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<ng-template #titleTemplate let-text="title">
    <h4 class="p-2 bg-success text-white">{{text}}</h4>
</ng-template>

<ng-template [ngTemplateOutlet]="titleTemplate"
             [ngTemplateOutletContext]="{title: 'Header'}">
</ng-template>

<div class="text-white m-2">

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// tail recursion: uses O(N) time and O(N) space (call stack)
function getNthFibTail(n, prev, curr){
    
    if(n === 0){ return prev; }
    if(n === 1){ return curr; }

    return getNthFibTail((n-1), curr, (curr+prev));
}

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// JavaScript objects can be created via the object literal {} or via the syntax new Object();
// Additional properties can be added or accessed in one of two ways: 
// object.propertyName or object['propertyName'].

let javascriptObj = {};
let testArr = [1,2,3,4];

javascriptObj.array = testArr;
console.log(javascriptObj);

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// array literal
let array1 = [1,2,3,4];
array1.push(5); //array1 = [1,2,3,4,5]
array1.push(7); //array1 = [1,2,3,4,5,7]
array1.push(2); //array1 = [1,2,3,4,5,7,2]
console.log(array1);

// deletion: pop returns last item
let array2 = [1,2,3,4];
array2.pop(); //returns 4, array2 = [1,2,3]

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// sets
let exampleSet = new Set();

// has one property - size
exampleSet.add(1); // exampleSet: Set {1}
exampleSet.add(1); // exampleSet: Set {1}
exampleSet.add(2); // exampleSet: Set {1, 2}

// deletion
exampleSet.delete(1); // true, exampleSet: Set {2}

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Intro to numpy

Spyder

• Provides an ipython environment from the IDE
  Console -> Open ipython console

ipython

• Can use standard help function help('numpy')
• Can get help on numpy array function help('numpy.array')
• Import numpy with import numpy as np

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Jupyter and IPython

Capture Cell Magic

• cell magic for output capture:
  %%capture: captures output and stderr streams
  store streams in variable or discard streams (default)

  %%capture myCapture
  print('this is standard output')
  print('this is stderr', file=sys.stderr)

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Pandas

Summary

• Python Package
• Panel Data System
• Data Analysis Library
• Data Structures:
  Series
  DataFrame
  Panel

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import pandas as pd

# create dataframes
df1 = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
                'W': ['W0', 'W1', 'W2'],
                'Y': ['Y0', 'Y1', 'Y2']})

df2 = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
                'X': ['X0', 'X1', 'X2'],
                'Z': ['Z0', 'Z1', 'Z2']})

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Intro to NLTK

Using NLTK

• import: import nltk
• download examples: import nltk, then nltk.download()
• opens nltk downloader GUI can download collections/packages
• corpora: sets of structured text
• Example: