hdary85

from PyQt5.QtWidgets import QApplication, QTreeWidget, QTreeWidgetItem, QVBoxLayout, QPushButton, QWidget, QLineEdit, QMessageBox, QLabel

class NestedDictManager(QApplication):
    def __init__(self, original_data):
        super(NestedDictManager, self).__init([])

        self.original_data = original_data.copy()
        self.data = original_data.copy()

        self.tree = QTreeWidget()

hdary85

%macro CalculTrimestre(dateDebutTrimestre);

    /* Format the dateDebutTrimestre variable as a date */
    %let dateDebutTrimestre = %sysfunc(inputn(&dateDebutTrimestre, date9.));

    /* Étape 1: Filtrer les clients dont l'anniversaire est dans le trimestre fourni */
    proc sql;
        create table ClientsAnniversaire as
        select *
        from VotreTable

hdary85

Taux_Variation_Produit = 
VAR Ventes_Semestre1 = CALCULATE(SUM('TableVentess'[Ventes]), 'TableVentess'[Semestre] = 1)
VAR Ventes_Semestre2 = CALCULATE(SUM('TableVentess'[Ventes]), 'TableVentess'[Semestre] = 2)
VAR Ventes_Produit = CALCULATE(SUM('TableVentess'[Ventes]), ALLEXCEPT('TableVentess', 'TableVentess'[Produit]))
RETURN
IF(ISBLANK(Ventes_Semestre1) || ISBLANK(Ventes_Semestre2) || Ventes_Semestre1 = 0, BLANK(), (Ventes_Semestre2 - Ventes_Semestre1) / Ventes_Produit)

hdary85

Taux_Variation_MNE = 
CALCULATE(
    (
        SUMX(
            VALUES('PBI'[MNE]),
            (CALCULATE(SUM('PBI'[Montant]), 'PBI'[Semestre] = 2) -
             CALCULATE(SUM('PBI'[Montant]), 'PBI'[Semestre] = 1)) /
             CALCULATE(SUM('PBI'[Montant]), 'PBI'[Semestre] = 1)
        )
    ),

hdary85

/* Déclaration de la macro variable */
%let date_sas = '01JAN2023'd;

/* Extraction de v1 */
data _null_;
    /* Conversion de la date en format numérique YYYYMM */
    v1 = year(&date_sas.) * 100 + month(&date_sas.);
    call symputx('v1', v1);
run;

hdary85

import pandas as pd

# Texte initial
texte = 'prix\n100\nnombre\n2'

# Diviser le texte en lignes
lignes = texte.split('\n')

# Initialiser des listes vides pour stocker les données
colonnes = []

hdary85

merged = pd.merge(df1, df2, on='C1', how='left')

# Grouping by 'C1' and checking for multiple occurrences
grouped = merged.groupby('C1')

# Creating the new DataFrame with custom values for multiple occurrences
df3 = pd.DataFrame(columns=merged.columns)
for name, group in grouped:
    if len(group) > 1:
        df3 = df3.append({col: 'multiple' if col == 'C2' else 'MULTIPLE' for col in merged.columns}, ignore_index=True)

hdary85

df['col1'] = df['col1'].fillna(df['col2'])

# --- 2. Prétraitement du DataFrame TV2 ---
# On s'attend à ce que TV2 contienne les colonnes suivantes en MAJUSCULE :
# PARTY_KEY, ROLE, PARTY_TYPE_CODE, RISQUE_SECTEUR, BASE_CURR_AMOUNT, etc.
TV2['PARTY_TYPE_CODE'] = TV2['PARTY_TYPE_CODE'].astype(int)

# Agrégation générale par PARTY_KEY pour les indicateurs financiers
tv2_agg = TV2.groupby('PARTY_KEY').agg(

hdary85

import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.cluster import DBSCAN
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping
import matplotlib.pyplot as plt

hdary85

import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.cluster import DBSCAN
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping