Intro to Web Scraping

from bokeh.resources import CDN
from bokeh.embed import file_html
html = file_html(p, CDN, "NZ_City_Letter_Analysis")
from IPython.core.display import HTML

What is Web Scraping

import scrapy

class BlogSpider(scrapy.Spider):
name = 'blogspider'
start_urls = ['']

def parse(self, response):
for title in response.css('.post-header>h2'):
yield {'title': title.css('a ::text').get()}
for next_page in response.css(''):
yield response.follow(next_page, self.parse)
class NameSpider(scrapy.Spider):
name = 'names'
start_urls = ['']

def parse(self,response):
def extract_from_table(table_row,table_col):
return response.xpath(f"//tr[{table_row}]//td[{table_col}]//text()").get()

for i in range(2,1000):
yield {
'Place Name' : extract_from_table(i,1),
'Components' : extract_from_table(i,2),
'Meaning' : extract_from_table(i,3)
import pandas as pd
import collections
from collections import OrderedDict
import operator
import matplotlib.pyplot as plt
import numpy as np
import math
from import show, output_file
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from import HoverTool

names_df = pd.read_csv('names.csv', header=0, sep=',', quotechar='"')
nz_names = names_df['Place Name'].tolist()
nz_dict = { i : 0 for i in nz_names }
lcount = dict(OrderedDict([(l, 0) for l in letters]))

for name in nz_names:
nz_dict[name] = dict(OrderedDict([(l, 0) for l in letters]))
city_dict = nz_dict[name]
for c in name:
if c.upper() in letters:
city_dict[c.upper()] += 1

total_df = pd.DataFrame.from_dict(nz_dict)
total_df = total_df.T

max_letters_cities = total_df.idxmax().tolist()
lettercounts = total_df.loc[total_df.idxmax()].max().tolist()
maxletters = dict(OrderedDict([(l, 0) for l in letters]))
for i,l in enumerate(letters):
maxletters[l] = max_letters_cities[i]
maxletters[l] = (lettercounts[i])

summary_df = pd.DataFrame()
scale = 1
summary_df['Word_Name'] = total_df.idxmax()
summary_df['Count'] = total_df.loc[total_df.idxmax()].max()

source = ColumnDataSource(summary_df)

hover = HoverTool()
('Word', '@Word')

p = figure(x_range=summary_df.index.tolist(), plot_height=250, title="Letter Counts",

p.vbar(x='index', top='Count', width=0.9,source=source)
p.xgrid.grid_line_color = None
p.y_range.start = 0





Engineer | Software Developer | Data Scientist

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Jack McKew

Jack McKew

Engineer | Software Developer | Data Scientist

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