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DavoudTaghawiNejad/ABCE.ipynb

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ABCE.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import abce"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simulation = abce.Simulation(name='ipythonsimulation', processes=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating the Agent\n",
"We create a simple agents and intialize it. The agent needs to inherit from abce.Agents. *The `__init__` function should not be overwritten, instead create an `init` function*. The init function will be called during initialisation.\n",
"The agents gets two parameters. All agents get the parameters we will specify in the next round. From agent_parameters the agents get only agent_parameters[id], where id is the number of the agent.\n",
"Agents have for now only one function. They can say their name."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Agent(abce.Agent):\n",
" def init(self, parameters, agent_parameter):\n",
" self.family_name = agent_parameter\n",
" self.world_size = parameters['world_size']\n",
" def say(self):\n",
" print(\"hello I am %s my id %i and my group is '%s', it is the %i round\" % (self.family_name, self.id, self.group, self.round))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"agents = simulation.build_agents(Agent, 'agent', \n",
" parameters={\"world_size\": 10}, \n",
" agent_parameters=['fred', 'astaire', 'altair', 'deurich'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Round0\n",
"hello I am fred my id 0 and my group is 'agent', it is the 0 round\n",
"hello I am astaire my id 1 and my group is 'agent', it is the 0 round\n",
"hello I am altair my id 2 and my group is 'agent', it is the 0 round\n",
"hello I am deurich my id 3 and my group is 'agent', it is the 0 round\n",
"\r",
"Round1\n",
"hello I am fred my id 0 and my group is 'agent', it is the 1 round\n",
"hello I am astaire my id 1 and my group is 'agent', it is the 1 round\n",
"hello I am altair my id 2 and my group is 'agent', it is the 1 round\n",
"hello I am deurich my id 3 and my group is 'agent', it is the 1 round\n",
"\r",
"Round2\n",
"hello I am fred my id 0 and my group is 'agent', it is the 2 round\n",
"hello I am astaire my id 1 and my group is 'agent', it is the 2 round\n",
"hello I am altair my id 2 and my group is 'agent', it is the 2 round\n",
"hello I am deurich my id 3 and my group is 'agent', it is the 2 round\n",
"\r",
"Round3\n",
"hello I am fred my id 0 and my group is 'agent', it is the 3 round\n",
"hello I am astaire my id 1 and my group is 'agent', it is the 3 round\n",
"hello I am altair my id 2 and my group is 'agent', it is the 3 round\n",
"hello I am deurich my id 3 and my group is 'agent', it is the 3 round\n",
"\r",
"Round4\n",
"hello I am fred my id 0 and my group is 'agent', it is the 4 round\n",
"hello I am astaire my id 1 and my group is 'agent', it is the 4 round\n",
"hello I am altair my id 2 and my group is 'agent', it is the 4 round\n",
"hello I am deurich my id 3 and my group is 'agent', it is the 4 round\n"
]
}
],
"source": [
"for r in range(5):\n",
" simulation.advance_time(r)\n",
" agents.say()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have created an initial agent, and checked the four attributes an agent has. The current round in self.round. His ID and group as well as one variable we assigned ourselfs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Giving a Good\n",
"We create 5 agents, the first one has a balls the agents pass the ball around."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Kid(abce.Agent):\n",
" def init(self, parameters, agent_parameters):\n",
" self.num_kids = parameters['num_kids']\n",
" if self.id == 0:\n",
" self.create('ball', 1)\n",
" \n",
" def whether_I_have_the_ball(self):\n",
" if self.possession('ball') > 0:\n",
" print('*')\n",
" else:\n",
" print('.')\n",
" \n",
" def give_the_ball_to_the_next_kid(self):\n",
" next_kid = (self.id + 1) % self.num_kids # the id of the next kid, if I am the last the first kid\n",
" if self.possession('ball') >= 1:\n",
" self.give('kid', next_kid, good='ball', quantity=1)\n",
" \n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"self.create, creates an object. self.possession, checks how much of one object I have. self.give, gives an object to another agent, specied by its group name and its id."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"num_kids = 5"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simulation = abce.Simulation(name='ipythonsimulation', processes=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"kids = simulation.build_agents(Kid, 'kid', num_kids,\n",
" parameters={\"num_kids\": num_kids}) \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Round0\n",
"*\n",
".\n",
".\n",
".\n",
".\n",
"\r",
"Round1\n",
".\n",
"*\n",
".\n",
".\n",
".\n",
"\r",
"Round2\n",
".\n",
".\n",
"*\n",
".\n",
".\n",
"\r",
"Round3\n",
".\n",
".\n",
".\n",
"*\n",
".\n",
"\r",
"Round4\n",
".\n",
".\n",
".\n",
".\n",
"*\n",
"\r",
"Round5\n",
"*\n",
".\n",
".\n",
".\n",
".\n",
"\r",
"Round6\n",
".\n",
"*\n",
".\n",
".\n",
".\n"
]
}
],
"source": [
"for r in range(7):\n",
" simulation.advance_time(r)\n",
