01-Accessing.ipynb

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   "source": [
    "import pandas as pd"
   ]
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   "source": [
    "### Download the data and load it to Pandas. \n",
    "\n",
    "You can find the data files in the README."
   ]
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   "source": [
    "# titles = pd.read_csv('data/titles.csv')\n",
    "# titles.head()"
   ]
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   "source": [
    "# cast = pd.read_csv('data/cast.csv', sep=\",\")\n",
    "# cast.head()"
   ]
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   "source": [
    "# release_dates = pd.read_csv('data/release_dates.csv', index_col=None,\n",
    "#                                       parse_dates=['date'], infer_datetime_format=True)\n",
    "# release_dates.head()"
   ]
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   "source": [
    "### 1. How many movies are listed in the titles dataframe?"
   ]
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   "source": [
    "### 2. What are the earliest two films listed in the titles dataframe?"
   ]
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   "source": [
    "### 3. What are the ten most common movie names of all time?"
   ]
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   "source": [
    "### 4. What are the 10 most common character names in movie history?"
   ]
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    "### 5. Count the number of films that have been released in each decade over the history of cinema.\n",
    "\n",
    "> Hint: You can make a new column to store what decade a movie is.\n",
    "\n",
    "> Hint2: If you're stuck on how to do this, expand the cell below."
   ]
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   "source": [
    "```python\n",
    "titles['decade'] = (titles['year'] // 10) * 10\n",
    "```"
   ]
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   "source": [
    "### 6. Retrieve the title and year of the first 10 rows in the titles dataframe."
   ]
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    "### 7. Get the character names and types for the 5th to the 15th rows (inclusive) in the cast dataframe."
   ]
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    "### 8. Extract the title and release date of the last 5 entries in the release_dates dataframe."
   ]
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    "### 9. Select the year and country of the 20th row in the release_dates dataframe."
   ]
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   "source": [
    "### 10. Print the name, character, and type for rows 100 to 105 (inclusive) in the cast dataframe."
   ]
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