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mcrovellamcrovella
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metadat fix
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‎01-Intro-to-Python.ipynb‎

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"list(even)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Reduce"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"source": [
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"The last functional operator is **`reduce()`**.\n",
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"\n",
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"The job of `reduce(function,list)` is to return a single value that combines all the elements of the list.\n",
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"\n",
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"`reduce(function, list)` sequentially applies `function()` to its previously returned value, and the next element of `list`. \n",
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"\n",
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"For example if `list = [a1,a2,a3,...,a10],` then the first step of \n",
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"`reduce(function, list)` will compute `[function(a1,a2),a3,...,a10]`, and so on.\n",
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"\n",
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"The function supplied to reduce is typically commutative."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 129,
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"metadata": {
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"slideshow": {
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"slide_type": "-"
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}
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"outputs": [
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{
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"data": {
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"text/plain": [
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"45"
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]
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},
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"execution_count": 129,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from functools import reduce\n",
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"reduce(lambda x,y: x+y, [x for x in range(10)])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 130,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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"data": {
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"26"
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"execution_count": 130,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"reduce (lambda x,y: x if x>y else y, [1, 15, 26, -27])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"anaconda-cloud": {},
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"celltoolbar": "Slideshow",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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"version": "3.7.10"
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},
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"rise": {
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"scroll": true,
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"theme": "beige"
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"theme": "beige",
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"transition": "fade"
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}
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},
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"nbformat": 4,

‎02A-Git-Jupyter.ipynb‎

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"anaconda-cloud": {},
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"celltoolbar": "Slideshow",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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"version": "3.7.10"
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"nbpresent": {
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"slides": {
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"rise": {
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"nbformat": 4,

‎02B-Pandas.ipynb‎

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"anaconda-cloud": {},
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"name": "python",
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"nbconvert_exporter": "python",
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"rise": {
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‎03-Probability-and-Statistics-Refresher.ipynb‎

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"nbconvert_exporter": "python",
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‎04-Linear-Algebra-Refresher.ipynb‎

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"anaconda-cloud": {},
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"kernelspec": {
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"name": "python",
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"nbconvert_exporter": "python",
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‎05-Distances-Timeseries.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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‎06-Clustering-I-kmeans.ipynb‎

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‎07-Clustering-II-in-practice.ipynb‎

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‎08-Clustering-III-hierarchical.ipynb‎

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‎09-Clustering-IV-GMM-EM.ipynb‎

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