{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " \n", "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n", "\n", "
Author: Denis Mironov (@dmironov)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#
Insights of Monty Hall paradox with Plotly
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotly is a technical computing company headquartered in Montreal, Quebec, that develops online data analytics and visualization tools https://plot.ly. Plotly provides online graphing, analytics, and statistics tools as well as graphing libraries for several programming languages including Python. With the latter, we present here a Plotly tutorial as a visual analysis of well-known Monty Hall paradox https://en.wikipedia.org/wiki/Monty_Hall_problem which is related to the following game:\n", "\n", ">Suppose you are on a game show, and you are given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say №1, and the host, who knows what is behind the doors, opens another door, say №3, which has a goat. The host then says to you, \"Do you want to pick door №2?\" Is it to your advantage to switch your choice?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is analytically proven that if initial door choice is changed, the percentage to win is about 66.6%, hence the percentage to win if initial door choice remains the same is 33.3%. It is also obvious that if our final choice is solely based on the outcome of unbiased coin toss, then we win in 1/2 cases. Here we are going to demonstrate the results of these strategies graphically by using Plotly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Jupiter Notebook is organized as follows:\n", "\n", "1. Simulation of Monty Hall Paradox\n", "2. Importing and discussing libraries used for illustrations\n", "3. Graphical analysis\n", " - Line, Box, Violin and Distribution plots\n", " - Contour and 3D Clustering plots\n", "4. Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Simulation of Monty Hall Paradox" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the problem, we need to generate the dataset first in accordance with the game rules outlined above. Here the dataset represents the results of win percentage by using one of three possible options upon opening of the other door by the host:\n", "\n", "1. Initial door choice is changed\n", "2. Initial door choice remains unchanged\n", "3. Random choice (initial door choice is either changed or not based on the outcome of an unbiased coin)\n", "\n", "To model the game with these three strategies, we present a slightly modified solution obtained by us while working on Workshop Case Study №037 of https://www.superdatascience.com. \n", "\n", "We create 6 functions:\n", "\n", "** 1. newGame() **\n", " - Creates a new game with all possible combinations of {car, goat, goat}. Final mapping of {car, goat, goat} to {door1, door2, door3} is done with the use of a random number\n", "\n", "** 2. guestChoice() **\n", " - Initial door choice made by a player\n", "\n", "** 3. openOneDoor(game, chosen_door) **\n", " - The host opens one door which is not the one choosen by a player and does not have a car behind. Such a modelling is based on the items positions behind the doors (game) and initial choice of a player (chosen_door) \n", "\n", "** 4. guestChange(game, chosen_door, change) **\n", " - Whether a player changes initial choice (chosen_door, change). A modelling based on the items positions behind the doors (game)\n", "\n", "** 5. checkResult(game, chosen_door, change) **\n", " - Whether a player wins or losses by using one of the three strategies mentioned above\n", "\n", "** 6. result(n=400, step=1) **\n", " - Modelling range(1, n+1, step) games in a row. Collecting the outcomes of each of three strategies applied to range(1, n+1, step) games. The latter means the we model 1 round, record the result, then model 2 consecutive rounds, record the result, and so on and so forth up to n consecutive rounds. Note that in these notations a game number represents the number of rounds, for instance, if a game number is 50, then there are 50 consecutive rounds in this game" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# random number generator\n", "from numpy import random\n", "\n", "# Generate a new game incorporating 3 doors: there is a car behind one of them,\n", "# there is a goat behind the other as well as a goat behind the last one.\n", "def newGame(): \n", " position = random.randint(1,4)\n", " if position == 1:\n", " game_dict = {'door1':'car','door2':'goat','door3':'goat'}\n", " return game_dict\n", " elif position == 2:\n", " game_dict = {'door1':'goat','door2':'car','door3':'goat'}\n", " return game_dict\n", " else:\n", " game_dict = {'door1':'goat','door2':'goat', 'door3':'car'}\n", " return game_dict\n", " \n", "# Function guestChoice is designed to randomly model door choice of a player\n", "def guestChoice():\n", " door_choice = random.randint(1,4)\n", " if door_choice == 1:\n", " return 'door1'\n", " elif door_choice == 2:\n", " return 'door2'\n", " else:\n", " return 'door3'\n", " \n", "# Function openOneDoor simulates door opening in the round.