Hi there I have the Following Code, which I run in Jupyter Notebook :-
<pre>import pandas as pd
import requests
from bs4 import BeautifulSoup
#res = requests.get("http://web.archive.org/web/20011108193342/http://www.raf.mod.uk/bbmf/calendar.html")
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]
##################
##################
##################
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
#make df[0] to list
list=[]
for i in df[0]:
list.append(i)
#reverse the list to make split to sublist easier
list.reverse()
#split list to sublist using condition len(val)> 2
size = len(list)
idx_list = [idx + 1 for idx, val in
enumerate(list) if len(val) > 2]
res = [list[i: j] for i, j in
zip([0] + idx_list, idx_list +
([size] if idx_list[-1] != size else []))]
#make monthname to numbers and print
for i in res:
for j in range(len(i)):
if i[j].upper()=='JUNE':
i[j]='6'
elif i[j].upper() =='MAY':
i[j]='5'
elif i[j].upper() == 'APRIL':
i[j]='4'
elif i[j].upper() =='JANUARY':
i[j]='1'
elif i[j].upper() == 'FEBRUARY':
i[j]='2'
elif i[j].upper() =='MARCH':
i[j]='3'
elif i[j].upper() == 'JULY':
i[j]='7'
elif i[j].upper() =='AUGUST':
i[j]='8'
elif i[j].upper() == 'SEPTEMBER':
i[j]='9'
elif i[j].upper() =='OCTOBER':
i[j]='10'
elif i[j].upper() == 'NOVEMBER':
i[j]='11'
elif i[j].upper() =='DECEMBER':
i[j]='12'
#append string and append to new list
finallist=[]
for i in res:
for j in range(len(i)):
if j < len(i) - 1:
#print(f'2004-{i[-1]}-{i[j]}')
finallist.append(f'2004-{i[-1]}-{i[j]}')
#print(finallist)
finallist.reverse()
#print("\n=== ORIGINAL DF ===\n")
#print(df)
#convert dataframe to list
listtemp1=df.values.tolist()
#replace found below values with 0000_removable
removelist=['LOCATION','LANCASTER','SPITFIRE','HURRICANE','DAKOTA','DATE','JUNE','JANUARY','FEBRUARY','MARCH','MAY','JULY','AUGUST','SEPTEMBER','OCTOBER','NOVEMBER','DECEMBER','APRIL']
for i in listtemp1:
for j in range(len(i)):
for place in removelist:
if str(i[j]).upper()==place:
i[j]='0000_removable'
else:
pass
#remove sublists with the replaced values we redirected
dellist=['0000_removable', '0000_removable', '0000_removable', '0000_removable', '0000_removable', '0000_removable']
res = [i for i in listtemp1 if i != dellist]
#assign back to dataframe DF3
df3=pd.DataFrame()
df3=pd.DataFrame(res, columns=['Date','LOCATION','LANCASTER','SPITFIRE','HURRICANE','DAKOTA'])
#print("\n=== AFTER REMOVE month and column names from DF, assigned to new as DF3 ===\n")
#print(df3)
#now assign that sorted date list to dataframe DF3
idx = 0
df3.insert(loc=idx, column='DATE', value=finallist)
pd.options.display.max_rows = 500
#print("\n=== FINAL DF3 after joining the edited date format column list ===\n")
#print(df3)
#validation logic if needed compare processed date from new joined "edited_Date_format" column with already existing "Date" column
#df3['ED1']= pd.to_datetime(df3['EDITED_DATE_FORMAT'],format='%Y-%m-%d').dt.day
#df3['validation of date'] = df3.apply(lambda x: str(x['ED1']) == x['Date'], axis=1)
#convert df3['EDITED_DATE_FORMAT'] column from object to datetime64 foramt
#df3['EDITED_DATE_FORMAT']= pd.to_datetime(df3['EDITED_DATE_FORMAT'],format='%Y-%m-%d')
##################
##################
##################
#df3 = df3.rename(columns=df.iloc[0])
#df3 = df.iloc[2:]
#df3.head(15)
pd.options.display.max_rows = 1000
