I am scraping an HMTL table and the final dataframe creates a `Date' column that needs cleaning and formatting.
My scope is to convert this column to a data column.
Below my dataframe:
All I want to do after this step is to clean up the Datecolumn and convert this column to a pandas date column.
Any help?
Here is how to produce this table:
## web scrapping
import requests
import lxml.html as lh
import pandas as pd
url='https://markets.ft.com/data/funds/tearsheet/historical?s=LU0841585341:GBP'
#Create a handle, page, to handle the contents of the website
page = requests.get(url)
#Store the contents of the website under doc
doc = lh.fromstring(page.content)
#Parse data that are stored between <tr>..</tr> of HTML
tr_elements = doc.xpath('//tr')
#Create empty list
col=[]
i=0
#For each row, store each first element (header) and an empty list
for t in tr_elements[0]:
i+=1
name=t.text_content()
# print '%d:"%s"'%(i,name)
col.append((name,[]))
#Since out first row is the header, data is stored on the second row onwards
for j in range(1,len(tr_elements)):
#T is our j'th row
T=tr_elements[j]
#If row is not of size 10, the //tr data is not from our table
if len(T)!=6:
break
#i is the index of our column
i=0
#Iterate through each element of the row
for t in T.iterchildren():
data=t.text_content()
#Check if row is empty
if i>0:
#Convert any numerical value to integers
try:
data=int(data)
except:
pass
#Append the data to the empty list of the i'th column
col[i][1].append(data)
#Increment i for the next column
i+=1
Dict={title:column for (title,column) in col}
df=pd.DataFrame(Dict)
df.head()