\n", "def openOneDoor(game, chosen_door):\n", " all_doors = ['door1', 'door2', 'door3']\n", " element_open = []\n", " options = game.copy()\n", " \n", " if game[chosen_door] != 'car':\n", " for element in all_doors:\n", " if element != chosen_door and game[element] != 'car':\n", " element_open.append(element)\n", " options[element_open[0]] = 'open'\n", " return options\n", " \n", " elif game[chosen_door] == 'car':\n", " for element in all_doors:\n", " if element != chosen_door:\n", " element_open.append(element)\n", " number = random.randint(1,3)\n", " if number == 1:\n", " options[element_open[0]] = 'open'\n", " return options\n", " else:\n", " options[element_open[1]] = 'open'\n", " return options\n", " \n", "# Function guesChange changes the game conditions depending on either \n", "# a player changes his/her initial decision or not\n", "def guestChange(game, chosen_door, change):\n", " game_with_open = openOneDoor(game, chosen_door)\n", " \n", " if change == 'Y':\n", " for element in game_with_open:\n", " if (game_with_open[element] != 'open' and element != chosen_door):\n", " return element\n", " \n", " else:\n", " return chosen_door\n", "\n", "# Function checkResult checks result of one round of a game.\n", "def checkResult(game, chosen_door, change):\n", " final_choice = guestChange(game, chosen_door, change)\n", " result = game[final_choice]\n", " \n", " if result == 'car':\n", " return 'WIN'\n", " else:\n", " return 'LOSE'\n", " \n", "# Function Result calculates win% if our strategy either\n", "# to change our initial door choice or not to change. We also simulate the \n", "# random process: decision whether to change the door or not solely based on\n", "# the outcome of unbiased coin.\n", "def result(n=400, step=1): \n", " list_N = []\n", " win_change = []\n", " win_nochange = []\n", " win_rand = []\n", " \n", " for N in range(1, n+1, step):\n", " count_y = 0\n", " count_n = 0\n", " count_rand = 0\n", "\n", " for i in range(N):\n", " game = newGame()\n", " chosen_door = guestChoice()\n", " if checkResult(game, chosen_door, change='Y') == 'WIN':\n", " count_y += 1\n", " if checkResult(game, chosen_door, change='N') == 'WIN':\n", " count_n += 1\n", " if checkResult(game, chosen_door, change='Y' if random.randint(2) == 0 else 'N') == 'WIN':\n", " count_rand += 1\n", "\n", " list_N.append(N)\n", " win_change.append(count_y / N * 100)\n", " win_nochange.append(count_n / N * 100)\n", " win_rand.append(count_rand / N * 100)\n", " \n", " return list_N, win_change, win_nochange, win_rand" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [], "source": [ "list_N, win_change, win_nochange, win_rand = result(n=2000, step=1) # modelling of 2000 games. i-th game has i rounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- ** list_N: ** is a list with № of game\n", "- ** win_change: ** is a list with win% if initial door choice has been changed. \n", "- ** win_nochange: ** win% if initial door choice has been unchanged. \n", "- ** win_rand: ** win% if a decision whether to change initial door choice has been made upon unbiased coin outcome. \n", "\n", "Each position in ** win_change ** , ** win_nochange **, ** win_rand ** corresponds to that in ** list_N **. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of games: 2000 \n", "Max number of rounds with changing initial door choice: 2000\n", "Max number of rounds without changing initial door choice: 2000 \n", "Max number of rounds with random choice after one door is open: 2000\n" ] } ], "source": [ "print (\"Total number of games: %s \" % list_N[-1])\n", "print (\"Max number of rounds with changing initial door choice: %s\" % len(win_change))\n", "print (\"Max number of rounds without changing initial door choice: %s \" % len(win_nochange))\n", "print (\"Max number of rounds with random choice after one door is open: %s\" % len(win_rand))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that in our modelling i-th game has i rounds. The latter means that, for instance, in game №5 we play the game 5 consecutive times, and calculate win% based on the outcomes in these 5 rounds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Importing and discussing libraries used for illustrations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only library we have been needed so far is a random number generator. Now, when we have simulated the game and collected the results, we are ready to start performing graphical analysis, but before that, evidently, we need to import some libraries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, we are interested in learning Plotly, so let's check its version first." