Data Analysis

JupyterLab

JupyterLab is a next-generation web-based user interface for Project Jupyter.

JupyterLab enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner

Code Consoles

provide transient scratchpads for running code interactively, with full support for rich output

Kernel-backed documents

enable code in any text file (Markdown, Python, R, LaTeX, etc.) to be run interactively in any Jupyter kernel.

JupyterLab also offers a unified model for viewing and handling data formats. JupyterLab understands many file formats (images, CSV, JSON, Markdown, PDF, Vega, Vega-Lite, etc.

NumPy

• a Python package used for data analysis

• Numpy 2-Dimensional Arrays / matrix

• rows are the first axis
• columns are the second axis

• the array function is used to convert th list of lists into an array

• Code Example(reading a csv file and converting the output list into an array)

  import csv with open(“winequality-red.csv”, ‘r’) as f: wines = list(csv.reader(f, delimiter=”;”)) import numpy as np wines = np.array(wines[1:], dtype=np.float)

  Alternative NumPy Array Creation Methods

• Using np.zeros where are all elements are zeroes.

  import numpy as np empty_array = np.zeros((3,4))

• Using numpy.random.rand. where each element is a random number

  np.random.rand(3,4)

Reading files using NumPy

• using numpy.genfromtxt function.

  wines = np.genfromtxt(“winequality-red.csv”, delimiter=”;”, skip_header=1)

Indexing NumPy Arrays

• NumPy is zero-indexed, meaning that the index of the first row is 0, and the index of the first column is 0

• Exampele: “wines[2,3]” ===> element positioned in the third row and the fourth column

Slicing NumPy Arrays

• using colon, selects elements up until the last index, without it being included.

  wines[0:3,3]

• We can extract an entire column wines[:,3] or an entire row wines[3,:]
• We can choose the whole array using wines[:,:]

Assigning Values To NumPy Arrays

wines[1,5] = 10

Arrays can either be single dimensional, two dimensional or multi dimensional

NumPy Data Types

• using array-name.dtype
• Data type can be any of the following:

  float — numeric floating point data. int — integer data. string — character data. object — Python objects.

Data Type Conversions

array-name.astype(int)

NumPy Array Operations

Single Array Math

basic mathematical operations (/, *, -, +, ^) with an array and a value, it will apply the operation to each of the elements in the array.

Multiple Array Math

• applying mathematical operations (/, *, -, +, ^) between arrays

NumPy has many Array Methods, such as “sum()”

Subsetting can be done by choosing certain elemnts from the array, on which a certain condition applies to. In other words, we get to filter arrays based on a certain criteria.

high_quality_and_alcohol = (wines[:,10] > 10) & (wines[:,11] > 7)