IMDB-Studio.ipynb

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    "# Exploratory Data Analysis part 1 Studio\n",
    "\n",
    "## Data Analysis of IMDB movies data\n",
    "\n",
    "#### The studio will continue working through the [\"A Comprehensive Guide To Data Analysis using Pandas: Hands-On Data Analysis on IMDB movies data\"](https://www.analyticsvidhya.com/blog/2021/05/a-comprehensive-guide-to-data-analysis-using-pandas-hands-on-data-analysis-on-imdb-movies-data/) in the **Data Analysis of IMDB movies data** section\n",
    "\n",
    "#### Blocks have been created for your code\n",
    "\n",
    "#### Follow the link in the article to download the dataset from [Kaggle](https://www.kaggle.com/PromptCloudHQ/imdb-data).  Link contained within the article."
   ]
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   "source": [
    "### 1. Read data"
   ]
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   "source": [
    "# import pandas\n",
    "\n",
    "# create your data variabe using .read_csv \n",
    "\n",
    "# create your data_indexed varialbe here"
   ]
  },
  {
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   "source": [
    "# If you are having trouble reading your file, try finding the path.  \n",
    "    # Right click on your data set, on the pop-up menu select \"Copy Path\" then paste it into your read_csv() function."
   ]
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   "source": [
    "# Read data with specified explicit index\n",
    "\n",
    "\n",
    "\n",
    "#print(data_indexed)"
   ]
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   "source": [
    "### 2. View Data\n",
    "#### As we go through this section, write your own definitions of what each function does as a comment in the code box."
   ]
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  {
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   "source": [
    "# Use the head() function to view the first 5 rows of the data set\n",
    "\n"
   ]
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  {
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   "id": "3fffca4b-90f4-4f73-a9af-f0b02886f8b3",
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   "source": [
    "# Use the tail() function to view the last 5 rows of the data set\n",
    "\n"
   ]
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   "source": [
    "### 3. Understand basic information about the data\n",
    "#### As we go through this section, write your own definitions of what each function does as a comment in the code box."
   ]
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   "source": [
    "# The info() function: \n",
    "\n"
   ]
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   "source": [
    "# shape:\n",
    "\n",
    "\n",
    "# what information does shape provide?  what is the order?"
   ]
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   "source": [
    "# columns:\n",
    "\n",
    "\n",
    "# what information does columns provide?"
   ]
  },
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   "source": [
    "# The describe() function: \n",
    "\n"
   ]
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   "source": [
    "### 4. Data Selection -- Indexing and Slicing"
   ]
  },
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   "source": [
    "#  Extract data as series\n",
    "# define and print the variable 'genre'\n",
    "\n"
   ]
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   "source": [
    "# Extract the data as a dataframe and print it\n",
    "\n",
    "\n"
   ]
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  {
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    "# Extract data using rows\n",
    "\n",
    "# loc function\n",
    "\n"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "# iloc function\n",
    "\n"
   ]
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   "source": [
    "### 5. Data Selection - Based on Conditional Filtering"
   ]
  },
  {
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   "source": [
    "# Use the code block to select movies that have been release between 2010-2016, with a rating of less than 6.0, but generated a signifianct (top) revenue.\n",
    "\n"
   ]
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    "# Note: The article is expecting more than 1 Twilight movie at the time of publishing.  \n",
    "#To find the second Twilight movie, try manipulating the quantile value.  \n",
    "#How specific can you get?"
   ]
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