A python package that can create filled-map images (choropleths) for data analysis
robtkiller_choropleth
robtkiller
Overview
Abstract
# choropleth
A python package that can create filled-map images (choropleths) for data analysis
##Basic Usage
**choropleth**.**choropleth**(*map_type*, *data*, *min_val*, *max_val*, *base_color*, *steps*)
***map_type*** is a string giving the map template image to use as the basis for the choropleth.
***data*** is a dictionary containing the values corresponding to each each sector of the map. Expected keys will depend on the *map_type* as decribed in the table below.
**map_type** | **Key** | **Examples**
------------------|----------------------------|---------------
'us-counties' | State+County FIPS code | '01011', '55121'
'us-states' | 2-letter state abbreviation| 'NV', 'AL', 'CA'
'world-countries | 2-letter country code | 'us', 'cn', 'de'
***min_val*** and ***max_val*** are `float` or `int`'s and set the minimum and maximum values, respectively, to interpolate shading over.
Sectors with values greater than ***max_val*** are assigned the ***base_color***.
Sectors with values less than the ***min_val*** are assigned the default gray.
***base_color*** is a string containing the hexidecimal color to interpolate shades from.
***steps*** is an `int` representing the number of intervals to calculate.
##Example
First, import the required modules and build a dictionary for your data.
```
import choropleth
import csv
data = {}
with open('poverty.csv','r') as f:
reader = csv.reader(f)
counties = list(reader)
for county in counties:
try:
data[county[1]] = float(county[11])
except:
pass
```
Next, create choropleth data.
```
choro = cp.choropleth('us-counties', data,3.0,35.0 , '#A40000', 6)
```
Finally, write it to a file.
```
with open('poverty.svg','w') as my_map:
my_map.write(choro)
```
A python package that can create filled-map images (choropleths) for data analysis
##Basic Usage
**choropleth**.**choropleth**(*map_type*, *data*, *min_val*, *max_val*, *base_color*, *steps*)
***map_type*** is a string giving the map template image to use as the basis for the choropleth.
***data*** is a dictionary containing the values corresponding to each each sector of the map. Expected keys will depend on the *map_type* as decribed in the table below.
**map_type** | **Key** | **Examples**
------------------|----------------------------|---------------
'us-counties' | State+County FIPS code | '01011', '55121'
'us-states' | 2-letter state abbreviation| 'NV', 'AL', 'CA'
'world-countries | 2-letter country code | 'us', 'cn', 'de'
***min_val*** and ***max_val*** are `float` or `int`'s and set the minimum and maximum values, respectively, to interpolate shading over.
Sectors with values greater than ***max_val*** are assigned the ***base_color***.
Sectors with values less than the ***min_val*** are assigned the default gray.
***base_color*** is a string containing the hexidecimal color to interpolate shades from.
***steps*** is an `int` representing the number of intervals to calculate.
##Example
First, import the required modules and build a dictionary for your data.
```
import choropleth
import csv
data = {}
with open('poverty.csv','r') as f:
reader = csv.reader(f)
counties = list(reader)
for county in counties:
try:
data[county[1]] = float(county[11])
except:
pass
```
Next, create choropleth data.
```
choro = cp.choropleth('us-counties', data,3.0,35.0 , '#A40000', 6)
```
Finally, write it to a file.
```
with open('poverty.svg','w') as my_map:
my_map.write(choro)
```
Authoring entity
| Agency Name | Role |
|---|---|
| robtkiller | owner |
Language
English
Methods, software and scripts
Software
| Name | Libraries or packages used |
|---|---|
| Python |
os, setuptools, math, bs4
|
License
| Name |
|---|
| GNU General Public License v2.0 |
Metadata production
Producers
| Name | Role |
|---|---|
| GitHub Bot | bot |
Date of Production
01 December 2019