Introduction to Importing Data in Python
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
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Description du cours
As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.
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Importation et nettoyage des données en Python
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Introduction and flat files
GratuitIn this chapter, you'll learn how to import data into Python from all types of flat files, which are a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas—you will learn how to use these packages to import flat files and customize your imports.
Welcome to the course!50 xpImporting entire text files100 xpImporting text files line by line100 xpThe importance of flat files in data science50 xpPop quiz: what exactly are flat files?50 xpWhy we like flat files and the Zen of Python50 xpImporting flat files using NumPy50 xpUsing NumPy to import flat files100 xpCustomizing your NumPy import100 xpImporting different datatypes100 xpImporting flat files using pandas50 xpUsing pandas to import flat files as DataFrames (1)100 xpUsing pandas to import flat files as DataFrames (2)100 xpCustomizing your pandas import100 xpFinal thoughts on data import50 xp - 2
Importing data from other file types
You've learned how to import flat files, but there are many other file types you will potentially have to work with as a data scientist. In this chapter, you'll learn how to import data into Python from a wide array of important file types. These include pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files.
Introduction to other file types50 xpNot so flat any more50 xpLoading a pickled file100 xpListing sheets in Excel files100 xpImporting sheets from Excel files100 xpCustomizing your spreadsheet import100 xpImporting SAS/Stata files using pandas50 xpHow to import SAS7BDAT50 xpImporting SAS files100 xpUsing read_stata to import Stata files50 xpImporting Stata files100 xpImporting HDF5 files50 xpUsing File to import HDF5 files50 xpUsing h5py to import HDF5 files100 xpExtracting data from your HDF5 file100 xpImporting MATLAB files50 xpLoading .mat files100 xpThe structure of .mat in Python100 xp - 3
Working with relational databases in Python
In this chapter, you'll learn how to extract meaningful data from relational databases, an essential skill for any data scientist. You will learn about relational models, how to create SQL queries, how to filter and order your SQL records, and how to perform advanced queries by joining database tables.
Introduction to relational databases50 xpPop quiz: The relational model50 xpCreating a database engine in Python50 xpCreating a database engine100 xpWhat are the tables in the database?100 xpQuerying relational databases in Python50 xpThe Hello World of SQL Queries!100 xpCustomizing the Hello World of SQL Queries100 xpFiltering your database records using SQL's WHERE100 xpOrdering your SQL records with ORDER BY100 xpQuerying relational databases directly with pandas50 xpPandas and The Hello World of SQL Queries!100 xpPandas for more complex querying100 xpAdvanced querying: exploiting table relationships50 xpThe power of SQL lies in relationships between tables: INNER JOIN100 xpFiltering your INNER JOIN100 xpFinal Thoughts50 xp
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Importation et nettoyage des données en Python
Aller à la pisteensembles de données
Chinook (SQLite)LIGO (HDF5)Battledeath (XLSX)Extent of infectious diseases (DTA)Gene expressions (MATLAB)MNISTSales (SAS7BDAT)SeaslugsTitaniccollaborateurs
prérequis
Intermediate Python
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