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286 changes: 286 additions & 0 deletions jjack113.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,286 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IPython version: %6.6s 7.17.0\n"
]
}
],
"source": [
"import IPython\n",
"import json\n",
"# Numpy is a library for working with Arrays\n",
"import numpy as np\n",
"# SciPy implements many different numerical algorithms\n",
"import scipy as sp\n",
"# Pandas is good with data tables\n",
"import pandas as pd\n",
"# Module for plotting\n",
"import matplotlib\n",
"#BeautifulSoup parses HTML documents (once you get them via requests)\n",
"import bs4\n",
"# Nltk helps with some natural language tasks, like stemming\n",
"import nltk\n",
"# Bson is a binary format of json to be stored in databases\n",
"import bson\n",
"# Mongo is one of common nosql databases \n",
"# it stores/searches json documents natively\n",
"import pymongo\n",
"print (\"IPython version: %6.6s\", IPython.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Make a 2 row x 3 column array of random numbers\n",
"[[0.88498519 0.11704372 0.660586 ]\n",
" [0.41760578 0.12936561 0.94512844]]\n",
"Add 5 to every element\n",
"[[5.88498519 5.11704372 5.660586 ]\n",
" [5.41760578 5.12936561 5.94512844]]\n",
"Get the first row\n",
"[5.88498519 5.11704372 5.660586 ]\n"
]
}
],
"source": [
"#Here is what numpy can do\\n\",\n",
"print (\"Make a 2 row x 3 column array of random numbers\")\n",
"x = np.random.random((2, 3))\n",
"print (x)\n",
"\n",
"#array operation (as in R)\n",
"print (\"Add 5 to every element\")\n",
"x = x + 5\n",
"print (x)\n",
"\n",
"# get a slice (first row) (as in R)\n",
"print (\"Get the first row\")\n",
"print (x[0, :])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function ndarray.any>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
"# on possible completions.\n",
"# In the code cell below, type x.<TAB>, to find built-in operations for x\n",
"x.any"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline \n",
"import matplotlib.pyplot as plt\n",
"heads = np.random.binomial(500, .5, size=500)\n",
"histogram = plt.hist(heads, bins=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Task 1\n",
"## write a program to produce Fibonacci numbers up to 1000000"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"1\n",
"2\n",
"3\n",
"5\n",
"8\n",
"13\n",
"21\n",
"34\n",
"55\n",
"89\n",
"144\n",
"233\n",
"377\n",
"610\n",
"987\n",
"1597\n",
"2584\n",
"4181\n",
"6765\n",
"10946\n",
"17711\n",
"28657\n",
"46368\n",
"75025\n",
"121393\n",
"196418\n",
"317811\n",
"514229\n",
"832040\n"
]
}
],
"source": [
"count = 0\n",
"x = 0\n",
"y = 1\n",
"\n",
"while count < nterms:\n",
" if x < 1000000:\n",
" print(x)\n",
" next_num = x + y\n",
" x = y\n",
" y = next_num\n",
" count += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Task 2\n",
"## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
"## Calculate the mean and standard deviation of that sample"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.508\n",
"0.49993599590347565\n"
]
}
],
"source": [
"import numpy as np\n",
"n, p = 1, .5\n",
"s = np.random.binomial(n, p, 1000)\n",
"mean = sum(s)/1000 \n",
"print(mean)\n",
"sd = np.std(s)\n",
"print(sd)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Task 3\n",
"## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
"### use numpy.random.normal to generate gaussian distribution"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'plt' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-a8aa6c41ed67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mscatterplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
]
}
],
"source": [
"import matplotlib\n",
"x = np.random.normal(0,5,1000)\n",
"e = np.random.normal(0,1,1000)\n",
"y = 0.5 * x + e\n",
"scatterplot = plt.scatter(x,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}