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1751 lines
96 KiB
1751 lines
96 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##  Pandas for EDA\n",
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"by [@josephofiowa](https://twitter.com/josephofiowa)\n",
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" \n",
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"<!---\n",
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"This assignment was developed by Joseph Nelson\n",
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"\n",
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"Questions? Comments?\n",
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"1. Log an issue to this repo to alert me of a problem.\n",
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"2. Suggest an edit yourself by forking this repo, making edits, and submitting a pull request with your changes back to our master branch.\n",
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"3. Hit me up on Slack @sonylnagale\n",
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"--->"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pandas Unit Lab\n",
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"\n",
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"**Woo!** We've made it to the end of our Pandas Unit. Let's put our skills to the test.\n",
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"\n",
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"We're going to explore data from some of the top movies according to IMDB. This is a guided question-and-response lab where some areas are specific asks and others are open ended for you to explore.\n",
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"\n",
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"# Pandas Unit Lab\n",
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"\n",
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"**Woo!** We've made it to the end of our Pandas Unit. Let's put our skills to the test.\n",
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"\n",
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"We're going to explore data from some of the top movies according to IMDB. This is a guided question-and-response lab where some areas are specific asks and others are open ended for you to explore.\n",
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"\n",
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"#### Important!!!\n",
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"- <font color=\"red\">**There are two ways to do this lab!**</font>\n",
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" - The first way is to read in a dataset that _has already been pulled from the API and cleaned for you_ (`movies_rated.csv`). This is the recommended 'first-pass' way to do this lab.\n",
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" - _After you have completed the lab using the supplied_ `movies_rated.csv`, you can call the API yourself!\n",
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" - Calling the API yourself takes time! Be prepared to parse lots of JSON, read docs, etc. Consider this a take-home exercise if the students desire.\n",
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"\n",
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"In this lab, we will:\n",
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"- Use `movie_app.py` to obtain relevant moving rating data\n",
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"- Leverage Pandas to conduct exploratory data analysis, including:\n",
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" - Assess data integrity\n",
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" - Create exploratory visualizations\n",
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" - Produce insights on top actors/actresses across films\n",
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" \n",
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"Let's get going!\n",
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"\n",
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"In this lab, we will:\n",
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"- Use `movie_app.py` to obtain relevant moving rating data\n",
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"- Leverage Pandas to conduct exploratory data analysis, including:\n",
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" - Assess data integrity\n",
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" - Create exploratory visualizations\n",
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" - Produce insights on top actors/actresses across films\n",
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" \n",
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"Let's get going!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The Dataset\n",
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"\n",
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"We'll work with a dataset on the top [IMDB movies](https://www.imdb.com/search/title?count=100&groups=top_1000&sort=user_rating), as rated by IMDB.\n",
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"\n",
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"\n",
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"Specifically, we have a CSV that contains:\n",
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"- IMDB star rating\n",
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"- Movie title\n",
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"- Year\n",
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"- Content rating\n",
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"- Genre\n",
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"- Duration\n",
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"- Gross\n",
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"\n",
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"_[Details available at the above link]_\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import our necessary libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib as plt\n",
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"import re\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Read in the dataset\n",
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"\n",
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"First, read in the dataset, called `movies.csv` into a DataFrame called \"movies.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"movies = pd.read_csv('../data/movies.csv')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Check the dataset basics\n",
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"\n",
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"Let's first explore our dataset to verify we have what we expect."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Print the first five rows."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>title</th>\n",
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" <th>year</th>\n",
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" <th>content_rating</th>\n",
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" <th>genre</th>\n",
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" <th>duration</th>\n",
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" <th>gross</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>The Shawshank Redemption</td>\n",
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" <td>1994</td>\n",