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Below figures are generated with 3.4.2 plotly version\n" ] } ], "source": [ "import plotly\n", "print (\"Below figures are generated with {} {}\".format(plotly.__version__, 'plotly version'))\n", "# author's output: Below figures are generated with 3.3.0 plotly version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we will see below, there is a JSON object under every plotly visualization. It is possible to operate with such an object similar to dictionary data structure by changing the values of keywords within the object. So, we load library to generate plotting objects in future:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# generates a plotting object\n", "import plotly.graph_objs as go " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are interested in getting illustrations in this Jupiter Notebook. For this purpose, we need to import download_plotlyjs, init_notebook_mode and iplot. The first one is used for plotly javascript initiallization, the second one is governed by argument connected = True or connected = False determining whether illustrations should be plotted inside Jupiter Notebook, True, or outside, False (new browser tab will be initiallized containing your figure), and the last one is used for illustrating a figure." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n", "init_notebook_mode(connected=True) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several chart types that are not included in plotly.js, for instance, a distribution plot. So, we have to import them separately." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import plotly.figure_factory as ff " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also import some packages that are well-known for you." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import warnings\n", "warnings.simplefilter('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Graphical analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3.1 Line, Box, Violin and Distribution plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we present ** plotResult** function which will be responsible for plotting scatter, box, violin and distribution illustrations. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plotResult(change, nochange, rand, game=None, output = 'fig', plot='line', \n", " title='', jitter=0, pointpos=0, width=700, height=300, bin_size=.2, show_rug=False):\n", " \n", " \"\"\"\n", " Creates line, box, violin or distribution plot by using Plotly library\n", " \n", " games: list\n", " - number of games\n", " \n", " change: list\n", " - percentage of wins if initial door choice is changed\n", " \n", " nochange: list\n", " - percentage of wins if initial door choice is unchanged\n", " \n", " rand: list\n", " - percentage of wins if upon opening, in accordance with game rules, \n", " a door by a host final choice is made pure randomly\n", " \n", " output: string\n", " - 'fig' means to give an output in form of illustration\n", " - 'json' means to give an output in form of json format\n", " \n", " plot: string\n", " - 'line' means to plot a line\n", " - 'dist' means to plot a distribution\n", " - 'violin' means to plot a violin\n", " - 'box' means to plot a box\n", " \n", " title: string\n", " - title of a graph\n", " \n", " jitter: number\n", " - a number between -1 and 1 (scatter of points while generating box plot)\n", " \n", " pointpot: number\n", " - a number between -2 and 2 (position of jitter in relation to box plot)\n", " \n", " width: number\n", " - width of a figure (not available when plot = 'dist' or plot = 'violin')\n", " \n", " height: number\n", " - height of a figure (not available when plot = 'dist' or plot = 'violin')\n", " \n", " bin_size: number\n", " - size of bins while creating a distribution plot\n", " \n", " show_rug: bool\n", " - whether a distribution graph should show a rug or not\n", " \n", " \"\"\"\n", " \n", " if type(game) == list:\n", " # Slicing list up to game[-1] \n", " change = change[:games[-1]]\n", " nochange = nochange[:games[-1]]\n", " rand = rand[:games[-1]]\n", " else:\n", " change = change\n", " nochange = nochange\n", " rand = rand\n", " \n", " # Two main conditions: whether plot != 'violin' and plot != 'dist'\n", " # this is due to different ways of creating plots\n", " if ((plot != 'violin') & (plot != 'dist')):\n", " \n", " # In this block we choose whether to plot line or box illustrations\n", " # Here is line option\n", " if plot == 'line':\n", " trace_change = go.Scatter(x=games, \n", " y=change, \n", " name='Change',\n", " line=dict(color='#33CFA5')) \n", " trace_no_change = go.Scatter(x=games, \n", " y=nochange, \n", " name='No Change',\n", " line=dict(color='#F06A6A'))\n", " trace_guessing = go.Scatter(x=games, \n", " y=rand,\n", " name='Random', \n", " line=dict(color='gray'))\n", " xx = dict(title= '№ Game', \n", " ticklen= 5, \n", " zeroline= False,\n", " gridwidth= 2)\n", " \n", " # Here is box option\n", " elif plot == 'box':\n", " trace_change = go.Box(y=change,\n", " jitter=jitter,\n", " boxpoints = 'all', \n", " pointpos = pointpos,\n", " name='Change', \n", " line=dict(color='#33CFA5'))\n", " trace_no_change = go.Box(y=nochange,\n", " jitter=jitter,\n", " boxpoints = 'all', \n", " pointpos = pointpos,\n", " name='No Change',\n", " line=dict(color='#F06A6A'))\n", " trace_guessing = go.Box(y=rand,\n", " jitter=jitter,\n", " boxpoints = 'all', \n", " pointpos = pointpos,\n", " name='Random',\n", " line=dict(color='gray'))\n", " xx=None\n", " \n", " # Collecting data in a list form.\n", " # trace_change - data related to percentage of wins when initial door choice is changed\n", " # trace_no_change - data ..... initial door choice is unchanged\n", " # trace_guessing - data .... final door choice is based on unbiased coin outcome\n", " data = [trace_change, trace_guessing, trace_no_change]\n", " \n", " # So-called ornament of our figure\n", " layout = go.Layout(title=title, \n", " autosize=False,\n", " width=width,\n", " height=height,\n", " xaxis = xx,\n", " yaxis = dict(title= '% Win',\n", " ticklen= 5,\n", " gridwidth= 2))\n", " \n", " # Saving figure as fig variable which will be return by the function as iplot(fig) if output = 'fig'\n", " fig = go.Figure(data=data, layout=layout)\n", " \n", " # Block of code related to plotting violin type of chart \n", " elif plot == 'violin':\n", " win_dist = pd.DataFrame(data={\n", " 'Change': win_change,\n", " 'Random': win_rand,\n", " 'No Change': win_nochange\n", " })\n", " color = ['#33CFA5', 'gray', '#F06A6A']\n", " data = []\n", " for i in range(3):\n", " trace = {\n", " \"type\": 'violin',\n", " \"y\": win_dist.iloc[:,i],\n", " \"name\": win_dist.columns[i],\n", " \"box\": {\"visible\": True},\n", " \"points\": 'all',\n", " \"jitter\": jitter,\n", " \"pointpos\" : pointpos,\n", " \"meanline\": {\"visible\": True},\n", " \"line\": {\"color\": color[i]}\n", " }\n", " data.append(trace)\n", "\n", " fig = {\n", " \"data\": data,\n", " \"layout\" : {\n", " \"title\": \"\",\n", " \"yaxis\": {\"title\": '% Win',\n", " \"zeroline\": False}\n", " }\n", " }\n", " \n", " # Block of code related to plotting a distribution\n", " elif plot == 'dist':\n", " data = [change, nochange, rand]\n", " group_labels = ['Change','No Change','Random']\n", " colors = ['#33CFA5','#F06A6A','gray',]\n", " xx=None\n", "\n", " fig = ff.create_distplot(data, \n", " group_labels, \n", " colors=colors,\n", " bin_size=bin_size, \n", " show_rug=show_rug)\n", " \n", " # if output == 'fig' return graph by using iplot from plotly library\n", " if output == 'fig':\n", " return iplot(fig)\n", " \n", " # if output == 'json' return information about plotly object\n", " elif output == 'json':\n", " return fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**go.Scatter **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's discuss a part of ** plotResult** which is implemented under if plot == 'line condition: \n", "\n", " if plot == 'line':\n", " trace_change = go.Scatter(x=games, \n", " y=change, \n", " name='change',\n", " line=dict(color='#33CFA5')) \n", " trace_no_change = go.Scatter(x=games, \n", " y=nochange, \n", " name='no change',\n", " line=dict(color='#F06A6A'))\n", " trace_guessing = go.Scatter(x=games, \n", " y=rand,\n", " name='random', \n", " line=dict(color='gray'))\n", " xx = dict(title= '№ Game', \n", " ticklen= 5, \n", " zeroline= False,\n", " gridwidth= 2)\n", "\n", " data = [trace_change, trace_guessing, trace_no_change]\n", "\n", " layout = go.Layout(title=title, \n", " autosize=False,\n", " width=width,\n", " height=height,\n", " xaxis = xx,\n", " yaxis = dict(title= '% Win',\n", " ticklen= 5,\n", " gridwidth= 2))\n", "\n", " fig = go.Figure(data=data, layout=layout)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The comments within the function have been purposely deleted." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this code snippet, we create three objects which are quite similar and started as go.Scatter. However, they have different arguments: \n", "\n", "- **y**: responsible for values taken on vertical axis\n", "- **name**: saves the name which should be shown in the output figure\n", "- **line**: a color of the curve\n", "\n", "All go.Scatter objects are saved in appropriate variables which names usually start with phrase trace. Later, such variables are stored in a list which is usually named as data since this is a collection of all our data. If we are not interested in putting any ornament of our figure, for instance, a title, width and/or height specifications, then we are ready to produce a figure by injecting data list to go.Figure plotly object.