Split and organize my Pandas Dataframe

[brunolelli]

Hello guys,

I'm using the following code in order to collect the cancelled flights from some airlines:

import pandas as pd

url_ini = 'https://flightaware.com/live/fleet/'
url_fim = '/cancelled'
    
ncia = ['SKU', 'TAM']

for i in range(len(ncia)):
    url = url_ini + str(ncia[i]) + url_fim
    df = pd.read_html(url)[3]
        
    df.to_csv(r'C:\Users\bruno\Desktop\Teste.txt', sep=';',  mode='a', header=None, encoding = 'utf-8', index=False)
    print('Atualizado apra a ' + str(ncia[i]) )

And the output is something like this:

Output:
SKU326;A320;Int'l Comodoro Arturo Merino Benítez (SCL / SCEL);Int'l Diego Aracena (IQQ / SCDA);Sex 17:43 -04;;;
SKU250;A320;Int'l Comodoro Arturo Merino Benítez (SCL / SCEL);Int'l El Loa (CJC / SCCF);Sex 18:29 -04;;;
SKU801;A320;Jorge Chávez Int'l (LIM / SPJC);Int'l Comodoro Arturo Merino Benítez (SCL / SCEL);Sex 17:44 -05;;;
SKU329;A320;Int'l Diego Aracena (IQQ / SCDA);Int'l Comodoro Arturo Merino Benítez (SCL / SCEL);Sex 20:47 -04;;;
SKU253;A320;Int'l El Loa (CJC / SCCF);Int'l Comodoro Arturo Merino Benítez (SCL / SCEL);Sex 21:11 -04;;;
SKU433;A320;Int'l Comodoro Arturo Merino Benítez (SCL / SCEL);El Tepual Int'l (PMC / SCTE);Sáb 10:55 -04;;;

But I would like to better organize my data and split the content from column 2 and 3, ("Int'l Comodoro Arturo Merino Benítez (SCL / SCEL)" and "Int'l Diego Aracena (IQQ / SCDA)") to just SCL IQQ. So basically I just need the airport code, I don't need the airport name.

My best output would be something like this:

Output:
SKU326;A320;SCL;IQQ;Sex 17:43 -04;;;
SKU250;A320;SCL;CJC;Sex 18:29 -04;;;
SKU801;A320;LIM;SCL;Sex 17:44 -05;;;
SKU329;A320;IQQ);SCL;Sex 20:47 -04;;;
SKU253;A320;CJC;SCL;Sex 21:11 -04;;;
SKU433;A320;SCL;PMC;Sáb 10:55 -04;;;

How can I do that?

Thank you guys.

[klllmmm]

Hope this helps

import pandas as pd
 
url_ini = 'https://flightaware.com/live/fleet/'
url_fim = '/cancelled'
     
ncia = ['SKU', 'TAM']
 
for i in range(len(ncia)):
    url = url_ini + str(ncia[i]) + url_fim
    df = pd.read_html(url)[3]
    df.columns = df.columns.droplevel(0)
    df['Origin'] = df['Origin'].str.split('(').str[1].str[:3] 
    df['Destination'] = df['Destination'].str.split('(').str[1].str[:3]
         
    df.to_csv(r'C:\Users\Kelum desktop PC\Desktop\Teste.txt', sep=';',  mode='a',  encoding = 'utf-8', index=False)
    print('Atualizado apra a ' + str(ncia[i]) )

Output:
Ident;Type;Origin;Destination;ScheduledDeparture Time;Unnamed: 5_level_1;Unnamed: 6_level_1;Unnamed: 7_level_1
SKU329;A320;IQQ;SCL;Fri 08:47PM -04;;;
SKU253;A320;CJC;SCL;Fri 09:11PM -04;;;
SKU433;A320;SCL;PMC;Sat 10:55AM -04;;;
SKU433;A320;PMC;BBA;Sat 01:42PM -04;;;
SKU434;A320;BBA;PMC;Sat 03:40PM -04;;;
SKU434;A320;PMC;SCL;Sat 05:19PM -04;;;
SKU121;A320;SCL;ZAL;Sun 11:46AM -04;;;
SKU122;A320;ZAL;SCL;Sun 02:14PM -04;;;
SKU101;A320;SCL;ZOS;Sun 02:57PM -04;;;
SKU147;A320;SCL;ZCO;Sun 04:35PM -04;;;
SKU102;A320;ZOS;SCL;Sun 05:14PM -04;;;
SKU304;A320;SCL;ARI;Sun 05:30PM -04;;;
SKU148;A320;ZCO;SCL;Sun 06:30PM -04;;;
SKU326;A320;SCL;IQQ;Sun 06:47PM -04;;;
SKU163;A320;SCL;CCP;Sun 07:27PM -04;;;
Ident;Type;Origin;Destination;ScheduledDeparture Time;Unnamed: 5_level_1;Unnamed: 6_level_1;Unnamed: 7_level_1
TAM3595;A319;SDU;CGH;Sat 07:25AM -03;;;
TAM8146;B763;GRU;LIS;Sat 04:50PM -03;;;
TAM3585;A319;CGH;SDU;Sat 05:40PM -03;;;

[brunolelli]

Hello!
Thanks per your kindly response...
It's working now!

Would you mind helping me with another issue?
As you can see, the last column shows the "date vale", like "Fri 20:47 -04".

I'm not sure if it's formated as text or date, so, how can I convert this "Fri 20:47 -04" to something like this "16/04/21 20:47"

Is it possible to be done?

Thanks

[klllmmm]

Date can not be determine certainty as date is not shown.

Ex. below entries are belong to March month and it could be any Saturday within March month.

TAM3595;A319;SDU;CGH;Sat 07:25AM -03;;;
TAM8146;B763;GRU;LIS;Sat 04:50PM -03;;;
TAM3585;A319;CGH;SDU;Sat 05:40PM -03;;;

[brunolelli]

Date can not be determine certainty as date is not shown.

Ex. below entries are belong to March month and it could be any Saturday within March month.

TAM3595;A319;SDU;CGH;Sat 07:25AM -03;;;
TAM8146;B763;GRU;LIS;Sat 04:50PM -03;;;
TAM3585;A319;CGH;SDU;Sat 05:40PM -03;;;

Thank you!