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Développement web Flask pour débutants (Large Print Edition)
Paperback
Publisher Price: $25.00
Publisher: Georgette Kelemaokalani
Published: Jan 23 2026
Pages: 192
Weight: 0.69
Height: 0.41 Width: 7.00 Depth: 9.19
Language: French
Frustré par les limites du SQL dans le monde des données actuel ?
Vous maîtrisez l'écriture de requêtes (SELECT, JOIN, agrégations), mais les tâches concrètes se heurtent à des obstacles fichiers CSV/Excel/API complexes, logique personnalisée difficile à gérer en SQL, exportations lentes pour le nettoyage, la visualisation et la préparation au ML, absence d'automatisation simple pour les rapports et changements d'outils constants qui vous font perdre un temps précieux et freinent votre progression.
SQL vers Python pour l'analyse de données Traduire des requêtes SQL en Python avec Pandas est la solution idéale pour une plus grande liberté.
Ce guide, paru en 2025, transforme votre expertise SQL en une maîtrise de Python et Pandas traduisez directement vos requêtes, travaillez en toute flexibilité où que vous soyez, automatisez vos flux de travail et accédez à des analyses plus approfondies sans dépendre d'une base de données.
Ce que vous maîtriserez et appliquerez immédiatement
Traductions SQL précises en Python convertissez les requêtes SELECT, LIMIT, DISTINCT, WHERE, GROUP BY, les fonctions de fenêtrage (classement/totaux cumulés), CASE et JOIN en code Pandas pour une analyse de données plus rapide et plus polyvalente. Gérez n'importe quelle source de données en toute simplicité chargez directement des fichiers CSV, Excel, API et JSON dans des DataFrames Pandas; nettoyez, transformez et analysez vos données sans bases de données rigides.
Automatisez vos flux de travail créez des scripts pour les tâches répétitives telles que l'analyse des tendances des ventes, la segmentation client et les indicateurs RH. Gagnez des heures chaque semaine grâce à des scripts Python réutilisables.
De l'initiation au perfectionnement maîtrisez les fondamentaux de Python et des DataFrames, la logique conditionnelle, les fusions et les bonnes pratiques grâce à des extraits de code, des exercices et des projets (par exemple, des exemples e-commerce et RH).
Intégrez vos données pour un impact optimal combinez des requêtes SQL traduites en Python avec Pandas pour l'analyse, puis étendez-les aux visualisations Matplotlib ou scikit-learn pour créer des pipelines complets.
Des analogies claires, des exemples pas à pas et du code exploitable vous permettent d'obtenir des résultats rapides, sans surcharge théorique.
N'hésitez plus. Prenez le contrôle de vos données. Apprenez dès maintenant à traduire vos requêtes SQL en Python pour l'analyse de données avec Pandas et améliorez vos compétences en la matière !
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