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" <td>R</td>\n",
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" <td>Drama</td>\n",
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" <td>142</td>\n",
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" <td>1963330</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>The Godfather</td>\n",
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" <td>1972</td>\n",
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" <td>R</td>\n",
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" <td>Crime</td>\n",
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" <td>175</td>\n",
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" <td>28341469</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>The Dark Knight</td>\n",
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" <td>2008</td>\n",
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" <td>PG-13</td>\n",
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" <td>Action</td>\n",
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" <td>152</td>\n",
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" <td>1344258</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>The Godfather: Part II</td>\n",
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" <td>1974</td>\n",
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" <td>R</td>\n",
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" <td>Crime</td>\n",
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" <td>202</td>\n",
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" <td>134966411</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Pulp Fiction</td>\n",
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" <td>1994</td>\n",
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" <td>R</td>\n",
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" <td>Crime</td>\n",
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" <td>154</td>\n",
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" <td>1935047</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" title year content_rating genre duration \\\n",
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"0 The Shawshank Redemption 1994 R Drama 142 \n",
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"1 The Godfather 1972 R Crime 175 \n",
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"2 The Dark Knight 2008 PG-13 Action 152 \n",
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"3 The Godfather: Part II 1974 R Crime 202 \n",
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"4 Pulp Fiction 1994 R Crime 154 \n",
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"\n",
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" gross \n",
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"0 1963330 \n",
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"1 28341469 \n",
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"2 1344258 \n",
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"3 134966411 \n",
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"4 1935047 "
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"movies.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"How many rows and columns are in the datset?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"(79, 6)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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|
}
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],
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"source": [
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"movies.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"What are the column names?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"Index(['title', 'year', 'content_rating', 'genre', 'duration', 'gross'], dtype='object')\n"
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]
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}
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],
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"source": [
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"print(movies.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"How many unique genres are there?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
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|
"data": {
|
|
"text/plain": [
|
|
"12"
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|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
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|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
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"movies['genre'].nunique()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"How many movies are there per genre?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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|
"metadata": {},
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|
"outputs": [
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|
{
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"data": {
|
|
"text/plain": [
|
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"Crime 16\n",
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"Drama 14\n",
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"Action 11\n",
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"Adventure 9\n",
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"Drama 7\n",
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"Biography 5\n",
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"Animation 5\n",
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"Comedy 4\n",
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"Western 3\n",
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"Mystery 2\n",
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"Horror 2\n",
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"Comedy 1\n",
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"Name: genre, dtype: int64"
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]
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},
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|
"execution_count": 7,
|
|
"metadata": {},
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|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"movies['genre'].value_counts()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
|
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"source": [
|
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"## Only run the below cells if you've obtained an [API key!](http://www.omdbapi.com/apikey.aspx)<br>Otherwise, proceed to the `importing movies_rated.csv` section below."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"### Obtain more data (with an API call)!\n",
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"\n",
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"- Let's take advantage of our `OmdbAPI` module (stored in `./OmdbAPI.py`, if you'd like to look under the hood) to obtain data from OMDB API on movie ratings. This will enable us to answer the question: **How do other publication's scores compare to IMDB ratings?** Specifically, where do Rotten Tomato critics most disagree with IMDB reviews? \n",
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"- Using the OmdbAPI module, we will obtain the `Internet Movie Database`, the `Rotten Tomatoes`, and the `Metacritic` reviews on the top rated IMDB movies. We will store these ratings in new columns in a new `movies_rated` DataFrame. We have also stored the file locally at `./data/movies_rated.csv`."
