Creates SVG choropleths of Chilean regions
Project description
clChoropleth
It creates a SVG Choropleth of a Chilean region.
Description
You need a Dictionary or a Pandas.Series with the values. The index or key can be the Comuna name and the value an int. The package uses “clcomuna” to get the comuna code, and it creates bins for the values. You can specify the comuna region, even if your data has comunas for the hole country.
Installation
pip3 install clchoropleth
Requirements
clcomuna
pandas
bf4 (BeautifulSoup)
Python3.3+
Usage:
>>> import clchoropleth
Example
import clchoropleth arica = {'ARICA': 27, 'CAMARONES': 13, 'GENERAL LAGOS': 10, 'PUTRE': 8} data = clchoropleth.prepare_data(arica) clchoropleth.run(data, "arica.svg", "15")
This should save an SVG file similar to this:
prepare_data
def prepare_data(data, t=70, noNone=False):
Prepares the dataset. If its a dictionary, it turns it into a pandas.Series object. It checks the comuna name and uses its code instead. You can specify the threshold for the clcomuna package. It aggregates by comuna code, so, for exmaple, if you have {'arica':10, 'Arica':11, 'ARICA':12} it will return 'arica':33 as a pd.Series. It can drops the ‘None’s that you will get for below the threshold. Returns a pd.Series
arica = {'ARICA': 27, 'CAMARONES': 13, 'GENERAL LAGOS': 10, 'PUTRE': 8, 'arica': 33} clchoropleth.prepare_data(arica) >>> 15101 60 15102 13 15201 8 15202 10 dtype: int64
select_comunas_from_region
def select_comunas_from_region (data, region):
Given the dataset (after prepare_data), returns a subset with only the comunas from the given region. ‘region’ must be a string.
data = {'ARICA': 27, 'CAMARONES': 13, 'GENERAL LAGOS': 10, 'PUTRE': 8, 'arica': 33, 'iquique': 123, 'santiago': 999} data_p = prepare_data(data) data_p >>> 01101 123 13101 999 15101 60 15102 13 15201 8 15202 10 dtype: int64 select_comunas_from_region(data_p, "15") >>> 15101 60 15102 13 15201 8 15202 10 dtype: int64
discretize
def discretize(data, bins=5, quantile=False):
Changes the values of the data into a bin number. You can specify a number of bins to be used and if to use ‘quintile’ based cut or not.
arica >>> 15101 60 15102 13 15201 8 15202 10 dtype: int64 discretize(arica) >>> 15101 4 15102 0 15201 0 15202 0 dtype: category Categories (5, int64): [0 < 1 < 2 < 3 < 4] discretize(arica, 4, True) >>> 15101 3 15102 2 15201 0 15202 1 dtype: category Categories (4, int64): [0 < 1 < 2 < 3]
make_map
def make_map(data, filename, region, colors='orange'):
It creates the choropleth map and saves it to filename destination.
- Args:
data: The pd.Series with the data. The index needs to be the code and the value the bin number.
filename: The filename of the map we are going to save.
region: we need to specify the region again, as a string (“09”), to open the original svg file and modify it.
colors: it can be a string, if is one of the 5 pre-set color lists: orange, blue, green, red, pruple. Or it can be a list of strings, with each string being a color. For example:[‘#ffffcc’,’#c2e699’,’#78c679’,’#31a354’,’#006837’] It needs to have the same length as the number of bins. For more colors: http://colorbrewer2.org/
- Returns:
It saves the map. No return valule.
run
def run(data, filename, region, colors='orange', bins=5, quantile=False):
Is a wrapper function that runs all the other functions of the module, except for ‘preapare_data’. Doesn’t return anything.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.