{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from datascience import *\n", "import numpy as np\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cones = Table.read_table('cones.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Review ##" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cones" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = cones.select('Flavor', 'Color')\n", "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y = x.drop('Color')\n", "y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = cones.select('Color', 'Price')\n", "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numbers ##" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "10 * 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "10 # int " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "1.7 + 4 # float" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "2 + 3 # int" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "2. + 3 # float" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "10 / 3 # float" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "10 / 2 # still a float" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "123 ** 4" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "1234567 ** 890 # limited size, but limit is large" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ ".12345678901234567890123456789 # limited precision" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "30 / 400" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "30 / 4000000000 # output in scientific notation " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "9 ** 0.5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "13 ** 0.5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(13 ** 0.5) ** 2 # After arithmetic, the final few decimal places can be wrong" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "float(3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "int(6.75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strings ##" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'Flavor'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'any snippet of text'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'2' + 'x' # concatenation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'straw' + 'berry'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'two ' + 'words' # notice the space after the 'two '" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'ha' * 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str(2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "int('2')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "int('2.3')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "float('2.3')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "int(float('2.3'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str('3', '2') # To concatenate strings, use +" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'3'+'2'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "2 + 'x'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"I'm a data scientist!\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "'I'm a data scientist!'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Types ##" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(2.3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(100)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type('abcd')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = 5.7\n", "type(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(cones)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arrays ##" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "make_array(1, 2, 3, 4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_array = make_array(5, 6, 7, 8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_array" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(my_array)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum(my_array)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum(my_array) / len(my_array)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_array" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_array * 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "another_one = make_array(20, 30, 40, 50)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_array + another_one" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "yet_another = make_array(1, 2, 3, 4, 5, 6)\n", "my_array + yet_another" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_array" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_array.item(0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ranges ##" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.arange(4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.arange(5, 25)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.arange(5, 25, 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.arange(5, 26, 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Columns of Tables are Arrays ##" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cones.select('Price') # still a table" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(cones.select('Price'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cones.column('Price') # an array" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(cones.column('Price'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 1 }