replace text in a txt

**deanhystad** · Oct-13-2023, 01:27 PM

Quote:but for now i have to insert data from txt to the database so something like:
INSERT INTO sbostats (partita,p1,pX,p2,p1x,px2,p12) VALUES ( "Verona - Napoli", "10.42%", "27.08%","62.5%", "37.5%", "89.58%", "72.92%")
val =

Are you sure? Do you want to put a string, "10.42%", in a database or do you want to put a number like 0.1042 or 10.42? Are you going to do with the percentage?

Do you want to use "Verona - Napoli", or do you want to create a table for Verona and add percentages for when they play Napoli? Or do you want partita to be two columns: HOME and VISITOR?

In this code I parse the file and load the values into a dataframe. Pandas supports writing dataframes to sql.

https://pandas.pydata.org/docs/reference...o_sql.html

        
          
          
              
              from itertools import islice
import pandas as pd
 
 
names = {
    "Verona": "Hellas Verona",
    "Sassuolu": "Sassuolu Calcio",
    "Monza": "AC Monza",
}
 
 
def batched(iterable, n):
    "Batch data into tuples of length n. The last batch may be shorter."
    # batched('ABCDEFG', 3) --> ABC DEF G
    if n < 1:
        raise ValueError("n must be at least one")
    it = iter(iterable)
    while batch := tuple(islice(it, n)):
        yield batch
 
 
def percentage(text):
    return float(text.strip().split()[0].replace("%", ""))/100
 
 
def load_file(filename):
    with open(filename, "r") as file:
        for lines in batched(file, 7):
            home, visitor = lines[0].strip().split(" - ")
            yield {
                "HOME": names.get(home, home),
                "VISITOR": names.get(visitor, visitor),
                "P1": percentage(lines[1]),
                "PX": percentage(lines[2]),
                "P2": percentage(lines[3]),
                "P1X": percentage(lines[4]),
                "PX2": percentage(lines[5]),
                "P12": percentage(lines[6]),
            }
 
 
df = pd.DataFrame(load_file("test.txt"))
print(df)

            

        
      