" kids.whether_I_have_the_ball()\n",
" kids.give_the_ball_to_the_next_kid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Well in every school yard we have a drug dealer.\n",
"We will illustrate this to explain how trade works.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randrange"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class NewKid(abce.Agent):\n",
" def init(self, parameters, agent_parameters):\n",
" self.num_dealers = parameters['num_dealers']\n",
" self.create('money', 100) # don't we all wish you'd this function in real live?\n",
" \n",
" def buy_drugs(self):\n",
" drug_dealer_id = randrange(self.num_dealers)\n",
" self.buy('drug_dealer', drug_dealer_id, good='drugs', quantity=1, price=10)\n",
" \n",
" def print_possessions(self):\n",
" print(' ' + self.group + str(dict(self.possessions())))\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The new kids, approach a random drug dealer and offer him 10 bugs."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class DrugDealer(abce.Agent):\n",
" def init(self, parameters, agent_parameters):\n",
" self.create('drugs', 1)\n",
" \n",
" def sell_to_customers(self):\n",
" for offer in self.get_offers('drugs'):\n",
" if offer.price >= 10 and self.possession('drugs') > 1:\n",
" self.accept(offer)\n",
" \n",
" def print_possessions(self):\n",
" print(' ' + self.group + str(dict(self.possessions())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drug dealer look at all the sell offers they get and decide to sell only to those kids that are willing to give them at least 10 dollars."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simulation_parameters={'num_dealers': 1, 'num_customers': 1, 'rounds': 2}"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simulation = abce.Simulation(name='school_yard', processes=1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"drug_dealers = simulation.build_agents(DrugDealer, 'drug_dealer', number=simulation_parameters['num_dealers'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"builds 10 drug dealers."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"customers = simulation.build_agents(NewKid, 'customer', number=simulation_parameters['num_customers'], parameters=simulation_parameters)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"kids = drug_dealers + customers"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Groups of agents can be merged to 'super' groups. We will print the amount of drugs and money all kids have for each of the two kids"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Round0\n",
"Customer offers 10 dollar:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 90.0}\n",
"Drug Dealer accepts or rejects the offer:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 100.0}\n",
"\n",
"\r",
"Round1\n",
"Customer offers 10 dollar:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 90.0}\n",
"Drug Dealer accepts or rejects the offer:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 100.0}\n",
"\n"
]
}
],
"source": [
"for r in range(simulation_parameters['rounds']):\n",
" simulation.advance_time(r)\n",
" print('Customer offers 10 dollar:')\n",
" customers.buy_drugs()\n",
" kids.print_possessions()\n",
" print('Drug Dealer accepts or rejects the offer:')\n",
" drug_dealers.sell_to_customers()\n",
" kids.print_possessions()\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When looking at round one one can see that after the customer offered 10 dollars, the 10 dollars are not available to him util the deal has either been accepted or rejected. After the drug dealer accepts the offer in the 0 round. The money is transfered to the drug dealer and the drugs to the customer.\n",
"\n",
"In round 1, where the drug dealer runs out of drugs the 10 dollars go back to the customer.\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Round0\n",
"Customer offers 10 dollar:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 90.0}\n",
"Drug Dealer accepts or rejects the offer:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 100.0}\n",
"\n",
"\r",
"Round1\n",
"Customer offers 10 dollar:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 90.0}\n",
"Drug Dealer accepts or rejects the offer:\n",
" drug_dealer{'money': 0, 'drugs': 1.0}\n",
" customer{'money': 100.0}\n",
"\n"
]
}
],
"source": [
"for r in range(simulation_parameters['rounds']):\n",
" simulation.advance_time(r)\n",
" print('Customer offers 10 dollar:')\n",
" customers.buy_drugs()\n",
" kids.print_possessions()\n",
" print('Drug Dealer accepts or rejects the offer:')\n",
" drug_dealers.sell_to_customers()\n",
" kids.print_possessions()\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lets capture data\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from math import sin\n",
"\n",
"class DataDealer(abce.Agent):\n",
" def init(self, simulation_parameters, agent_parameters):\n",
" self.count = 0\n",
" self.create('money', 0)\n",
"\n",
" def counting(self):\n",
" self.count += 1\n",
" self.curve = sin(self.count / 100)\n",
" self.create('money', self.curve * self.id)\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simulation = abce.Simulation(name='gatherdata', processes=1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simulation.aggregate('datadealer', variables=['count'])\n",
