从网络抓取的出生姓名数据中确定最常见的姓名

Question

我的任务是从此页面 https://www.ssa.gov/cgi-bin/popularnames.cgi 进行网页抓取。在那里您可以找到最常见的出生名字列表。现在，我必须找到给定年份中男孩和女孩最常用的名字（换句话说，两种性别使用完全相同的名字），但我不知道如何才能做到这一点。使用下面的代码，我解决了先前的任务以输出给定年份的列表，但我不知道如何修改我的代码，以便获得女孩和男孩最常用的名字。

import requests
import lxml.html as lh


url = 'https://www.ssa.gov/cgi-bin/popularnames.cgi'
string = input("Year: ")
r = requests.post(url, data=dict(year=string, top="1000", number="n" ))



doc = lh.fromstring(r.content)
tr_elements = doc.xpath('//table[2]//td[2]//tr')
cols = []


for col in tr_elements[0]:
    name = col.text_content()
    number = col.text_content()
    cols.append((number, []))


count=0
for row in tr_elements[1:]:
    i = 0
    for col in row:
        val = col.text_content()
        cols[i][1].append(val)
        i += 1
        if(count<4):
            print(val, end = '  ')
            count += 1
        else:
            count=0
            print(val)

Answer 1

这是一种方法。第一步是按姓名对数据进行分组，并记录有多少性别使用了该姓名及其合计总数。之后，我们可以使用它按具有多个性别的名称过滤结构。最后，我们按计数对这个多性别列表进行排序，并取第 0 个元素。这是我们今年最受欢迎的多性别名字。

import requests
import lxml.html as lh

url = "https://www.ssa.gov/cgi-bin/popularnames.cgi"
year = input("Year: ")
response = requests.post(url, data=dict(year=year, top="1000", number="n"))
doc = lh.fromstring(response.content)
tr_elements = doc.xpath("//table[2]//td[2]//tr")
column_names = [col.text_content() for col in tr_elements[0]]
names = {}
most_common_shared_names_by_year = {}

for row in tr_elements[1:-1]:
    row = [cell.text_content() for cell in row]

    for i, gender in ((1, "male"), (3, "female")):
        if row[i] not in names:
            names[row[i]] = {"count": 0, "genders": set()}

        names[row[i]]["count"] += int(row[i+1].replace(",", "")) 
        names[row[i]]["genders"].add(gender)

shared_names = [
    (name, data) for name, data in names.items() if len(data["genders"]) > 1
]
most_common_shared_names = sorted(shared_names, key=lambda x: -x[1]["count"])
print("%s => %s" % most_common_shared_names[0])

如果你很好奇，这里是自 2000 年以来的结果：

2000 => Tyler, 22187
2001 => Tyler, 19842
2002 => Tyler, 18788
2003 => Ryan, 20171
2004 => Madison, 20829
2005 => Ryan, 18661
2006 => Ryan, 17116
2007 => Jayden, 17287
2008 => Jayden, 19040
2009 => Jayden, 19053
2010 => Jayden, 18641
2011 => Jayden, 18064
2012 => Jayden, 16952
2013 => Jayden, 15462
2014 => Logan, 14478
2015 => Logan, 13753
2016 => Logan, 12099
2017 => Logan, 15117

从网络抓取的出生姓名数据中确定最常见的姓名

Determining most common name from web scraped birth name data

python

lxml

web-scraping