1. Apprendre
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  2. Cours
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  3. Introduction to Importing Data in Python
  • 1

    Introduction and flat files

    Gratuit

    In 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.

  • 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.

  • 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.

Initializing

Exercice

The structure of .mat in Python

Here, you'll discover what is in the MATLAB dictionary that you loaded in the previous exercise.

The file 'albeck_gene_expression.mat' is already loaded into the variable mat. The following libraries have already been imported as follows:

import scipy.io
import matplotlib.pyplot as plt
import numpy as np

Once again, this file contains gene expression data from the Albeck Lab at UCDavis.

Instructions

100 XP
  • Use the method .keys() on the dictionary mat to print the keys. Most of these keys (in fact the ones that do NOT begin and end with '__') are variables from the corresponding MATLAB environment.
  • Print the type of the value corresponding to the key 'CYratioCyt' in mat. Recall that mat['CYratioCyt'] accesses the value.
  • Print the shape of the value corresponding to the key 'CYratioCyt' using the numpy function shape().
  • Execute the entire script to see some oscillatory gene expression data!