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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Explore Movie Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2017-06-26 18:57:49 \n",
+ "\n",
+ "CPython 3.6.1\n",
+ "IPython 6.1.0\n",
+ "\n",
+ "pea 0.0.7\n",
+ "pandas 0.20.2\n",
+ "\n",
+ "compiler : MSC v.1900 64 bit (AMD64)\n",
+ "system : Windows\n",
+ "release : 7\n",
+ "machine : AMD64\n",
+ "processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel\n",
+ "CPU cores : 8\n",
+ "interpreter: 64bit\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import pandas as pd\n",
+ "import settings\n",
+ "import etl\n",
+ "\n",
+ "%matplotlib inline\n",
+ "\n",
+ "%load_ext watermark\n",
+ "%watermark -d -t -v -m -p pea,pandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "data = etl.Data()\n",
+ "data.load()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Available Columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
+ " 'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
+ " 'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
+ " 'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
+ " 'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
+ " 'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n",
+ " 'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n",
+ " 'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.movie.columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## plotting with IPlotter\n",
+ "\n",
+ "This example is using my own branch of IPlotter which builds the dictionary from a pandas DataFrame. Much less verbose, but can be done with the current version on PyPI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from iplotter import C3Plotter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "c3 = C3Plotter()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Timeseries of mean gross"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot_data = data.movie.groupby(['title_year']).mean()[['gross']].fillna(0)\n",
+ "c3.plot(plot_data, zoom=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "country_group = data.movie.groupby('country').count()['duration']\n",
+ "counts = country_group.values.tolist()\n",
+ "countries = country_group.index.values.tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from iplotter import PlotlyPlotter\n",
+ "from IPython.display import HTML\n",
+ "\n",
+ "plotly = PlotlyPlotter()\n",
+ "\n",
+ "c3_plotter = C3Plotter()\n",
+ "\n",
+ "plotly_chart = [{\n",
+ " \"type\": 'choropleth',\n",
+ " \"locationmode\": 'country names',\n",
+ " \"locations\": countries,\n",
+ " \"z\": counts,\n",
+ " \"zmin\": 0,\n",
+ " \"zmax\": max(counts),\n",
+ " \"colorscale\": [\n",
+ " [0, 'rgb(242,240,247)'], [0.2, 'rgb(218,218,235)'],\n",
+ " [0.4, 'rgb(188,189,220)'], [0.6, 'rgb(158,154,200)'],\n",
+ " [0.8, 'rgb(117,107,177)'], [1, 'rgb(84,39,143)']\n",
+ " ],\n",
+ " \"colorbar\": {\n",
+ " \"title\": 'Count',\n",
+ " \"thickness\": 10\n",
+ " },\n",
+ " \"marker\": {\n",
+ " \"line\": {\n",
+ " \"color\": 'rgb(255,255,255)',\n",
+ " \"width\": 2\n",
+ " }\n",
+ " }\n",
+ "}]\n",
+ "\n",
+ "plotly_layout = {\n",
+ " \"title\": 'Movie Counts by Country',\n",
+ " \"geo\": {\n",
+ " \"scope\": 'country names',\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "country_plot = plotly.plot(data=plotly_chart)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "variables": {
+ " country_plot ": ""
+ }
+ },
+ "source": [
+ "### Movies by Country\n",
+ "\n",
+ "{{ country_plot }}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "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
+}