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add duck typing
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‎Chapter1/class.ipynb

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"kmeans_file_path = \"kmeans_model.pkl\"\n",
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"kmeans.to_pickle(kmeans_file_path)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "647169d6",
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"metadata": {},
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"source": [
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"### Embracing Duck Typing for Cleaner, More Adaptable Data Science Code"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6fce343a",
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"metadata": {},
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"source": [
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"Duck typing comes from the phrase \"If it walks like a duck and quacks like a duck, then it must be a duck.\" \n",
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"\n",
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"This lets you write flexible code that works with different types of objects, as long as they have the methods or attributes you're using.\n",
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"\n",
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"For data scientists, duck typing allows creating versatile functions that work with various data structures without explicitly checking their types.\n",
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"\n",
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"Here's a simple example:"
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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": 5,
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"id": "26fe0dfe",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mean: 3.0\n",
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"Standard Deviation: 1.4142135623730951\n",
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"Mean: 3.0\n",
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"Standard Deviation: 1.5811388300841898\n",
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"Mean: 3.0\n",
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"Standard Deviation: 1.4142135623730951\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"\n",
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"class CustomDataFrame:\n",
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" def __init__(self, data):\n",
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" self.data = data\n",
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"\n",
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" def mean(self):\n",
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" return np.mean(self.data)\n",
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"\n",
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" def std(self):\n",
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" return np.std(self.data)\n",
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"\n",
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"\n",
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"def analyze_data(data):\n",
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" print(f\"Mean: {data.mean()}\")\n",
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" print(f\"Standard Deviation: {data.std()}\")\n",
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"\n",
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"\n",
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"# These all work, thanks to duck typing\n",
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"numpy_array = np.array([1, 2, 3, 4, 5])\n",
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"pandas_series = pd.Series([1, 2, 3, 4, 5])\n",
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"custom_df = CustomDataFrame([1, 2, 3, 4, 5])\n",
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"\n",
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"analyze_data(numpy_array)\n",
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"analyze_data(pandas_series)\n",
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"analyze_data(custom_df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "96171599",
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"metadata": {},
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"source": [
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"In this example, the `analyze_data` function works with NumPy arrays, Pandas Series, and our custom `CustomDataFrame` class, because they all have `mean` and `std` methods. This flexibility is powerful in data science workflows where you might be working with various data structures.\n",
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"\n",
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"This flexibility is valuable in data science because:\n",
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"\n",
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"1. It saves time: You don't need separate functions for different data types.\n",
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"\n",
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"Bad example:\n"
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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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"id": "b84455c8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mean: 3.0\n",
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"Standard Deviation: 1.4142135623730951\n",
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"Mean: 3.0\n",
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"Standard Deviation: 1.5811388300841898\n",
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"Mean: 3.0\n",
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"Standard Deviation: 1.4142135623730951\n"
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]
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}
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],
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"source": [
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"def analyze_numpy_array(data):\n",
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" print(f\"Mean: {np.mean(data)}\")\n",
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" print(f\"Standard Deviation: {np.std(data)}\")\n",
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"\n",
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"def analyze_pandas_series(data):\n",
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" print(f\"Mean: {data.mean()}\")\n",
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" print(f\"Standard Deviation: {data.std()}\")\n",
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"\n",
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"def analyze_custom_df(data):\n",
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" print(f\"Mean: {data.mean()}\")\n",
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" print(f\"Standard Deviation: {data.std()}\")\n",
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"\n",
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"numpy_array = np.array([1, 2, 3, 4, 5])\n",
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"pandas_series = pd.Series([1, 2, 3, 4, 5])\n",
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"custom_df = CustomDataFrame([1, 2, 3, 4, 5])\n",
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"\n",
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"analyze_numpy_array(numpy_array)\n",
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"analyze_pandas_series(pandas_series)\n",
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"analyze_custom_df(custom_df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c569cd67",
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"metadata": {},
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"source": [
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"2. It's cleaner: You avoid lots of `if` statements checking for types."
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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": 3,
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"id": "e463b7aa",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mean: 3.0\n",
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"Standard Deviation: 1.4142135623730951\n",
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"Mean: 3.0\n",
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"Standard Deviation: 1.5811388300841898\n",
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"Mean: 3.0\n",
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"Standard Deviation: 1.4142135623730951\n"
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]
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}
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],
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"source": [
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"def analyze_data(data):\n",
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" if isinstance(data, np.ndarray):\n",
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" mean = np.mean(data)\n",
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" std = np.std(data)\n",
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" elif isinstance(data, pd.Series):\n",
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" mean = data.mean()\n",
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" std = data.std()\n",
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" elif isinstance(data, CustomDataFrame):\n",
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" mean = data.mean()\n",
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" std = data.std()\n",
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" else:\n",
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" raise TypeError(\"Unsupported data type\")\n",
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" \n",
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" print(f\"Mean: {mean}\")\n",
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" print(f\"Standard Deviation: {std}\")\n",
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"\n",
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"numpy_array = np.array([1, 2, 3, 4, 5])\n",
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"pandas_series = pd.Series([1, 2, 3, 4, 5])\n",
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"custom_df = CustomDataFrame([1, 2, 3, 4, 5])\n",
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"python_list = [1, 2, 3, 4, 5]\n",
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"\n",
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"analyze_data(numpy_array)\n",
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"analyze_data(pandas_series)\n",
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"analyze_data(custom_df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4591a347",
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"metadata": {},
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"source": [
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"3. It's more adaptable: Your code can handle new data types easily.\n",
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"\n",
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"Bad example:"
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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": 4,
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"id": "9bc290c8",
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"metadata": {},
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"outputs": [],
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"source": [
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"def analyze_data(data):\n",
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" if isinstance(data, np.ndarray):\n",
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" print(f\"Mean: {np.mean(data)}\")\n",
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" print(f\"Standard Deviation: {np.std(data)}\")\n",
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" elif isinstance(data, pd.Series):\n",
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" print(f\"Mean: {data.mean()}\")\n",
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" print(f\"Standard Deviation: {data.std()}\")\n",
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" elif isinstance(data, CustomDataFrame):\n",
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" print(f\"Mean: {data.mean()}\")\n",
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" print(f\"Standard Deviation: {data.std()}\")\n",
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" else:\n",
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" raise TypeError(\"Unsupported data type\")\n",
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"\n",
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"\n",
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"# If you introduce a new data type, you have to modify the function\n",
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"class NewDataType:\n",
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" def __init__(self, data):\n",
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" self.data = data\n",
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" def mean(self):\n",
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" return sum(self.data) / len(self.data)\n",
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" def std(self):\n",
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" mean = self.mean()\n",
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" return (sum((x - mean) ** 2 for x in self.data) / len(self.data)) ** 0.5\n",
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"\n",
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"new_data = NewDataType([1, 2, 3, 4, 5])\n",
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"# This will raise a TypeError\n",
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"# analyze_data(new_data)"
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]
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}
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],
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"metadata": {

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