Datenanalyse mit Python: Auswertung von Daten mit Pandas NumPy und IPython (Animals)

Erfahren Sie alles ber das Manipulieren Bereinigen Verarbeiten und Aufbereiten von Datenstzen mit Python: Aktualisiert auf Python 3.6 zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lsen. Gleichzeitig lernen Sie die neuesten Versionen von pandas NumPy IPython und Jupyter kennen. Geschrieben von Wes McKinney dem Begrnder des pandas-Projekts bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl fr Datenanalysten fr die Python Neuland ist als auch fr Python-Programmierer die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehriges Material des Buchs sind auf GitHub verfgbar. Aus dem Inhalt: - Nutzen Sie die IPython-Shell und Jupyter Notebook fr das explorative Computing - Lernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennen - Setzen Sie die Datenanalyse-Tools der pandas- Bibliothek ein - Verwenden Sie flexible Werkzeuge zum Laden Bereinigen Transformieren Zusammenfhren und Umformen von Daten - Erstellen Sie interformative Visualisierungen mit matplotlib - Wenden Sie die GroupBy-Mechanismen von pandas an um Datenstzen zurechtzuschneiden umzugestalten und zusammenzufassen - Analysieren und manipulieren Sie verschiedenste Zeitreihen-Daten Fr diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einfhrung in statsmodels und scikit-learn. Bereits ein Klassiker im Python-kosystem wurde diese Neuauflage in allen Bereichen aktualisiert von Python 3.6 bis zu den neuesten Funktionen von pandas. Durch die genaue Erklrung der Datenwerkzeuge von Python hilft dieses Buch Lesern dabei sie effektiv in neuer und kreativer Weise zu nutzen. Das Buch ist ein wesentlicher Bestandteil einer jeden modernen Bibliothek zum datenintensiven Computing. ? Fernando Prez Assistant Professor fr Statistik an der UC Berkeley Begrnder von IPython und Mitbegrnder von Project Jupyter

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