PB de création d'exécutable avec pyinstaller à cause de dépendances vers mathplotlib + cacher le code ds PDF

**Curveater** · 04/09/2020, 12h00

Bonjour

j'ai problème avec pyinstaller le créateur d'éxécutable à partir de code python à cause de dépendances vers mathplotlib
Lorsque je lance la commande: pyinstaller --onefile Wallet12latest.py l'éxécutable Wallet12latest.exe est généré sauf que lorsque je le lance il crash , en donnant le bug suivant, tout fonctionne bien dans Jupyter notebook:

Je souhaiterais aussi savoir si c'est possible comment cacher le code lorsqu'on convertit le jupyter notebook en PDF avec jupyter-nbconvert --to PDFviaHTML Wallet12latest.ipynb
Nom : bug.png
Affichages : 113
Taille : 66,2 Ko

Nom : bug.png
Affichages : 113
Taille : 66,2 Ko

Le contenu de Wallet12latest.py est le suivant:

Code :

Sélectionner tout - Visualiser dans une fenêtre à part

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import os
import requests
import datetime
from datetime import date
from datetime import datetime
from bs4 import BeautifulSoup
import pandas as pd
import matplotlib.pyplot as plt
 
nbAppleStock = 189
nbMicrosoftStock = 100
AppleInit = 127.84
MicrosoftInit = 228.0599
EURUSDInit = 1.19517400 
InitialWalletInvested = (nbAppleStock * AppleInit + nbMicrosoftStock * MicrosoftInit)/EURUSDInit
fees = 0.0047 * InitialWalletInvested
feesApple = 0.0047 * AppleInit*nbAppleStock / EURUSDInit
feesMicrosoft = 0.0047 * MicrosoftInit*nbMicrosoftStock / EURUSDInit
fees = 0
feesApple = 0
feesMicrosoft = 0
 
url1='https://fr.finance.yahoo.com/quote/AAPL/history/'
url2='https://fr.finance.yahoo.com/quote/MSFT/history/'
url3='https://fr.finance.yahoo.com/quote/EURUSD=X/'
url4='https://finance.yahoo.com/quote/AAPL/history?p=AAPL'
url5='https://finance.yahoo.com/quote/MSFT/history?p=MSFT'
url6='https://finance.yahoo.com/quote/EURUSD%3DX/history?p=EURUSD%3DX'
url7='https://www.boursedirect.fr/fr/marche/nasdaq-ngs-global-select-market/apple-inc-AAPL-USD-XNGS/seance'
url8='https://www.boursedirect.fr/fr/marche/nasdaq-ngs-global-select-market/microsoft-corporation-MSFT-USD-XNGS/seance'
page1 = requests.get(url1)
page2 = requests.get(url2)
page3 = requests.get(url3)
page4 = requests.get(url4)
page5 = requests.get(url5)
page6 = requests.get(url6)
page7 = requests.get(url7)
page8 = requests.get(url8)
soup1 = BeautifulSoup(page1.text, 'html.parser')
soup2 = BeautifulSoup(page2.text, 'html.parser') 
soup3 = BeautifulSoup(page3.text, 'html.parser')
soup4 = BeautifulSoup(page4.text, 'html.parser')
soup5 = BeautifulSoup(page5.text, 'html.parser')
soup6 = BeautifulSoup(page6.text, 'html.parser')
soup7 = BeautifulSoup(page7.text, 'html.parser')
soup8 = BeautifulSoup(page8.text, 'html.parser')
#print(soup7.prettify())
 
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
print(now.strftime("%H"))
print("Current Time =", current_time)
 
if (int(now.strftime("%H"))<15) | (int(now.strftime("%H"))>22) :
    AppleStockPriceString = soup7.find('tbody',{'class':"bd-streaming-select-value-trades"}).findAll("td")[2:3][0]
    AppleStockPriceString = str(AppleStockPriceString)
    AppleStockPriceString=AppleStockPriceString.replace("<td>","").replace("</td>","")
    MicrosoftStockPriceString = soup8.find('tbody',{'class':"bd-streaming-select-value-trades"}).findAll("td")[2:3][0]
    MicrosoftStockPriceString = str(MicrosoftStockPriceString)
    MicrosoftStockPriceString=MicrosoftStockPriceString.replace("<td>","").replace("</td>","")
else :
    AppleStockPriceString = soup1.find('div',{'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}).find('span').text
    MicrosoftStockPriceString = soup2.find('div',{'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}).find('span').text
 
EURUSDString = soup3.find('div',{'class': 'My(6px) Pos(r) smartphone_Mt(6px)'}).find('span').text
AppleStockPriceString = AppleStockPriceString.replace(",",".")
MicrosoftStockPriceString = MicrosoftStockPriceString.replace(",",".")
EURUSDString = EURUSDString.replace(",",".")
AppleStockPrice = float(AppleStockPriceString)
MicrosoftStockPrice = float(MicrosoftStockPriceString)
EURUSD = float(EURUSDString)
print("EURUSD: " + EURUSDString)
print("AppleStockPrice: " + str(AppleStockPrice) + "$")
print("MicrosoftStockPrice: " + str(MicrosoftStockPrice) + "$")
EURUSDPerformance = 1/(1 + (EURUSD/EURUSDInit)-1) - 1
print("EURUSD Performance SI: " + str(round(EURUSDPerformance * 100,2)) + "%")
WalletPresentValue = (nbAppleStock * AppleStockPrice + nbMicrosoftStock * MicrosoftStockPrice)/EURUSD
print("Fees: " + str(fees) + "€")
Performance =(WalletPresentValue/(InitialWalletInvested + fees) - 1)
print("Overall Performance SI: " + str(round((Performance)*100,2)) + "%")
print("Apple Performance SI: " + str(round((((1 + AppleStockPrice/(AppleInit) - 1))*(1-feesApple/(nbAppleStock*AppleInit))*(1+EURUSDPerformance)-1)*100,2))+ "%")
PNLApple = round(nbAppleStock* AppleInit *((1 +(AppleStockPrice/AppleInit -1))*(1+ EURUSDPerformance))/EURUSDInit - nbAppleStock * AppleInit/EURUSDInit - feesApple, 2)
print("P&L on Apple: " + str(PNLApple) + "€")
print("Microsoft Performance SI: " + str(round((((1 + MicrosoftStockPrice/(MicrosoftInit) - 1))*(1-feesMicrosoft/(nbMicrosoftStock*MicrosoftInit))*(1+EURUSDPerformance)-1)*100,2)) + "%")
PNLMicrosoft = round(nbMicrosoftStock* MicrosoftInit *((1 +(MicrosoftStockPrice/MicrosoftInit -1))*(1+ EURUSDPerformance))/EURUSDInit - nbMicrosoftStock * MicrosoftInit/EURUSDInit - feesMicrosoft, 2)
print("P&L on Microsoft: " + str(PNLMicrosoft) + "€")
print("P&L: " + str(round(PNLApple + PNLMicrosoft,2)))
print("Invested Wallet Present Value in €: " + str(round(WalletPresentValue,2)))
Cash = 11637
print("Total Compte Titres in €: " + str(round(Cash + WalletPresentValue,2)))
 
table = soup4.find('table')
table_rows = table.find_all('tr')
res = []
for tr in table_rows:
    td = tr.find_all('td')
    row = [tr.text.strip() for tr in td if tr.text.strip()]
    if row:
        res.append(row)
df1 = pd.DataFrame(res, columns=["Date", "Ouverture", "Élevé ", "Faible ","Clôture*","Cours de clôture ajusté**","Volume"])
df1 = df1.drop(["Ouverture", "Élevé ", "Faible ","Cours de clôture ajusté**","Volume"], axis=1)
df1 = df1.drop_duplicates("Date", keep='first', inplace=False)
df1['Clôture*'] = df1['Clôture*'].str.replace(',','.')
df1["Clôture*"] = pd.to_numeric(df1["Clôture*"], downcast="float")
df1["Date"] = df1["Date"].astype(str)
df1["Date"].describe()
indexNames = df1[ df1['Date'].str.contains("split") ].index
df1.drop(indexNames , inplace=True)
df1['Date'] = df1['Date'].astype('datetime64[ns]')
df1.plot(figsize=(10,2), x ='Date', y='Clôture*', kind = 'line')
plt.title('Apple Stock Prices in $')
plt.xlabel('Dates')
plt.ylabel('Stock Price in $')
plt.show()
df1Wallet = df1[df1["Date"] >= '2020-09-01']
table = soup5.find('table')
table_rows = table.find_all('tr')
res = []
for tr in table_rows:
    td = tr.find_all('td')
    row = [tr.text.strip() for tr in td if tr.text.strip()]
    if row:
        res.append(row)
df2 = pd.DataFrame(res, columns=["Date", "Ouverture", "Élevé ", "Faible ","Clôture*","Cours de clôture ajusté**","Volume"])
df2 = df2.drop(["Ouverture", "Élevé ", "Faible ","Cours de clôture ajusté**","Volume"], axis=1)
df2 = df2.drop_duplicates("Date", keep='first', inplace=False)
df2['Clôture*'] = df2['Clôture*'].str.replace(',','.')
df2["Clôture*"] = pd.to_numeric(df2["Clôture*"], downcast="float")
df2["Date"] = df2["Date"].astype(str)
df2["Date"].describe()
indexNames = df2[ df2['Date'].str.contains("split") ].index
df2.drop(indexNames , inplace=True)
df2['Date'] = df2['Date'].astype('datetime64[ns]')
df2.plot(figsize=(10,2), x ='Date', y='Clôture*', kind = 'line')
plt.title('Microsfot Stock Prices in $')
plt.xlabel('Dates')
plt.ylabel('Stock Price in $')
plt.show()
df2Wallet = df2[df2["Date"] >= '2020-09-01']
 
