Obtaining Correct Date In Pandas DataFrame

[eddywinch82]

Hi there,

I have a Python Code, which runs fine, but I want the Correct complete Date shown, in the Date Column, in each Row of the DataFrame Output.

Due to the layout of the Schedule Table, at that particular URL, only the day is shown in the Date Column, in each Row displayed.

Here is the Code :-

import pandas as pd
import requests
from bs4 import BeautifulSoup

res = requests.get("http://web.archive.org/web/20041020000138/http://www.raf.mod.uk/bbmf/displaydates.html")
soup = BeautifulSoup(res.content,'lxml')
table = soup.find_all('table', align="CENTER")[0]
df = pd.read_html(str(table))

df = df[0]
df = df.rename(columns=df.iloc[0])
df = df.iloc[2:]
df.head(15)
pd.options.display.max_rows = 1000

display = df[(df['Location'].str.contains('[a-zA-Z]')) & (df['Lancaster'].str.contains('X')) & (df['Spitfire'].str.contains('X', na=True)) & (df['Dakota'].str.contains('X', na=True))]    
display 

#display.drop('Dakota', axis=1, inplace=True)
display.dropna(subset=['Spitfire', 'Hurricane'], how='all')

And this is the Output :-

Output:
Date 	Location 	Lancaster 	Spitfire 	Hurricane 	Dakota
21 	14 	Digby 	X 	X 	X 	NaN
22 	15 	Harlaxton 	X 	X 	X 	NaN
23 	15 	Coleraine 	X 	X 	X 	NaN
30 	16 	New Leake 	X 	X 	X 	X
31 	16 	Blackpool 	X 	X 	X 	NaN
32 	16 	Elvington 	X 	X 	X 	NaN
46 	30 	Conington 	X 	X 	X 	NaN
47 	30 	Enfield 	X 	X 	X 	NaN
48 	30 	Southend 	X 	X 	X 	NaN
51 	31 	Southend 	X 	X 	X 	NaN
53 	31 	Sharnbrook 	X 	X 	X 	NaN
54 	31 	Peterborough 	X 	X 	X 	NaN
62 	5 	Portsmouth 	X 	X x 2 	NaN 	NaN
63 	5 	Sandown (IoW) 	X 	X x 2 	NaN 	NaN
64 	5 	Yarmouth Harbour (IoW) 	X 	X x 2 	NaN 	NaN
68 	6 	Newhaven Fort 	X 	X x 2 	NaN 	NaN
71 	6 	Westerham 	X 	X x 2 	NaN 	NaN
72 	6 	Arromanche 	X 	X x 2 	NaN 	NaN
75 	7 	Normandy 	X 	X x 2 	NaN 	X
79 	12 	Digby 	X 	X 	NaN 	NaN
80 	12 	Bracebridge Heath 	X 	X 	NaN 	NaN
81 	12 	Newark 	X 	X x 2 	X 	X
82 	12 	Castle Donington 	X 	X 	NaN 	NaN
87 	13 	Barrow in Furness 	X 	X 	X 	NaN
88 	13 	Cosford 	X 	X 	X 	X
89 	13 	Loughborough 	X 	X 	X 	NaN
91 	13 	Little Bytham 	X 	X 	NaN 	NaN
128 	26 	Woodston 	X 	X 	X 	NaN
129 	26 	Barrowden 	X 	X 	X 	NaN
130 	26 	Skillington 	X 	X 	X 	NaN
131 	26 	East Bridgeford 	X 	X 	X 	NaN
132 	26 	Waddington 	X 	X 	X 	X
139 	27 	Waddington 	X 	X 	X 	X
151 	3 	Tattershall 	X 	X 	NaN 	NaN
162 	3 	Branston 	X 	X 	X 	NaN
163 	3 	Ripley Castle 	X 	X 	X 	NaN
175 	4 	Spilsby 	X 	X 	NaN 	NaN