\n", "\n", "As one can see above, we have decided to design our figure in a more nicely way. For doing this we use go.Layout object incorporating arguments:\n", "\n", "- **title**: a title of our illustration\n", "- **width **: width of our figure*\n", "- **height **: height of our figure*\n", "- **xaxis **: responsible for notations on horizontal axis (uses xx variable defined within if condition)\n", "- **yaxis **: responsible for notations on vertical axis\n", "\n", "($*$) **width** and **height** control figure size with autosize=False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** go.Box **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Box is a graph object which takes arguments similar to those taken by Scatter, but with several new:\n", "\n", "- ** jitter **: to make points more visible (from 0 to 1)\n", "- ** boxpoints **: show data points\n", "- ** pointpos **: position of points in relation to violin graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Violin**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " elif plot == 'violin':\n", " win_dist = pd.DataFrame(data={\n", " 'Change': win_change,\n", " 'Rand': win_rand,\n", " 'No change': win_nochange\n", " })\n", " color = ['#33CFA5', 'gray', '#F06A6A']\n", " data = []\n", " for i in range(3):\n", " trace = {\n", " \"type\": 'violin',\n", " \"y\": win_dist.iloc[:,i],\n", " \"name\": win_dist.columns[i],\n", " \"box\": {\"visible\": True},\n", " \"points\": 'all',\n", " \"jitter\": jitter,\n", " \"pointpos\" : pointpos,\n", " \"meanline\": {\"visible\": True},\n", " \"line\": {\"color\": color[i]}\n", " }\n", " data.append(trace)\n", "\n", " fig = {\n", " \"data\": data,\n", " \"layout\" : {\n", " \"title\": \"\",\n", " \"yaxis\": {\"zeroline\": False}\n", " }\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here you see a code block responsible for violin plotting. The procedure is similar to that we have already covered, though slighly different:\n", "\n", "1. Creating a dataframe with columns representing our data (with initial door change, without initial changing, and a random choice upon opening one of doors)\n", "2. Creating a list of colors which will be later mapped to the line keyword within trace.\n", "3. We initialize a loop over each of columns of our dataframe, and save trace as an element of a data list. Variable trace contains the following parameters of future figure:\n", "\n", " - **type **: a type of our figure ('violin' in our case)\n", " - **y**: data on vertical axis\n", " - **name**: a name shown in the figure\n", " - **box**: make a box\n", " - **points**: show data points\n", " - **jitter**: make points more visible (from 0 to 1)\n", " - **pointpos** : position of points in relation to violin graph\n", " - **meanline**: line showing mean value\n", " - **line**: here it is responsible for violin color \n", "4. Previously, we used go.Figure, which is a json object, and assigned it to fig variable, while this time we create such an object by hands." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Distribution **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " elif plot == 'dist':\n", " data = [change, nochange, rand]\n", " group_labels = ['change', 'no change', 'random']\n", " colors = ['#F06A6A', '#33CFA5', 'gray']\n", " xx=None\n", "\n", " fig = ff.create_distplot(data, \n", " group_labels, \n", " colors=colors,\n", " bin_size=bin_size, \n", " show_rug=show_rug)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code block is responsible for creating of a distribution plot. The procedure shown here is differ from that related to scatter, box or violin plots.\n", "\n", "1. We create a list of elements which are lists. In our case, the lists contain an information about our percentage of wins obtained under different conditions\n", "2. Next we define group_labels that will be shown in the figure. The order of labels corresponding to that in data list\n", "3. After, we set up a color to each list of our data in the same order as the that defined in data\n", "4. Parameter bin_size defines the size of histogram bins, while show_rug adds rug to a distribution plot\n", "5. Having created data, group_labels, colors, bin_size and show_rug, we use them as input parameters to ff.create_distplot imported from plotly.figure_factory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now see our **plotResult** function in action" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "games = list_N.copy() " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "#33CFA5" }, "name": "Change", "type": "scatter", "uid": "691b9867-7221-4826-b37f-a7cb5104e1af", "x": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 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