table = soup6.find('table')
table_rows = table.find_all('tr')
res = []
for tr in table_rows:
    td = tr.find_all('td')
    row = [tr.text.strip() for tr in td if tr.text.strip()]
    if row:
        res.append(row)
df3 = pd.DataFrame(res, columns=["Date", "Ouverture", "Élevé ", "Faible ","Clôture*","Cours de clôture ajusté**","Volume"])
df3 = df3.drop(["Ouverture", "Élevé ", "Faible ","Cours de clôture ajusté**","Volume"], axis=1)
df3 = df3.drop_duplicates("Date", keep='first', inplace=False)
df3['Clôture*'] = df3['Clôture*'].str.replace(',','.')
df3['Clôture*'] = df3["Clôture*"].apply (pd.to_numeric, errors='coerce')
df3 = df3.dropna()
df3["Clôture*"] = pd.to_numeric(df3["Clôture*"], downcast="float")
df3["Date"] = df3["Date"].astype(str)
df3["Date"].describe()
 
df3['Date'] = df3['Date'].astype('datetime64[ns]')
df3.plot(figsize=(10,2), x ='Date', y='Clôture*', kind = 'line')
plt.title('EURUSD Exchange Rate')
plt.xlabel('Dates')
plt.ylabel('EURUSD Exchange Rate')
plt.show()
df3Wallet = df3[df3["Date"] >= '2020-09-01']
Wallet = pd.merge(df1Wallet, df2Wallet, on='Date')
Wallet = pd.merge(Wallet, df3Wallet, on = 'Date')
Wallet['Wallet'] = (Wallet["Clôture*_x"]*nbAppleStock + Wallet["Clôture*_y"]*nbMicrosoftStock)/Wallet["Clôture*"]
Wallet['Performance SI'] = (Wallet['Wallet']-InitialWalletInvested)/InitialWalletInvested
Wallet['Date'] =pd.to_datetime(Wallet.Date)
Wallet = Wallet.drop(["Clôture*_x","Clôture*_y","Clôture*"], axis=1)
Wallet = pd.concat([pd.DataFrame([[pd.to_datetime('2020-08-31'), float(InitialWalletInvested), float(0)]],columns=Wallet.columns),Wallet],ignore_index=True)
Wallet = pd.concat([pd.DataFrame([[pd.to_datetime(date.today()), float(WalletPresentValue), Performance]],columns=Wallet.columns),Wallet],ignore_index=True)
Wallet['Performance SI']=Wallet['Performance SI']*100
Wallet = Wallet.sort_values(by='Date')
Wallet.plot(figsize=(10,2), x ='Date', y='Wallet', kind = 'line')
 
plt.title('Ludo Wallet')
plt.xlabel('Dates')
plt.ylabel('Value in €')
plt.show()
Wallet.plot(figsize=(10,2), x ='Date', y='Performance SI', kind = 'line')
plt.title('Ludo Wallet Performance SI')
plt.xlabel('Dates')
plt.ylabel('Performance in %')
plt.show()
os.system('jupyter-nbconvert --to PDFviaHTML Wallet12latest.ipynb')
os.system('explorer.exe "Wallet12latest.pdf"')