官术网_书友最值得收藏!

Sort the countries by area size

You programmed three functions so far; now, let's add another one to our list by converting the code that generated a list of country names to a function and add this function to world_areas.py, as follows:

def get_country_names(datasource):
    """Returns a list of country names."""
    layer = datasource.GetLayerByIndex(0)
    country_names = []
    layer.ResetReading()
    for feature in layer:
        country_names.append(feature.GetFieldAsString(4))
    return country_names

Now, we have four functions, which are:

  • open_shapefile
  • transform_geometries
  • calculate_areas
  • get_country_names

All these functions return iterables, with each item sharing the same index on all of them, thus making it easy to combine the information.

So, let's take advantage of this feature to sort the countries by area size and return a list of the five biggest countries and their areas. For this, add another function, as follows:

def get_biggest_countries(countries, areas, elements=5):
    """Returns a list of n countries sorted by area size."""    
    countries_list = [list(country) 
                      for country in zip(areas, countries)]

    sorted_countries = sorted(countries_list, 
                              key=itemgetter(0), reverse=True) 
    return sorted_countries[:5]

In the first line, the two lists are zipped together, producing a list of country-area pairs. Then, we used the Python list's sorted method, but as we don't want the lists to be sorted by both values, we will define the key for sorting. Finally, the list is sliced, returning only the desired number of values.

  1. In order to run this code, you need to import the itemgetter function and put it at the beginning of the code but after from __future__ imports, as follows:
    from operator import itemgetter
  2. Now, edit the testing part of your code to look similar to the following:
    datasource = open_shapefile("../data/world_borders_simple.shp")
    transformed_geoms = transform_geometries(datasource, 4326, 3395)
    country_names = get_country_names(datasource)
    country_areas = calculate_areas(transformed_geoms)
    biggest_countries = get_biggest_countries(country_names, 
                                              country_areas)
    for item in biggest_countries:
        print("{}\t{}".format(item[0], item[1])) 
  3. Now, run the code and take a look at the results, as follows:
    Opening ../data/world_borders_simple.shp
    Number of features: 246
    82820725.1423 Russia
    51163710.3726 Canada
    35224817.514 Greenland
    21674429.8403 United States
    14851905.8596 China
    
    Process finished with exit code 0
主站蜘蛛池模板: 兰西县| 临沧市| 霍邱县| 新化县| 年辖:市辖区| 高台县| 嵊州市| 博客| 武义县| 分宜县| 甘谷县| 大石桥市| 栾城县| 莱西市| 富蕴县| 武邑县| 明光市| 芜湖县| 大渡口区| 新竹市| 山西省| 金塔县| 鄂州市| 襄城县| 家居| 上蔡县| 衡阳市| 准格尔旗| 梓潼县| 沭阳县| 衡南县| 永德县| 承德县| 邢台县| 通辽市| 汶上县| 宜都市| 南开区| 沾化县| 榕江县| 沁水县|