Output:             HOME        VISITOR      P1      PX      P2     P1X     PX2     P12
0   Hellas Verona         Napoli  0.1042  0.2708  0.6250  0.3750  0.8958  0.7292
1          Torino          Inter  0.1538  0.2308  0.6154  0.3846  0.8462  0.7692
2        Sassuolo          Lazio  0.2407  0.2963  0.4630  0.5370  0.7593  0.7037
3         AS Roma       AC Monza  0.5758  0.3333  0.0909  0.9091  0.4242  0.6667
4         Bologna      Frosinone  0.5238  0.2937  0.1825  0.8175  0.4762  0.7063
5     Salernitana       Cagliari  0.3778  0.2889  0.3333  0.6667  0.6222  0.7111
6        Atalanta          Genoa  0.7230  0.1757  0.1014  0.8986  0.2770  0.8243
7        AC Milan       Juventus  0.4335  0.3064  0.2601  0.7399  0.5665  0.6936
8         Udinese          Lecce  0.4410  0.3275  0.2314  0.7686  0.5590  0.6725
9      Fiorentina         Empoli  0.6859  0.1987  0.1154  0.8846  0.3141  0.8013
10          Genoa    Salernitana  0.5455  0.2597  0.1948  0.8052  0.4545  0.7403
11       Sassuolo        Bologna  0.4539  0.2695  0.2766  0.7234  0.5461  0.7305
12          Lecce         Torino  0.3396  0.2642  0.3962  0.6038  0.6604  0.7358
13       Juventus  Hellas Verona  0.6794  0.2214  0.0992  0.9008  0.3206  0.7786
14       Cagliari      Frosinone  0.2857  0.2381  0.4762  0.5238  0.7143  0.7619
15       AC Monza        Udinese  0.3704  0.3580  0.2716  0.7284  0.6296  0.6420
16          Inter        AS Roma  0.5510  0.2653  0.1837  0.8163  0.4490  0.7347
17         Napoli       AC Milan  0.3857  0.4000  0.2143  0.7857  0.6143  0.6000
18         Empoli       Atalanta  0.2500  0.3750  0.3750  0.6250  0.7500  0.6250
19          Lazio     Fiorentina  0.4100  0.2650  0.3250  0.6750  0.5900  0.7350
20  Hellas Verona         Napoli  0.1042  0.2708  0.6250  0.3750  0.8958  0.7292
21         Torino          Inter  0.1538  0.2308  0.6154  0.3846  0.8462  0.7692
22       Sassuolo          Lazio  0.2407  0.2963  0.4630  0.5370  0.7593  0.7037
23        AS Roma       AC Monza  0.5758  0.3333  0.0909  0.9091  0.4242  0.6667
24        Bologna      Frosinone  0.5238  0.2937  0.1825  0.8175  0.4762  0.7063
25    Salernitana       Cagliari  0.3778  0.2889  0.3333  0.6667  0.6222  0.7111
26       Atalanta          Genoa  0.7230  0.1757  0.1014  0.8986  0.2770  0.8243
27       AC Milan       Juventus  0.4335  0.3064  0.2601  0.7399  0.5665  0.6936
28        Udinese          Lecce  0.4410  0.3275  0.2314  0.7686  0.5590  0.6725
29     Fiorentina         Empoli  0.6859  0.1987  0.1154  0.8846  0.3141  0.8013
30          Genoa    Salernitana  0.5455  0.2597  0.1948  0.8052  0.4545  0.7403
31       Sassuolo        Bologna  0.4539  0.2695  0.2766  0.7234  0.5461  0.7305
32          Lecce         Torino  0.3396  0.2642  0.3962  0.6038  0.6604  0.7358
33       Juventus  Hellas Verona  0.6794  0.2214  0.0992  0.9008  0.3206  0.7786
34       Cagliari      Frosinone  0.2857  0.2381  0.4762  0.5238  0.7143  0.7619
35       AC Monza        Udinese  0.3704  0.3580  0.2716  0.7284  0.6296  0.6420
36          Inter        AS Roma  0.5510  0.2653  0.1837  0.8163  0.4490  0.7347
37         Napoli       AC Milan  0.3857  0.4000  0.2143  0.7857  0.6143  0.6000
38         Empoli       Atalanta  0.2500  0.3750  0.3750  0.6250  0.7500  0.6250
39          Lazio     Fiorentina  0.4100  0.2650  0.3250  0.6750  0.5900  0.7350

If you don't want to use pandas, you can easily write a loop to put entries in a database.

        
              sql = "INSERT INTO sbostats (home, visitor, p1, pX, p2, p1x, px2, p12) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)"
for game in load_file("test.txt"):
    mycursor.execute(sql, tuple(game.values()))

cartonics · (This post was last modified: Oct-13-2023, 02:29 PM by cartonics.)

i need the value it can be 10.42 but also 0.1042!

i need Verona - Napoli (with - also) so i can do an update query and put them directly in calcio table
but its good to have also home and away team in other to columns to do other joins on other tables...

so with pandas i can also build the query and send to my db?

something like:
UPDATE calcio (home,visitors,p1,pX,p2,p1x,px2,p12) VALUES (" Verona","Napoli" ,"10.42 ","27.08","62.50 " ,"37.50","89.58","72.92" ) where partita ="Verona - Napoli"

and so on...

        
              sql = "INSERT INTO sbostats (home, visitor, p1, pX, p2, p1x, px2, p12) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)"
for game in load_file("test.txt"):
    mycursor.execute(sql, tuple(game.values()))

Error: for game in load_file("test.txt"):
NameError: name 'load_file' is not defined

**deanhystad** · Oct-13-2023, 06:35 PM

See previous post.

cartonics · (This post was last modified: Oct-13-2023, 08:01 PM by cartonics.)

(Oct-13-2023, 06:35 PM)deanhystad Wrote: See previous post.

