11. Group By.ipynb

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    "# Group By\n",
    "\n",
    "Any groupby operation involves one of the following operations on the original object. They are −\n",
    "\n",
    "- Splitting the Object\n",
    "\n",
    "- Applying a function\n",
    "\n",
    "- Combining the results\n",
    "\n",
    "In many situations, we split the data into sets and we apply some functionality on each subset. In the apply functionality, we can perform the following operations −\n",
    "\n",
    "- **Aggregation** − computing a summary statistic\n",
    "\n",
    "- **Transformation** − perform some group-specific operation\n",
    "\n",
    "- **Filtration** − discarding the data with some condition\n",
    "\n",
    "![](https://i.stack.imgur.com/sgCn1.jpg)"
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   "source": [
    "import pandas as pd"
   ]
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   "source": [
    "imdb_df = pd.read_csv(\"data/imdb_1000.csv\")"
   ]
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   "source": [
    "imdb_df.head()"
   ]
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   "outputs": [],
   "source": [
    "genre_groups = imdb_df.groupby('genre')"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_groups"
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "genre_groups.get_group('Adventure')"
   ]
  },
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    "genre_groups.aggregate('mean').sort_values('star_rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_groups['duration'].aggregate([np.max, np.min, np.mean, np.std])"
   ]
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   "source": [
    "imdb_df['avg_duration'] = genre_groups['duration'].transform(lambda x: x.mean())"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "imdb_df.head()"
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   "cell_type": "code",
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   "source": [
    "imdb_df['genre'].value_counts()"
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "genre_groups.filter(lambda x: x['duration'].mean() < 120)['genre'].value_counts()"
   ]
  }
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