185 	10 	East Fortune 	X 	X 	X 	NaN
186 	10 	Thirsk 	X 	X 	X 	NaN
203 	14 	Culdrose 	X 	X 	X 	NaN
204 	14 	Shrivenham 	X 	X 	X 	NaN
208 	17 	Old Brampton 	X 	X 	X 	NaN
209 	17 	Glenridding 	X 	X 	X 	NaN
210 	17 	Carlisle 	X 	X 	X 	NaN
219 	23 	Llandiloes 	X 	X 	X 	X
220 	23 	Benson 	X 	NaN 	X 	NaN
221 	24 	Farnborough 	X 	X 	X 	NaN
222 	24 	North Coates 	X 	X 	X 	NaN
223 	24 	Scunthorpe 	X 	X 	X 	NaN
224 	24 	Breighton 	X 	X 	X 	NaN
225 	24 	Sunderland 	X 	X 	X 	NaN
226 	25 	Sunderland 	X 	X 	X 	NaN
227 	25 	York 	X 	X 	X 	NaN
228 	25 	Breighton 	X 	X 	X 	NaN
229 	25 	Luddington 	X 	X 	X 	NaN
230 	25 	North Coates 	X 	X 	X 	NaN
231 	25 	Sutton on Sea 	X 	X 	X 	NaN
235 	29 	Lowestoft 	X 	X 	X 	NaN
236 	30 	Lowestoft 	X 	X 	X 	NaN
240 	31 	Lincoln Showground 	X 	X 	X 	NaN
241 	31 	Kielder Forest 	X 	X 	X 	NaN
242 	31 	Windermere 	X 	X 	X 	NaN
244 	1 	Windermere 	X 	X 	X 	NaN
245 	1 	Cudworth 	X 	X 	X 	NaN
246 	1 	Doncaster 	X 	X 	X 	NaN
247 	1 	Lincoln Showground 	X 	X 	X 	NaN
250 	13 	Eastbourne 	X 	X 	X 	NaN
251 	14 	Eastbourne 	X 	X 	X 	NaN
258 	15 	Eastbourne 	X 	X 	X 	NaN
260 	15 	Barnack 	X 	X 	X 	NaN
271 	21 	Shepway 	X 	X 	X 	NaN
274 	22 	Falaise 	X 	X 	NaN 	NaN
277 	27 	Dartmouth 	X 	X 	X 	NaN
278 	28 	Henstridge 	X 	X 	X 	NaN
279 	28 	Shoreham 	X 	X 	X 	NaN
280 	28 	Kent Showground 	X 	X 	X 	NaN
281 	29 	Shoreham 	X 	X 	X 	NaN
285 	29 	Little Gransden 	X 	X 	X 	NaN
289 	30 	Aylsham 	X 	X 	X 	NaN
291 	30 	Grimsthorpe 	X 	NaN 	X 	NaN
303 	4 	Duxford 	X 	X 	X 	NaN
304 	4 	Goodwood 	X 	X 	X 	NaN
305 	4 	Fareham 	X 	X 	X 	NaN
306 	5 	Goodwood 	X 	X 	X 	NaN
308 	5 	Duxford 	X 	X 	X 	NaN
315 	9 	Guernsey 	X 	X 	X 	X
316 	9 	Jersey 	X 	X 	X 	X
319 	11 	Southport 	X 	X 	X 	NaN
320 	11 	Morecambe 	X 	X 	X 	NaN
321 	11 	Leuchars 	X 	X 	X 	NaN
325 	12 	Morecambe 	X 	X 	X 	NaN
326 	12 	Southport 	X 	X 	X 	NaN
350 	18 	Yeovilton 	X 	X 	X 	NaN
351 	18 	Little Casterton 	X 	X 	X 	NaN
364 	25 	Lichfield 	X 	X 	NaN 	NaN

If you type, and go to the Url, you will see where the Month Is displayed. What should I type, so that all the full Dates are shown, in the Date Column ?

For example for the Venue Ripley the date would show as, 02/05/2004

For Blackpool it would show as 16/05/2004

And that pattern, supplied for every Row, in the Output DataFrame ?

Sorry in the Output I posted, the layout is incorrect. If you run the Code, in Jupyter Notebook, you will see,
what I was trying to post.

Any help would be appreciated

Regards

Eddie Winch