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387 lines
8.0 KiB
387 lines
8.0 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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"In this lab, we will:\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": 2,
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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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"%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_rated.csv` into a DataFrame called \"movies.\" It's in the `../data` folder."
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 our datatypes. Do they make sense?"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"## Exploratory data analysis\n",
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"\n",
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"Let's transition to asking and answering some questions with our data."
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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 top five R-Rated movies?\n",
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"\n",
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"*hint: Boolean filters needed! Then sorting!*"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 is the average Rotten Tomato score for these films?"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 is the Five Number Summary like for these films as per IMDB? Is it skewed?"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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},
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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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"Create your own question...then answer it!"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"**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",
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"\n",
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"*[skip this if you are low on time]*"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"## Exploratory data analysis with visualizations\n",
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"\n",
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"For each of these prompts, create a plot to visualize the answer. Consider what plot is *most appropriate* to explore the given prompt.\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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"What is the relationship between IMDB ratings and Rotten Tomato ratings?"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 is the relationship between IMDB rating and movie duration?"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 in each genre category? (Remember to create a plot here)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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 does the distribution of Rotten Tomatoes ratings look like?"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"## Bonus\n",
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"\n",
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"There are many things left unexplored! Consider investigating something about gross revenue and genres."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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