04-Apply.ipynb

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    "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": [
    "# cast = pd.read_csv('data/cast.csv', index_col=None)\n",
    "# cast.head()"
   ]
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    "# 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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    "### 1. Define a year as a \"Superman year\" whose films feature more Superman characters than Batman. How many years in film history have been Superman years? \n",
    "\n",
    "#### Use .apply() to make the comparison between number of movies in each year"
   ]
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    "> Hint:\n",
    "\n",
    "> Make two dataframes, one for superman roles and one for batman roles. Find the number of roles for each in each year, then merge them so that you have year, n_batman, n_superman. Then use an apply function to check the condition."
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    "### 2. Classify movies based on the number of roles into 'Small Cast' (1-5 roles), 'Medium Cast' (6-15 roles), and 'Large Cast' (more than 15 roles)\n",
    "\n",
    "> Hint: Group by title and year, then use a function and .apply() to classify. "
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    "### 3. Count the number of movies where the same actor or actress played multiple roles. Use .apply() to check if the same name is repeated in any movie's cast."
   ]
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    "### 4. Find movies with at least one character with a name starting with \"Dr.\". \n",
    "\n",
    "### Use apply to create a new column indicating whether a movie has such a character, and then count the number of these movies."
   ]
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    "### 5. Create a new column indicating whether each movie has a lead role (n=1) played by an actress. \n",
    "\n",
    "#### Use apply to achieve this, and then count the number of such movies."
   ]
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