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import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
import os
import re
from lxml.html.clean import Cleaner
import json
def clean_html(raw_html):
cleaner = Cleaner(remove_tags=["sup"])
return cleaner.clean_html(raw_html).decode("utf-8")
EMPTY = "[EMPTY]"
def isYear(value):
for i in range(1990, 2022):
if str(i) in value:
return True
return False
def existTopHeaders(html):
first_row = html.tr
if first_row.td.string == None:
return True
for td in first_row.find_all("td"):
if not td.string:
continue
value = td.string.replace(",", "").strip()
if value:
try:
float(value[1:])
if isYear(value):
return True
else:
return False
except:
continue
return True
def belongToTopHeaders(row):
for i, td in enumerate(row.find_all("td")):
if not td.string:
continue
value = td.string.replace(",", "").strip()
if value:
try:
float(value[1:])
if isYear(value):
return True
else:
return False
except:
continue
return True
def handle_unnamed_single_topheader(columns, j):
tmp = j
while tmp < len(columns) and (columns[tmp].startswith("Unnamed") or columns[tmp] == EMPTY):
tmp += 1
if tmp < len(columns):
return columns[tmp]
tmp = j
while tmp >= 0 and (columns[tmp].startswith("Unnamed") or columns[tmp] == EMPTY):
tmp -= 1
if tmp < 0:
return f"data {j}"
else:
return columns[tmp]
def handle_unnamed_multi_topheader(columns, j):
tmp = j
while tmp < len(columns) and (columns[tmp][0].startswith("Unnamed") or columns[tmp][0] == EMPTY):
tmp += 1
if tmp < len(columns):
return columns[tmp][0]
tmp = j
while tmp >= 0 and (columns[tmp][0].startswith("Unnamed") or columns[tmp][0] == EMPTY):
tmp -= 1
if tmp < 0:
return f"data {j}"
else:
return columns[tmp][0]
def readHTML(html_string):
# file_path = html_path
html = BeautifulSoup(html_string, features='html.parser')
# remove superscripts and subscripts
for sup in html.select('sup'):
sup.extract()
for sup in html.select('sub'):
sup.extract()
# 1. locate top header
top_header_nonexist_flag = 0
if not existTopHeaders(html):
top_header_nonexist_flag = 1
new_tr_tag = html.new_tag("tr")
new_td_tag = html.new_tag("td")
new_tr_tag.insert(0, new_td_tag)
for i in range(len(html.tr.find_all("td")[1:])):
new_td_tag1 = html.new_tag("td")
new_td_tag1.string = f"data{i}"
new_tr_tag.insert(i+1, new_td_tag1)
html.table.insert(0, new_tr_tag)
else:
html.tr.td.string = ""
header = [0]
top_header_flag = True
for i, tr in enumerate(html.find_all("tr")):
# # for locating top header
# if tr.td.string and ("in thousands" in tr.td.string.lower() or "in millions" in tr.td.string.lower()) and len(tr.td.string) < len("in thousands") + 5:
# tr.td.replace_with(html.new_tag("td"))
if top_header_flag and i > 0 and not top_header_nonexist_flag:
if belongToTopHeaders(tr):
header.append(i)
else:
top_header_flag = False
# for locating left header
if tr.td.string != None:
for td in tr.find_all("td")[1:]:
if td.string == None:
td.string = EMPTY
data = pd.read_html(str(html), header=header, index_col=0)[0]
return data, header, top_header_nonexist_flag
def generateDescription(data, header, top_header_nonexist_flag):
describe_dict = {}
for i in range(data.shape[0]):
for j in range(data.shape[1]):
value = data.iloc[i, j]
if str(value).startswith("Unnamed") or str(value) == EMPTY or str(value) == "-" or str(value) == u'\u2014':
continue
describe = ""
if pd.isnull(data.index[i]):
describe += "total"
else:
describe += f"{data.index[i]}"
temp_i = i - 1
while temp_i >= 0:
if (data.iloc[temp_i] == EMPTY).all():
describe += f" {data.index[temp_i]}"
break
temp_i -= 1
if not top_header_nonexist_flag:
describe += " of"
if len(header) == 1:
describe += f" {handle_unnamed_single_topheader(data.columns, j)}"
else:
describe += f" {handle_unnamed_multi_topheader(data.columns, j)}"
prev = handle_unnamed_multi_topheader(data.columns, j)
for temp_j in header[1:]:
if data.columns[j][temp_j].startswith("Unnamed") or data.columns[j][temp_j] == EMPTY:
continue
if data.columns[j][temp_j] == prev:
continue
describe += f" {data.columns[j][temp_j]}"
prev = data.columns[j][temp_j]
describe += f" is {data.iloc[i, j]}."
x_index = i+len(header)
y_index = j+1
if top_header_nonexist_flag == 1:
x_index -= 1
describe_dict[f"{x_index}-{y_index}"] = describe
return describe_dict
def generateDiscreptionCell(data, header, top_header_nonexist_flag):
discribe_dict = {}
for i in range(data.shape[0]):
for j in range(data.shape[1]):
value = data.iloc[i, j]
if str(value).startswith("Unnamed") or str(value) == "-" or str(value) == "[EMPTY]":
continue
discribe = f"{data.iloc[i, j]}"
x_index = i+len(header)
y_index = j+1
if top_header_nonexist_flag == 1:
x_index -= 1
discribe_dict[f"{x_index}-{y_index}"] = discribe
return discribe_dict |