|
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
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|
"source": [
|
|
"import OmdbAPI"
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]
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},
|
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{
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|
"cell_type": "code",
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|
"execution_count": 9,
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|
"metadata": {},
|
|
"outputs": [],
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|
"source": [
|
|
"# replace e54ad9e7 with your API key\n",
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"# this may take a minute\n",
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"movies_rated = OmdbAPI.Omdb(movies, 'e54ad9e7').get_ratings()"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>title</th>\n",
|
|
" <th>year</th>\n",
|
|
" <th>content_rating</th>\n",
|
|
" <th>genre</th>\n",
|
|
" <th>duration</th>\n",
|
|
" <th>gross</th>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>The Shawshank Redemption</td>\n",
|
|
" <td>1994</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" <td>142</td>\n",
|
|
" <td>1963330</td>\n",
|
|
" <td>9.3/10</td>\n",
|
|
" <td>91%</td>\n",
|
|
" <td>80/100</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>The Godfather</td>\n",
|
|
" <td>1972</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Crime</td>\n",
|
|
" <td>175</td>\n",
|
|
" <td>28341469</td>\n",
|
|
" <td>9.2/10</td>\n",
|
|
" <td>98%</td>\n",
|
|
" <td>100/100</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>The Dark Knight</td>\n",
|
|
" <td>2008</td>\n",
|
|
" <td>PG-13</td>\n",
|
|
" <td>Action</td>\n",
|
|
" <td>152</td>\n",
|
|
" <td>1344258</td>\n",
|
|
" <td>9.0/10</td>\n",
|
|
" <td>94%</td>\n",
|
|
" <td>82/100</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" title year content_rating genre duration \\\n",
|
|
"0 The Shawshank Redemption 1994 R Drama 142 \n",
|
|
"1 The Godfather 1972 R Crime 175 \n",
|
|
"2 The Dark Knight 2008 PG-13 Action 152 \n",
|
|
"\n",
|
|
" gross Internet Movie Database Rotten Tomatoes Metacritic \n",
|
|
"0 1963330 9.3/10 91% 80/100 \n",
|
|
"1 28341469 9.2/10 98% 100/100 \n",
|
|
"2 1344258 9.0/10 94% 82/100 "
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated.head(3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Just in case there were movies that the API was unable to get, let's drop nulls."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"movies_rated.dropna(inplace=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's get the ratings in the same float format using an apply function with some regular expressions. Note the use of .copy() when writing and reading from the same dataframe as a best practice."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>title</th>\n",
|
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" <th>year</th>\n",
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" <th>content_rating</th>\n",
|
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" <th>genre</th>\n",
|
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" <th>duration</th>\n",
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" <th>gross</th>\n",
|
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" <th>Internet Movie Database</th>\n",
|
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" <th>Rotten Tomatoes</th>\n",
|
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" <th>Metacritic</th>\n",
|
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" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
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" <tr>\n",
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" <th>0</th>\n",
|
|
" <td>The Shawshank Redemption</td>\n",
|
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" <td>1994</td>\n",
|
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" <td>R</td>\n",
|
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" <td>Drama</td>\n",
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" <td>142</td>\n",
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" <td>1963330</td>\n",
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" <td>9.3</td>\n",
|
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" <td>9.1</td>\n",
|
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" <td>8.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>The Godfather</td>\n",
|
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" <td>1972</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Crime</td>\n",
|
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" <td>175</td>\n",
|
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" <td>28341469</td>\n",
|
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" <td>9.2</td>\n",
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" <td>9.8</td>\n",
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" <td>10.0</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>2</th>\n",
|
|
" <td>The Dark Knight</td>\n",
|
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" <td>2008</td>\n",
|
