areas_in_saarlands
)
ax.set_title("Area of German States in Saarlands", size=18)
ax.set(xlabel="German State", ylabel="Area (Saarlands)")
ax.bar_label(ax.containers[0])
ax.xaxis.label.set_size(14)
ax.yaxis.label.set_size(14)
plt.xticks(rotation=45)

plt.show()

To-Do ⇧

Please see the contribution guidelines if you are interested in contributing to this project. Work that is in progress or could be implemented includes:

• Expand the unit conversion process in utils.py
• Keep requests below the Wikidata REST API rate limit in howmany.compare()
• Other suggestions welcome!

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