"simulation.panel('datadealer', possessions=['money'], variables=['curve'])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"datadealers = simulation.build_agents(DataDealer, 'datadealer', number=10)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Round0\n",
"Round1\n",
"Round2\n",
"Round3\n",
"Round4\n",
"Round5\n",
"Round6\n",
"Round7\n",
"Round8\n",
"Round9\n",
"Round10\n",
"Round11\n",
"Round12\n",
"Round13\n",
"Round14\n",
"Round15\n",
"Round16\n",
"Round17\n",
"Round18\n",
"Round19\n",
"Round20\n",
"Round21\n",
"Round22\n",
"Round23\n",
"Round24\n",
"Round25\n",
"Round26\n",
"Round27\n",
"Round28\n",
"Round29\n",
"Round30\n",
"Round31\n",
"Round32\n",
"Round33\n",
"Round34\n",
"Round35\n",
"Round36\n",
"Round37\n",
"Round38\n",
"Round39\n",
"Round40\n",
"Round41\n",
"Round42\n",
"Round43\n",
"Round44\n",
"Round45\n",
"Round46\n",
"Round47\n",
"Round48\n",
"Round49\n",
"Round50\n",
"Round51\n",
"Round52\n",
"Round53\n",
"Round54\n",
"Round55\n",
"Round56\n",
"Round57\n",
"Round58\n",
"Round59\n",
"Round60\n",
"Round61\n",
"Round62\n",
"Round63\n",
"Round64\n",
"Round65\n",
"Round66\n",
"Round67\n",
"Round68\n",
"Round69\n",
"Round70\n",
"Round71\n",
"Round72\n",
"Round73\n",
"Round74\n",
"Round75\n",
"Round76\n",
"Round77\n",
"Round78\n",
"Round79\n",
"Round80\n",
"Round81\n",
"Round82\n",
"Round83\n",
"Round84\n",
"Round85\n",
"Round86\n",
"Round87\n",
"Round88\n",
"Round89\n",
"Round90\n",
"Round91\n",
"Round92\n",
"Round93\n",
"Round94\n",
"Round95\n",
"Round96\n",
"Round97\n",
"Round98\n",
"Round99\n",
"\n",
"time only simulation 0.65\n",
"time with data and network 0.70\n",
"{\n",
" \"name\": \"gatherdata\",\n",
" \"random_seed\": 1498927895.321422\n",
"}\n",
"time with post processing 0.75\n"
]
}
],
"source": [
"with simulation.database:\n",
" for r in range(100):\n",
" simulation.advance_time(r)\n",
" datadealers.counting()\n",
" datadealers.aggregate()\n",
" datadealers.panel()\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/taghawi/Dropbox/workspace/abce/examples/ipython_tutorial/result/gatherdata_2017-07-01_17-51I\n"
]
},
{
"data": {
"text/plain": [
"['aggregate_datadealer.csv',\n",
" 'aggregate_datadealer_mean.csv',\n",
" 'aggregate_datadealer_std.csv',\n",
" 'aggregate_panel_datadealer.csv',\n",
" 'database.db',\n",
" 'description.txt',\n",
" 'panel_datadealer.csv']"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"print(simulation.path)\n",
"os.listdir(simulation.path)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from abce.show import show"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(simulation.path + '/panel_datadealer.csv')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>id</th>\n",
" <th>round</th>\n",
" <th>money</th>\n",
" <th>curve</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0.030000</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0.039999</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0.049999</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0.059999</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0.069999</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0.079999</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0.089999</td>\n",
" <td>0.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.029999</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0.059997</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0.089996</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0.119994</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0.149993</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0.179991</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>17</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0.209990</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>18</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0.239988</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>0.269987</td>\n",
" <td>0.019999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index id round money curve\n",
"0 0 0 0 0.000000 0.010000\n",
"1 1 1 0 0.010000 0.010000\n",
"2 2 2 0 0.020000 0.010000\n",
"3 3 3 0 0.030000 0.010000\n",
"4 4 4 0 0.039999 0.010000\n",
"5 5 5 0 0.049999 0.010000\n",
"6 6 6 0 0.059999 0.010000\n",
"7 7 7 0 0.069999 0.010000\n",
"8 8 8 0 0.079999 0.010000\n",
"9 9 9 0 0.089999 0.010000\n",
"10 10 0 1 0.000000 0.019999\n",
"11 11 1 1 0.029999 0.019999\n",
"12 12 2 1 0.059997 0.019999\n",
"13 13 3 1 0.089996 0.019999\n",
"14 14 4 1 0.119994 0.019999\n",
"15 15 5 1 0.149993 0.019999\n",
"16 16 6 1 0.179991 0.019999\n",
"17 17 7 1 0.209990 0.019999\n",
"18 18 8 1 0.239988 0.019999\n",
"19 19 9 1 0.269987 0.019999"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110c33748>"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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81mL+VI0QJmpq36es7CWcnX3IHPcGoaGzLLpuUVER27ZtQ61WM2XK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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110c437f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.pivot(index='round', columns='id', values='money').plot()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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