JupyterLab

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

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.
Notebook cell outputs can be mirrored into their own tab, side by side with the notebook, enabling simple dashboards with interactive controls backed by a kernel.
Multiple views of documents with different editors or viewers enable live editing of documents reflected in other viewers.
JupyterLab also offers a unified model for viewing and handling data formats and understands many file formats
JupyterLab offers customizable keyboard shortcuts and the ability to use key maps from vim, emacs, and Sublime Text in the text editor.
JupyterLab extensions can customize or enhance any part of JupyterLab, including new themes, file editors, and custom components.
JupyterLab is served from the same server and uses the same notebook document format as the classic Jupyter Notebook.

Numpy tutorial

NumPy is a commonly used Python data analysis package

With NumPy, we can work with multidimensional arrays
the core of NumPy is written in a programming language called C, which stores data differently than the Python data types. NumPy data types map between Python and C, allowing us to use NumPy arrays without any conversion hitches.
NumPy has several different data types :
- float, int, string and object (Python objects)
advantages of NumPy :
- it simple to perform mathematical operations on arrays
- and makes it quite easy to do computations.
NumPy makes it possible to test to see if rows match certain values using mathematical comparison operations
With NumPy, it’s very common to combine multiple arrays into a single unified array

import numpy as np numpyarray = np.array(ordinaryArray[1:], dtype=np.float)

empty_array = np.zeros((3,4)) create zeroes element array with three row and four columns
np.random.rand(3,4) create array with random number element
Using NumPy To Read In Files

data = np.genfromtxt("file.csv", delimiter=";", skip_header=1)
- skip_header=1 so that the header row is skipped.
- genfromtxt function to read in the file.csv file.
  Indexing NumPy Arrays
  
  numpyarray[2,3]

numpyarray[0:3,3]

we can do it using a colon (:). A colon indicates that we want to select all the elements from the starting index up to but not including the ending index.

numpyarray[1,5] = value

1-Dimensional NumPy Arrays
- NumPy is a package for working with multidimensional arrays. One of the most common types of multidimensional arrays is the 1-dimensional array, or vector.
N-Dimensional NumPy Arrays
Converting Data Types
You can use the numpy.ndarray.astype method to convert an array to a different type. The method will actually copy the array, and return a new array with the specified data type.
We can check the name property of the dtype of the resulting array to see what data type NumPy mapped the resulting array to
- nparray.dtype.name
if you want more control over how the array is stored in memory, you can directly create NumPy dtype objects like np.int32

np.npArray.sum () : This finds the sum of all the elements in an array
sum(axis=0) We can pass the axis keyword argument into the sum method to find sums over an axis
We can verify that we did the sum correctly by checking the shape sum().shape
np.npArray.mean — finds the mean of an array.
np.npArray.std — finds the standard deviation of an array.
np.npArray.min — finds the minimum value in an array.
np.npArray.max — finds the maximum value in an array.

NumPy makes it possible to test to see if rows match certain values using mathematical comparison operations like <, >, >=, <=, and ==
Subsetting One of the powerful things we can do with a Boolean array and a NumPy array is select only certain rows or columns in the NumPy array.

We can change the shape of arrays while still preserving all of their elements. This often can make it easier to access array elements.
np.transpose flip the axes, so rows become columns, and vice versa.
np.ravel to turn an array into a one-dimensional representation. It will essentially flatten an array into a long sequence of values:
np.reshape to reshape an array to a certain shape we specify.

to combine multiple arrays into a single unified array. We can use np.vstack to vertically stack multiple arrays. np.vstack((array1, array2))
np.concatenate as a general purpose version of hstack and vstack. If we want to concatenate two arrays, we pass them into concatenate, then specify the axis keyword argument that we want to concatenate along. Concatenating along the first axis is similar to vstack, and concatenating along the second axis is similar to hstack