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" <td>PG-13</td>\n",
|
|
" <td>Action</td>\n",
|
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" <td>152</td>\n",
|
|
" <td>1344258</td>\n",
|
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" <td>9.0</td>\n",
|
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" <td>9.4</td>\n",
|
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" <td>8.2</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
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"</div>"
|
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],
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"text/plain": [
|
|
" title year content_rating genre duration \\\n",
|
|
"0 The Shawshank Redemption 1994 R Drama 142 \n",
|
|
"1 The Godfather 1972 R Crime 175 \n",
|
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"2 The Dark Knight 2008 PG-13 Action 152 \n",
|
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"\n",
|
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" gross Internet Movie Database Rotten Tomatoes Metacritic \n",
|
|
"0 1963330 9.3 9.1 8.0 \n",
|
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"1 28341469 9.2 9.8 10.0 \n",
|
|
"2 1344258 9.0 9.4 8.2 "
|
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]
|
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},
|
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"execution_count": 12,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated['Rotten Tomatoes'] = movies_rated['Rotten Tomatoes'].copy().apply(lambda x: float(re.match('\\d{1,}', x)[0])/10)\n",
|
|
"movies_rated['Internet Movie Database'] = movies_rated['Internet Movie Database'].copy().apply(lambda x: float(re.match('(\\S+)\\/', x)[1]))\n",
|
|
"movies_rated['Metacritic'] = movies_rated['Metacritic'].copy().apply(lambda x: float(re.match('(\\S+)\\/', x)[1])/10)\n",
|
|
"movies_rated.head(3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Finally, let's write the cleaned result to a local file so we don't have to call the API again and risk exceeding our daily limit."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"movies_rated.to_csv('./movies_rated.csv', index=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Importing `movies_rated.csv`\n",
|
|
"\n",
|
|
"If you just called the API in the previous section, you can skip this and proceed to the `exploratory data analysis` section."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's read in the cleaned, rated `movies_rated.csv` file, which was included with this repo just in case you couldn't call the API."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
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"metadata": {},
|
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"outputs": [
|
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{
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"data": {
|
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
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" <th>title</th>\n",
|
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" <th>year</th>\n",
|
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" <th>content_rating</th>\n",
|
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" <th>genre</th>\n",
|
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" <th>duration</th>\n",
|
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" <th>gross</th>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>The Shawshank Redemption</td>\n",
|
|
" <td>1994</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" <td>142</td>\n",
|
|
" <td>1963330</td>\n",
|
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" <td>9.3</td>\n",
|
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" <td>9.1</td>\n",
|
|
" <td>8.0</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>The Godfather</td>\n",
|
|
" <td>1972</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Crime</td>\n",
|
|
" <td>175</td>\n",
|
|
" <td>28341469</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" <td>9.8</td>\n",
|
|
" <td>10.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>The Dark Knight</td>\n",
|
|
" <td>2008</td>\n",
|
|
" <td>PG-13</td>\n",
|
|
" <td>Action</td>\n",
|
|
" <td>152</td>\n",
|
|
" <td>1344258</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>9.4</td>\n",
|
|
" <td>8.2</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" title year content_rating genre duration \\\n",
|
|
"0 The Shawshank Redemption 1994 R Drama 142 \n",
|
|
"1 The Godfather 1972 R Crime 175 \n",
|
|
"2 The Dark Knight 2008 PG-13 Action 152 \n",
|
|
"\n",
|
|
" gross Internet Movie Database Rotten Tomatoes Metacritic \n",
|
|
"0 1963330 9.3 9.1 8.0 \n",
|
|
"1 28341469 9.2 9.8 10.0 \n",
|
|
"2 1344258 9.0 9.4 8.2 "
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated = pd.read_csv('../data/movies_rated.csv')\n",
|
|
"movies_rated.head(3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Check our datatypes. Notice anything potentially problematic?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"title object\n",
|
|
"year int64\n",
|
|
"content_rating object\n",
|
|
"genre object\n",
|
|
"duration int64\n",
|
|
"gross int64\n",
|
|
"Internet Movie Database float64\n",
