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‎Activity01/Activity01.ipynb

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‎Exercise01/Exercise01.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. Import the numpy package"
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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": 1,
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"import numpy as np"
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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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"source": [
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"2. create a list of 3 elements."
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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": 2,
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"list_1"
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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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"source": [
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"3. convert the list to a numpy array"
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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": 3,
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"array_1"
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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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"source": [
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"4. Create an array of floating Numbers"
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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": 4,
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"a "
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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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"source": [
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"5. Check the type of both objects"
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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": 5,
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"type(list_1)"
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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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"source": [
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"6. load data from a csv into a numpy array"
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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": 8,
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"source": [
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"type(data)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎Exercise02/Exercise02.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. create an array and a list from exercise 26"
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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": 1,
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"array_1 = np.array(list_1) "
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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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"source": [
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"2. concat list_1 with itself."
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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": 3,
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"list_2"
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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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"source": [
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"3. concat array_1 with itself"
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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": 5,
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"array_2"
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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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"source": [
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"4. Load a csv file and concatinate it with itself."
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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": 6,
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"data = data.astype('float64')\n",
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"data + data"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎Exercise16/Exercise16.ipynb

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"cells": [
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 1,
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"metadata": {},
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{
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],
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"source": [
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"import pandas as pd \n",
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"df = pd.read_csv(\"stock.csv\")\n",
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"df = pd.read_csv(\"../datasets/stock.csv\")\n",
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"df.head()\n",
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"print(\"\\nA column is created by assigning it in relation\\n\",'-'*75, sep='')\n",
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"df['New'] = df['Price']+df['Price']\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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},
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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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": "3.7.3"
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"version": "3.7.4"
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}
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},
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"nbformat": 4,

‎Exercise18/Exercise18.ipynb

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"cells": [
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"cell_type": "code",
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"execution_count": 1,
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3\n"
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]
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}
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],
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"source": [
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"x = np.random.randint(1,10)\n",
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"print(x)\n"
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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": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[6]\n"
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]
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}
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],
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"source": [
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"x = np.random.randint(1,10,size=1)\n",
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"print(x)\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[74.65 55.09 98. 92.87 66.13 57.59 93.64 85.92 53.45 52.65 74.01 53.37\n",
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" 55.05 70.02 91.54]\n"
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]
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}
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],
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"source": [
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"x = 50+50*np.random.random(size=15)\n",
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"x = x.round(decimals=2)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[0.77351794 0.60313134 0.18648257]\n",
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" [0.19922192 0.87757217 0.44662105]\n",
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" [0.01515985 0.74621941 0.0894386 ]]\n"
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]
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}
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],
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"source": [
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"x = np.random.rand(3,3)\n",
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"print(x)\n"
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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": "3.7.3"
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"version": "3.7.4"
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}
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},
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"nbformat": 4,

‎Exercise22/Exercise22.ipynb

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