        
          
          
              
              from itertools import islice
import pandas as pd
import mysql.connector
from mysql.connector import errorcode
 
mydb = mysql.connector.connect(
  host="localhost",
  user="root",
  database="my_cataviz"
)
 
mycursor = mydb.cursor()
  
names = {
    "Verona": "Hellas Verona",
    "Sassuolu": "Sassuolu Calcio",
    "Monza": "AC Monza",
}
  
  
def batched(iterable, n):
    "Batch data into tuples of length n. The last batch may be shorter."
    # batched('ABCDEFG', 3) --> ABC DEF G
    if n < 1:
        raise ValueError("n must be at least one")
    it = iter(iterable)
    while batch := tuple(islice(it, n)):
        yield batch
  
  
def percentage(text):
    return float(text.strip().split()[0].replace("%", ""))
  
  
def load_file(filename):
    with open(filename, "r") as file:
        for lines in batched(file, 7):
            home, visitor = lines[0].strip().split(" - ")
            yield {
                "PARTITA": names.get(lines[0]),
                "HOME": names.get(home, home),
                "VISITOR": names.get(visitor, visitor),
                "P1": percentage(lines[1]),
                "PX": percentage(lines[2]),
                "P2": percentage(lines[3]),
                "P1X": percentage(lines[4]),
                "PX2": percentage(lines[5]),
                "P12": percentage(lines[6]),
            }
  
  
df = pd.DataFrame(load_file("statistics.txt"))
#print(df)
 
 
mycursor = mydb.cursor()
 
sql = "INSERT INTO sbostats (partita,casa, fcasa, p1, px, p2, p1x, px2, p12) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)"
for game in load_file("statistics.txt"):
     
    mycursor.execute(sql, tuple(game.values()))
     
#mycursor.execute(sql, val)
 
mydb.commit()
 
print(mycursor.rowcount, "records inserted.")

            

        
      

THANKS I tryed to add also the "partita" ... non errors but the column of database is empty. While other columns are fine!

**deanhystad** · Oct-13-2023, 08:42 PM

lines[0] will be something like "Verona - Napoli". There is no "Verona - Napoli" entry in the names dictionary, and there should never be.

There is code to get the home and visitor teams, and how to update the team names using the names dictionary. Maybe you can use that to make a partitia column.

cartonics · (This post was last modified: Oct-14-2023, 06:33 AM by cartonics.)

(Oct-13-2023, 08:42 PM)deanhystad Wrote: lines[0] will be something like "Verona - Napoli". There is no "Verona - Napoli" entry in the names dictionary, and there should never be.

There is code to get the home and visitor teams, and how to update the team names using the names dictionary. Maybe you can use that to make a partitia column.

i solved via mysql doing a concat of 2 columns

nicoali · (This post was last modified: Dec-15-2023, 06:28 PM by nicoali.)

Quote:I don't see any value in putting this information in a database. A database needs to be carefully designed to be useful. If you could put this information into a database, what kind of queries would you want to perform? Use the queries to define what tables and columns you'll need in your database, then write the code to load the file into database tables. Don't create the tables first then try to figure out how to write a query.

is it possible to do searchs on the txt that has this structure?

        
          
          
              
