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‎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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[1, 2, 3]"
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]
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},
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"execution_count": 3,
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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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"list_1 = [1,2,3] \n",
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 2, 3])"
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]
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},
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"execution_count": 4,
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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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"array_1 = np.array(list_1)\n",
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1.2, 3.4, 5.6])"
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]
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},
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"execution_count": 5,
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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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"a = np.array([1.2, 3.4, 5.6]) \n",
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"numpy.ndarray"
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]
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},
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"execution_count": 6,
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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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"type(a)"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"numpy.ndarray"
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]
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},
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"execution_count": 7,
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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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"type(array_1)"
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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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"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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([('MMM', 100), ('AOS', 101), ('ABT', 102), ('ABBV', 103),\n",
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" ('ACN', 104), ('ATVI', 105), ('AYI', 106), ('ADBE', 107),\n",
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" ('AAP', 108), ('AMD', 109), ('AES', 110), ('AET', 111),\n",
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" ('AMG', 112), ('AFL', 113), ('A', 114), ('APD', 115),\n",
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" ('AKAM', 116), ('ALK', 117), ('ALB', 118), ('ARE', 119),\n",
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" ('ALXN', 120), ('ALGN', 121), ('ALLE', 122), ('AGN', 123),\n",
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" ('ADS', 124), ('LNT', 125), ('ALL', 126), ('GOOGL', 127),\n",
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" ('GOOG', 128), ('MO', 129), ('AMZN', 130), ('AEE', 131),\n",
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" ('AAL', 132), ('AEP', 133), ('AXP', 134), ('AIG', 135),\n",
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" ('AMT', 136), ('AWK', 137), ('AMP', 138), ('ABC', 139),\n",
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" ('AME', 140), ('AMGN', 141), ('APH', 142), ('APC', 143),\n",
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" ('ADI', 144), ('ANDV', 145), ('ANSS', 146), ('ANTM', 147),\n",
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" ('AON', 148)], dtype=[('Symbol', '<U5'), ('Price', '<i8')])"
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]
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},
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"execution_count": 9,
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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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"data = np.genfromtxt('stock.csv', delimiter=',', names=True,dtype=None, encoding='ascii')\n",
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"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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"type(data)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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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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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎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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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"list_1 = [1,2,3] "
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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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"metadata": {},
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"outputs": [],
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"source": [
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"list_2 = list_1 + list_1"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[1, 2, 3, 1, 2, 3]"
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]
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},
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"execution_count": 3,
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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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"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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([2, 4, 6])"
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]
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},
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"execution_count": 9,
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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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"array_2 = array_1 + array_1 \n",
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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": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([202., 204., 206., 208., 210., 212., 214., 216., 218., 220., 222.,\n",
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" 224., 226., 228., 230., 232., 234., 236., 238., 240., 242., 244.,\n",
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" 246., 248., 250., 252., 254., 256., 258., 260., 262., 264., 266.,\n",
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" 268., 270., 272., 274., 276., 278., 280., 282., 284., 286., 288.,\n",
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" 290., 292., 294., 296.])"
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]
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},
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"execution_count": 23,
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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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"data = np.genfromtxt('numbers.csv', delimiter=',', names=True)\n",
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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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"metadata": {
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"kernelspec": {
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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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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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