|
|
"Rotten Tomatoes float64\n",
|
|
"Metacritic float64\n",
|
|
"dtype: object"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated.dtypes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Exploratory data analysis\n",
|
|
"\n",
|
|
"Let's transition to asking and answering some questions with our data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"What are the top five R-Rated movies?\n",
|
|
"\n",
|
|
"*hint: Boolean filters needed! Then sorting!*"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>title</th>\n",
|
|
" <th>year</th>\n",
|
|
" <th>content_rating</th>\n",
|
|
" <th>genre</th>\n",
|
|
" <th>duration</th>\n",
|
|
" <th>gross</th>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>The Shawshank Redemption</td>\n",
|
|
" <td>1994</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" <td>142</td>\n",
|
|
" <td>1963330</td>\n",
|
|
" <td>9.3</td>\n",
|
|
" <td>9.1</td>\n",
|
|
" <td>8.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>The Godfather</td>\n",
|
|
" <td>1972</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Crime</td>\n",
|
|
" <td>175</td>\n",
|
|
" <td>28341469</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" <td>9.8</td>\n",
|
|
" <td>10.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>The Godfather: Part II</td>\n",
|
|
" <td>1974</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Crime</td>\n",
|
|
" <td>202</td>\n",
|
|
" <td>134966411</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>9.7</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Schindler's List</td>\n",
|
|
" <td>1993</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Biography</td>\n",
|
|
" <td>195</td>\n",
|
|
" <td>534858444</td>\n",
|
|
" <td>8.9</td>\n",
|
|
" <td>9.7</td>\n",
|
|
" <td>9.3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>The Good, the Bad and the Ugly</td>\n",
|
|
" <td>1966</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Western</td>\n",
|
|
" <td>178</td>\n",
|
|
" <td>57300000</td>\n",
|
|
" <td>8.9</td>\n",
|
|
" <td>9.7</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" title year content_rating genre \\\n",
|
|
"0 The Shawshank Redemption 1994 R Drama \n",
|
|
"1 The Godfather 1972 R Crime \n",
|
|
"3 The Godfather: Part II 1974 R Crime \n",
|
|
"5 Schindler's List 1993 R Biography \n",
|
|
"7 The Good, the Bad and the Ugly 1966 R Western \n",
|
|
"\n",
|
|
" duration gross Internet Movie Database Rotten Tomatoes Metacritic \n",
|
|
"0 142 1963330 9.3 9.1 8.0 \n",
|
|
"1 175 28341469 9.2 9.8 10.0 \n",
|
|
"3 202 134966411 9.0 9.7 9.0 \n",
|
|
"5 195 534858444 8.9 9.7 9.3 \n",
|
|
"7 178 57300000 8.9 9.7 9.0 "
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated[movies_rated.content_rating == 'R'].sort_values(by='Internet Movie Database', ascending=False).head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"What is the average Rotten Tomato score for the top IMDB films?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"9.087341772151897"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated['Rotten Tomatoes'].mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"What is the Five Number Summary like for top rated films as per IMDB? Is it skewed?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"count 79.000000\n",
|
|
"mean 8.537975\n",
|
|
"std 0.222056\n",
|
|
"min 8.300000\n",
|
|
"25% 8.400000\n",
|
|
"50% 8.500000\n",
|
|
"75% 8.600000\n",
|
|
"max 9.300000\n",
|
|
"Name: Internet Movie Database, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated['Internet Movie Database'].describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The average is *slightly* higher than the median, so there's a small positive skew."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Create your own question...then answer it!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>year</th>\n",
|
|
" <th>duration</th>\n",
|
|
" <th>gross</th>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>year</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.145930</td>\n",
|
|
" <td>-0.107644</td>\n",
|
|
" <td>-0.044124</td>\n",
|
|
" <td>-0.479430</td>\n",
|
|
" <td>-0.487070</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>duration</th>\n",
|
|
" <td>0.145930</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.098006</td>\n",
|
|
" <td>0.416829</td>\n",
|
|
" <td>-0.088653</td>\n",
|
|
" <td>-0.020531</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>gross</th>\n",
|
|
" <td>-0.107644</td>\n",
|
|
" <td>0.098006</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.146099</td>\n",
|
|
" <td>-0.019891</td>\n",
|
|
" <td>-0.038350</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <td>-0.044124</td>\n",
|
|
" <td>0.416829</td>\n",
|
|
" <td>0.146099</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.062015</td>\n",
|
|
" <td>0.261009</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <td>-0.479430</td>\n",
|
|
" <td>-0.088653</td>\n",
|
|
" <td>-0.019891</td>\n",
|
|
" <td>0.062015</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.765957</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" <td>-0.487070</td>\n",
|
|
" <td>-0.020531</td>\n",
|
|
" <td>-0.038350</td>\n",
|
|
" <td>0.261009</td>\n",