              Genoa - Juventus
26% (3.85)  1.11%   1
18% (5.56)  -1.11%   X
56% (1.79)  -1.11%   2
44% (2.27)  1.11%   1X
74% (1.35)  -1.11%   X2
82% (1.22)  1.11%   12
60% (1.67)  -11.11%   GOL
40% (2.50)  11.11%   NO GOL
96% (1.04)  -1%   OVER 0.5
82% (1.22)  -1.11%   OVER 1.5
58% (1.72)  1.11%   OVER 2.5
28% (3.57)  -1.11%   OVER 3.5
18% (5.56)  -1.11%   OVER 4.5
4% (25.00)  1%   UNDER 0.5
18% (5.56)  1.11%   UNDER 1.5
42% (2.38)  -1.11%   UNDER 2.5
72% (1.39)  1.11%   UNDER 3.5
82% (1.22)  1.11%   UNDER 4.5
66% (1.52)  -1.11%   SEGNA CASA
34% (2.94)  1.11%   SEGNA CASA OVER 1.5
12% (8.33)  -1.11%   SEGNA CASA OVER 2.5
66% (1.52)  -1.11%   SEGNA CASA UNDER 1.5
88% (1.14)  1.11%   SEGNA CASA UNDER 2.5
90% (1.11)  -11%   SEGNA OSPITE
50% (2.00)  -1.11%   SEGNA OSPITE OVER 1.5
24% (4.17)  1.11%   SEGNA OSPITE OVER 2.5
50% (2.00)  1.11%   SEGNA OSPITE UNDER 1.5
76% (1.32)  -1.11%   SEGNA OSPITE UNDER 2.5
24% (4.17)  -11.11%   1 PRIMO TEMPO
32% (3.13)  11.11%   X PRIMO TEMPO
44% (2.27)  -1.11%   2 PRIMO TEMPO
56% (1.79)  1.11%   1X PRIMO TEMPO
76% (1.32)  11.11%   X2 PRIMO TEMPO
68% (1.47)  -11.11%   12 PRIMO TEMPO
78% (1.28)  -11.11%   OVER 0.5 PRIMO TEMPO
36% (2.78)  -1.11%   OVER 1.5 PRIMO TEMPO
10% (10.00)  1.11%   OVER 2.5 PRIMO TEMPO
22% (4.55)  11.11%   UNDER 0.5 PRIMO TEMPO
64% (1.56)  1.11%   UNDER 1.5 PRIMO TEMPO
90% (1.11)  -1.11%   UNDER 2.5 PRIMO TEMPO
16% (6.25)  -1.11%   GOL PRIMO TEMPO
84% (1.19)  1.11%   NO GOL PRIMO TEMPO
18% (5.56)  -1.11%   1 SECONDO TEMPO
40% (2.50)  11%   X SECONDO TEMPO
42% (2.38)  -11.11%   2 SECONDO TEMPO
58% (1.72)  11.11%   1X SECONDO TEMPO
82% (1.22)  1.11%   X2 SECOND HALF
60% (1.67)  -11%   12 SECONDO TEMPO
78% (1.28)  -11%   OVER 0.5 SECONDO TEMPO
52% (1.92)  1.11%   OVER 1.5 SECONDO TEMPO
30% (3.33)  -1.11%   OVER 2.5 SECONDO TEMPO
22% (4.55)  11%   UNDER 0.5 SECONDO TEMPO
48% (2.08)  -1.11%   UNDER 1.5 SECONDO TEMPO
70% (1.43)  1.11%   UNDER 2.5 SECONDO TEMPO
40% (2.50)  1.11%   GOL SECONDO TEMPO
60% (1.67)  -1.11%   NO GOL SECONDO TEMPO
56% (1.79)  1.11%   DISPARI
44% (2.27)  -1.11%   PARI
54% (1.85)  -1.11%   MULTIGOL CASA 1-2
62% (1.61)  -1.11%   MULTIGOL CASA 1-3
30% (3.33)  11.11%   MULTIGOL CASA 2-3
66% (1.52)  -11.11%   MULTIGOL OSPITE 1-2
80% (1.25)  -1.11%   MULTIGOL OSPITE 1-3
40% (2.50)  1.11%   MULTIGOL OSPITE 2-3
38% (2.63)  -1.11%   MULTIGOL 1-2
68% (1.47)  1.11%   MULTIGOL 1-3
78% (1.28)  1.11%   MULTIGOL 1-4
86% (1.16)  -1.11%   MULTIGOL 1-5
92% (1.09)  1.11%   MULTIGOL 1-6
54% (1.85)  -1.11%   MULTIGOL 2-3
64% (1.56)  1.11%   MULTIGOL 2-4
72% (1.39)  -1.11%   MULTIGOL 2-5
78% (1.28)  -1.11%   MULTIGOL 2-6
40% (2.50)  11.11%   MULTIGOL 3-4
48% (2.08)  1.11%   MULTIGOL 3-5
54% (1.85)  1.11%   MULTIGOL 3-6
18% (5.56)  -1.11%   MULTIGOL 4-5
24% (4.17)  -1.11%   MULTIGOL 4-6
14% (7.14)  -1.11%   MULTIGOL 5-6
4% (25.00)  -1.11%   MULTIGOL 7-+
26% (3.85)  1.11%   1 + OVER 1.5
14% (7.14)  -1.11%   X + OVER 1.5
42% (2.38)  -11.11%   2 + OVER 1.5
20% (5.00)  1.11%   1 + OVER 2.5
4% (25.00)  1%   X + OVER 2.5