pd.options.display.max_columns = 1000
df3['LANCASTER'] = df3['LANCASTER'].replace({'X': 'L'})
df3['HURRICANE'] = df3['HURRICANE'].replace({'X': 'H'})
df3['SPITFIRE'] = df3['SPITFIRE'].replace({'X': 'S'})
df3['SPITFIRE'] = df3['SPITFIRE'].replace({'X x 2': 'SS'})
df3['DAKOTA'] = df3['DAKOTA'].replace({'X': 'D'})
#display = df3[(df3['LOCATION'].str.contains('[a-zA-Z]')) & (df3['DAKOTA'].str.contains('X')) & (df3['SPITFIRE'].str.contains('X', na=True)) & (df3['LANCASTER'] != 'X')]
#display = df3[(df3['LOCATION'].str.contains('[a-zA-Z]')) & (df3['DAKOTA'].str.contains('D')) & (df3['SPITFIRE'].str.contains('S', na=True)) & (df3['LANCASTER'] != 'L')]
display = df3[(df3['LOCATION'].str.contains('[a-zA-Z]')) & (df3['DATE'].str.contains('-10$|15$')) & (df3['DAKOTA'].str.contains('D')) & (df3['SPITFIRE'].str.contains('S', na=True)) & (df3['LANCASTER'] != 'L')]
#Months = May Jun Jul Aug Sep
#Months = -5- -6- -7- -8- -9-
#print(display)
display['DATE']= pd.to_datetime(display['DATE'],format='%Y-%m-%d')
display['DATE']= pd.to_datetime(display['DATE']).dt.strftime('%m-%d-%Y')
##added two lines above to convert date format
display=display.rename_axis('MyIdx')
display=display.sort_values(by=['DATE','MyIdx'])
display['DATE']= pd.to_datetime(display['DATE']).dt.strftime('%d-%b-%Y')
#display.drop_duplicates(subset=['LOCATION', 'DATE'], keep='last', inplace=True)
#display.drop('LANCASTER', axis=1, inplace=True)
#display.drop('Date', axis=1, inplace=True)
display=display.dropna(subset=['SPITFIRE', 'HURRICANE'], how='all')
display=display[['LOCATION','DATE','DAKOTA','HURRICANE','SPITFIRE']]
display=display.fillna('--')
#display.reset_index(drop=True, inplace=True)
display.to_csv(r'C:\Users\Edward\Desktop\BBMF Schedules And Master Forum Thread Texts\BBMF-2004-Code (Dakota With Fighters).csv')
display
display.sort_values(by=['DATE'])
#print(display)
And when I include that last line of Code, it correctly outputs the earliest days in the DataFrame Output first, i.e. 10 before 15, but not in the month order I want :-
LOCATION DATE DAKOTA HURRICANE SPITFIRE
MyIdx
176 Duxford 10-Jul-2004 D H S
177 Cirencester 10-Jul-2004 D H S
178 Brize Norton 10-Jul-2004 D H S
74 Shrivenham 20:00 10-Jun-2004 D H S
257 Campbletown 15-Aug-2004 D -- S
258 Sunderland 15-Aug-2004 D -- S
261 Scampton 15-Aug-2004 D -- S
200 RIAT Fairford 15-Jul-2004 D -- SS
22 Tilford 15-May-2004 D -- S
23 Abingdon 15-May-2004 D -- S
24 Hyde Heath Village 15-May-2004 D -- S
I want 10th June 2004 first then the 10th of July/s then the 15th of May's then the 15th of July Rows, then the 15th August Rows. How do I modify that line of Code, so that I can filter to get that order, without changing the index position of the Rows via code, which I know how to do ?
I mean add something to the first line of code e.g. the displays.sort_values Line, so that the Earlier month with a day, is shown 'favoured' before the later month with the same day ? i.e. 10-Jun-2004 is shown before 10-Jul-2004 , 15-May-2004 is shown before 15-Jul-2004 Rows etc. But still dates with day 10, showing before day 15 Rows.
(df3['DATE'].str.contains('-10$|15$'))
Was the part of the Code, I use to filter the days of the month, I want included
in the DataFrame Output.
Any help would be much appreciated.
Eddie Winch
What I have tried:
I run the Code as shown above, and get the stated Output.
Submit an article or tip