|
|
" <td>0.765957</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
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],
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"text/plain": [
|
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" year duration gross \\\n",
|
|
"year 1.000000 0.145930 -0.107644 \n",
|
|
"duration 0.145930 1.000000 0.098006 \n",
|
|
"gross -0.107644 0.098006 1.000000 \n",
|
|
"Internet Movie Database -0.044124 0.416829 0.146099 \n",
|
|
"Rotten Tomatoes -0.479430 -0.088653 -0.019891 \n",
|
|
"Metacritic -0.487070 -0.020531 -0.038350 \n",
|
|
"\n",
|
|
" Internet Movie Database Rotten Tomatoes Metacritic \n",
|
|
"year -0.044124 -0.479430 -0.487070 \n",
|
|
"duration 0.416829 -0.088653 -0.020531 \n",
|
|
"gross 0.146099 -0.019891 -0.038350 \n",
|
|
"Internet Movie Database 1.000000 0.062015 0.261009 \n",
|
|
"Rotten Tomatoes 0.062015 1.000000 0.765957 \n",
|
|
"Metacritic 0.261009 0.765957 1.000000 "
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"# correlation between star rating and Rotten Tomato rating?\n",
|
|
"movies_rated.corr()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Challenge:** Create a dataframe that is the ratio between Rotten Tomato rating vs IMDB rating. What film has the highest IMDB : Rotten Tomato ratio? The lowest?\n",
|
|
"\n",
|
|
"*[skip this if you are low on time]*"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>Ratings Ratio</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>1.021978</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0.938776</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>0.957447</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
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],
|
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"text/plain": [
|
|
" Ratings Ratio\n",
|
|
"0 1.021978\n",
|
|
"1 0.938776\n",
|
|
"2 0.957447"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"rr = pd.DataFrame(movies_rated['Internet Movie Database'] / movies_rated['Rotten Tomatoes'], columns=['Ratings Ratio'])\n",
|
|
"rr.head(3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Top 3 ratings ratio movies (rated higher on IMBD compared to Rotten Tomatoes)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
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"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" }\n",
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" vertical-align: top;\n",
|
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" }\n",
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>title</th>\n",
|
|
" <th>year</th>\n",
|
|
" <th>content_rating</th>\n",
|
|
" <th>genre</th>\n",
|
|
" <th>duration</th>\n",
|
|
" <th>gross</th>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" <th>Ratings Ratio</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>Forrest Gump</td>\n",
|
|
" <td>1994</td>\n",
|
|
" <td>PG-13</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" <td>142</td>\n",
|
|
" <td>1401164</td>\n",
|
|
" <td>8.8</td>\n",
|
|
" <td>7.2</td>\n",
|
|
" <td>8.2</td>\n",
|
|
" <td>1.222222</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>Interstellar</td>\n",
|
|
" <td>2014</td>\n",
|
|
" <td>PG-13</td>\n",
|
|
" <td>Adventure</td>\n",
|
|
" <td>169</td>\n",
|
|
" <td>315544750</td>\n",
|
|
" <td>8.6</td>\n",
|
|
" <td>7.1</td>\n",
|
|
" <td>7.4</td>\n",
|
|
" <td>1.211268</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>The Intouchables</td>\n",
|
|
" <td>2011</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Biography</td>\n",
|
|
" <td>112</td>\n",
|
|
" <td>1059654</td>\n",
|
|
" <td>8.5</td>\n",
|
|
" <td>7.4</td>\n",
|
|
" <td>5.7</td>\n",
|
|
" <td>1.148649</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" title year content_rating genre duration gross \\\n",
|
|
"12 Forrest Gump 1994 PG-13 Drama 142 1401164 \n",
|
|
"19 Interstellar 2014 PG-13 Adventure 169 315544750 \n",
|
|
"42 The Intouchables 2011 R Biography 112 1059654 \n",
|
|
"\n",
|
|
" Internet Movie Database Rotten Tomatoes Metacritic Ratings Ratio \n",
|
|
"12 8.8 7.2 8.2 1.222222 \n",
|
|
"19 8.6 7.1 7.4 1.211268 \n",
|
|
"42 8.5 7.4 5.7 1.148649 "
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated.merge(rr, left_index=True, right_index=True).sort_values('Ratings Ratio', ascending=False).head(3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Bottom 3 ratings ratio movies (rated lower on IMBD compared to Rotten Tomatoes)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
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"<div>\n",
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|
"<style scoped>\n",
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|
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|
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|
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|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>title</th>\n",
|
|
" <th>year</th>\n",
|
|
" <th>content_rating</th>\n",
|
|
" <th>genre</th>\n",
|
|
" <th>duration</th>\n",
|
|
" <th>gross</th>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" <th>Ratings Ratio</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>66</th>\n",
|
|