34% (2.94)  -1.11%   2 + OVER 2.5
10% (10.00)  -1.11%   1 + OVER 3.5
4% (25.00)  1%   X + OVER 3.5
14% (7.14)  -1.11%   2 + OVER 3.5
6% (16.67)  -1.11%   1 + OVER 4.5
2% (50.00)  1%   X + OVER 4.5
10% (10.00)  -1.11%   2 + OVER 4.5
40% (2.50)  1.11%   1X + OVER 1.5
56% (1.79)  -11.11%   X2 + OVER 1.5
68% (1.47)  -1.11%   12 + OVER 1.5
24% (4.17)  1.11%   1X + OVER 2.5
38% (2.63)  1.11%   X2 + OVER 2.5
54% (1.85)  -1.11%   12 + OVER 2.5
14% (7.14)  -1.11%   1X + OVER 3.5
18% (5.56)  -1.11%   X2 + OVER 3.5
24% (4.17)  -11.11%   12 + OVER 3.5
8% (12.50)  -1.11%   1X + OVER 4.5
12% (8.33)  -1.11%   X2 + OVER 4.5
16% (6.25)  -11.11%   12 + OVER 4.5
0% (NA)  1%   1 + UNDER 1.5
4% (25.00)  1%   X + UNDER 1.5
14% (7.14)  1.11%   2 + UNDER 1.5
6% (16.67)  1%   1 + UNDER 2.5
14% (7.14)  -1.11%   X + UNDER 2.5
22% (4.55)  -1.11%   2 + UNDER 2.5
16% (6.25)  11%   1 + UNDER 3.5
14% (7.14)  -1.11%   X + UNDER 3.5
42% (2.38)  -1.11%   2 + UNDER 3.5
20% (5.00)  11.11%   1 + UNDER 4.5
16% (6.25)  -1.11%   X + UNDER 4.5
46% (2.17)  1.11%   2 + UNDER 4.5
4% (25.00)  1%   1X + UNDER 1.5
18% (5.56)  1.11%   X2 + UNDER 1.5
14% (7.14)  1.11%   12 + UNDER 1.5
20% (5.00)  1.11%   1X + UNDER 2.5
36% (2.78)  -1.11%   X2 + UNDER 2.5
28% (3.57)  1.11%   12 + UNDER 2.5
30% (3.33)  11.11%   1X + UNDER 3.5
56% (1.79)  -1.11%   X2 + UNDER 3.5
58% (1.72)  11.11%   12 + UNDER 3.5
36% (2.78)  1.11%   1X + UNDER 4.5
62% (1.61)  -1.11%   X2 + UNDER 4.5
66% (1.52)  11.11%   12 + UNDER 4.5
20% (5.00)  1.11%   1 + GOL
14% (7.14)  -1.11%   X + GOL
26% (3.85)  -11.11%   2 + GOL
34% (2.94)  -1.11%   1X + GOL
40% (2.50)  -11.11%   X2 + GOL
46% (2.17)  -1.11%   12 + GOL
6% (16.67)  1%   1 + NO GOL
4% (25.00)  1%   X + NO GOL
30% (3.33)  1.11%   2 + NO GOL
10% (10.00)  11%   1X + NO GOL
34% (2.94)  1.11%   X2 + NO GOL
36% (2.78)  1.11%   12 + NO GOL

            

        
      

for example find all the % > 65 and odd the value in () > of 1,2 and put in output match and type ?

nicoali · Dec-16-2023, 04:07 PM

        
              import re
 
def is_condition_met(line):
    # Utilizza un'espressione regolare per estrarre la percentuale e il valore tra parentesi
    match = re.search(r'(\d+\.\d+%)\s+\((\d+\.\d+)\)', line)
     
    if match:
        percentuale = float(match.group(1)[:-1])  # Rimuove il simbolo % e converte in float
        valore = float(match.group(2))
         
        # Verifica le condizioni richieste
        if percentuale > 75 and valore > 1.2:
            return True
 
    return False
 
with open('statistics.txt', 'r') as file:
    lines = file.readlines()
 
    for line in lines:
        if is_condition_met(line):
            print(line.strip())  # Stampa la linea senza spazi iniziali/finali

can be done something in python to have a "window" to choice which field to choice then do the search?

logicielfr · Jan-23-2024, 09:33 PM

thanks for all reply

Athi · Jan-30-2024, 06:58 AM

Great discussion!
I've gained a lot of insights from this. Thank you everyone.

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