" <td>Toy Story 3</td>\n",
|
|
" <td>2010</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Animation</td>\n",
|
|
" <td>103</td>\n",
|
|
" <td>499468</td>\n",
|
|
" <td>8.3</td>\n",
|
|
" <td>9.9</td>\n",
|
|
" <td>9.2</td>\n",
|
|
" <td>0.838384</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>74</th>\n",
|
|
" <td>L.A. Confidential</td>\n",
|
|
" <td>1997</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Crime</td>\n",
|
|
" <td>138</td>\n",
|
|
" <td>13182281</td>\n",
|
|
" <td>8.3</td>\n",
|
|
" <td>9.9</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>0.838384</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>63</th>\n",
|
|
" <td>Toy Story</td>\n",
|
|
" <td>1995</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Animation</td>\n",
|
|
" <td>81</td>\n",
|
|
" <td>83471511</td>\n",
|
|
" <td>8.3</td>\n",
|
|
" <td>10.0</td>\n",
|
|
" <td>9.5</td>\n",
|
|
" <td>0.830000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" title year content_rating genre duration gross \\\n",
|
|
"66 Toy Story 3 2010 R Animation 103 499468 \n",
|
|
"74 L.A. Confidential 1997 R Crime 138 13182281 \n",
|
|
"63 Toy Story 1995 R Animation 81 83471511 \n",
|
|
"\n",
|
|
" Internet Movie Database Rotten Tomatoes Metacritic Ratings Ratio \n",
|
|
"66 8.3 9.9 9.2 0.838384 \n",
|
|
"74 8.3 9.9 9.0 0.838384 \n",
|
|
"63 8.3 10.0 9.5 0.830000 "
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated.merge(rr, left_index=True, right_index=True).sort_values('Ratings Ratio', ascending=False).tail(3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Exploratory data analysis with visualizations\n",
|
|
"\n",
|
|
"For each of these prompts, create a plot to visualize the answer. Consider what plot is *most appropriate* to explore the given prompt.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"What is the relationship between IMDB ratings and Rotten Tomato ratings?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f53393ce198>"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated.plot(kind='scatter', x='Internet Movie Database', y='Rotten Tomatoes')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"What is the relationship between IMDB rating and movie duration?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f5339084da0>"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated.plot(kind='scatter', x='duration', y='Internet Movie Database')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"How many movies are there in each genre category? (Remember to create a plot here)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f5339006f98>"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated['genre'].value_counts().plot(kind='bar', color='dodgerblue')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"What does the distribution of Rotten Tomatoes ratings look like?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f5338f93780>"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated['Rotten Tomatoes'].plot(kind='hist', bins=15)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Bonus\n",
|
|
"\n",
|
|
"There are many things left unexplored! Consider investigating something about gross revenue and genres."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f5338f69f60>"
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"movies_rated['gross'].plot(kind='hist', bins=15, color='dodgerblue')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>title</th>\n",
|
|
" <th>year</th>\n",
|
|
" <th>content_rating</th>\n",
|
|
" <th>genre</th>\n",
|
|
" <th>duration</th>\n",
|
|
" <th>gross</th>\n",
|
|
" <th>Internet Movie Database</th>\n",
|
|
" <th>Rotten Tomatoes</th>\n",
|
|
" <th>Metacritic</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>One Flew Over the Cuckoo's Nest</td>\n",
|
|
" <td>1975</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" <td>133</td>\n",
|
|
" <td>665845272</td>\n",
|
|
" <td>8.7</td>\n",
|
|
" <td>9.4</td>\n",
|
|
" <td>8.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>Schindler's List</td>\n",
|
|
" <td>1993</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Biography</td>\n",
|
|
" <td>195</td>\n",
|
|
" <td>534858444</td>\n",
|
|
" <td>8.9</td>\n",
|
|
" <td>9.7</td>\n",
|
|
" <td>9.3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>Fight Club</td>\n",
|
|
" <td>1999</td>\n",
|
|
" <td>R</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" <td>139</td>\n",
|
|
" <td>377845905</td>\n",
|
|
" <td>8.8</td>\n",
|
|
" <td>7.9</td>\n",
|
|
" <td>6.6</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" title year content_rating genre \\\n",
|
|
"17 One Flew Over the Cuckoo's Nest 1975 R Drama \n",
|
|
"5 Schindler's List 1993 R Biography \n",
|
|
"13 Fight Club 1999 R Drama \n",
|
|
"\n",
|
|
" duration gross Internet Movie Database Rotten Tomatoes Metacritic \n",
|
|
"17 133 665845272 8.7 9.4 8.0 \n",
|
|
"5 195 534858444 8.9 9.7 9.3 \n",
|
|
"13 139 377845905 8.8 7.9 6.6 "
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# top 10 grossing films\n",
|
|
"movies_rated.sort_values(by='gross', ascending=False).head(3)"
|
|
]
|
|
},
|
|
{
|
|
"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": "3.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|