| \n", - " | Title | \n", - "Worldwide_Gross | \n", - "Production_Budget | \n", - "IMDB_Rating | \n", - "
|---|---|---|---|---|
| 0 | \n", - "The Land Girls | \n", - "146083.0 | \n", - "8000000.0 | \n", - "6.1 | \n", - "
| 1 | \n", - "First Love, Last Rites | \n", - "10876.0 | \n", - "300000.0 | \n", - "6.9 | \n", - "
| 2 | \n", - "I Married a Strange Person | \n", - "203134.0 | \n", - "250000.0 | \n", - "6.8 | \n", - "
| 3 | \n", - "Let's Talk About Sex | \n", - "373615.0 | \n", - "300000.0 | \n", - "NaN | \n", - "
| 4 | \n", - "Slam | \n", - "1087521.0 | \n", - "1000000.0 | \n", - "3.4 | \n", - "
__repr__\n",
- "\n",
- "In python, `__repr__` helps you get information about an object for logging and debugging. Below is what you get by default when you define a new class. (Note: we are using `store_attr`, which was discussed earlier)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<__main__.Test at 0x7ffcd766cee0>"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "class Test:\n",
- " def __init__(self, a, b=2, c=3): store_attr() # `store_attr` was discussed previously\n",
- " \n",
- "Test(1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can use [basic_repr](https://fastcore.fast.ai/basics.html#basic_repr) to quickly give us a more sensible default:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Test(a=2, b=2, c=3)"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "class Test:\n",
- " def __init__(self, a, b=2, c=3): store_attr() \n",
- " __repr__ = basic_repr('a,b,c')\n",
- " \n",
- "Test(2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "## Monkey Patching With A Decorator\n",
- "\n",
- "It can be convenient to [monkey patch](https://www.geeksforgeeks.org/monkey-patching-in-python-dynamic-behavior/) with a decorator, which is especially helpful when you want to patch an external library you are importing. We can use the [decorator @patch](https://fastcore.fast.ai/foundation.html#patch) from `fastcore.foundation` along with type hints like so:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "class MyClass(int): pass \n",
- "\n",
- "@patch\n",
- "def func(self:MyClass, a): return self+a\n",
- "\n",
- "mc = MyClass(3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now, `MyClass` has an additional method named `func`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "13"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mc.func(10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Still not convinced? I'll show you another example of this kind of patching in the next section."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "## A better pathlib.Path\n",
- "\n",
- "When you see [these extensions](https://fastcore.fast.ai/xtras.html#Extensions-to-Pathlib.Path) to pathlib.path you won't ever use vanilla pathlib again! A number of additional methods have been added to pathlib, such as:\n",
- "\n",
- "- `Path.readlines`: same as `with open('somefile', 'r') as f: f.readlines()`\n",
- "- `Path.read`: same as `with open('somefile', 'r') as f: f.read()`\n",
- "- `Path.save`: saves file as pickle\n",
- "- `Path.load`: loads pickle file\n",
- "- `Path.ls`: shows the contents of the path as a list. \n",
- "- etc.\n",
- "\n",
- "[Read more about this here](https://fastcore.fast.ai/xtras.html#Extensions-to-Pathlib.Path). Here is a demonstration of `ls`: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(#7) [Path('2020-09-01-fastcore.ipynb'),Path('README.md'),Path('fastcore_imgs'),Path('2020-02-20-test.ipynb'),Path('.ipynb_checkpoints'),Path('2020-02-21-introducing-fastpages.ipynb'),Path('my_icons')]"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from fastcore.utils import *\n",
- "from pathlib import Path\n",
- "p = Path('.')\n",
- "p.ls() # you don't get this with vanilla Pathlib.Path!!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Wait! What's going on here? We just imported `pathlib.Path` - why are we getting this new functionality? Thats because we imported the `fastcore.utils` module, which patches this module via the `@patch` decorator discussed earlier. Just to drive the point home on why the `@patch` decorator is useful, I'll go ahead and add another method to `Path` right now:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'This is fun!'"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "@patch\n",
- "def fun(self:Path): return \"This is fun!\"\n",
- "\n",
- "p.fun()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "That is magical, right? I know! That's why I'm writing about it!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "\n",
- "## An Even More Concise Way To Create Lambdas\n",
- "\n",
- "\n",
- "`Self`, with an uppercase S, is an even more concise way to create lambdas that are calling methods on an object. For example, let's create a lambda for taking the sum of a Numpy array:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr=np.array([5,4,3,2,1])\n",
- "f = lambda a: a.sum()\n",
- "assert f(arr) == 15"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can use `Self` in the same way:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [],
- "source": [
- "f = Self.sum()\n",
- "assert f(arr) == 15"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's create a lambda that does a groupby and max of a Pandas dataframe:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "| \n", - " | Another Column | \n", - "
|---|---|
| Some Column | \n", - "\n", - " |
| a | \n", - "6 | \n", - "
| b | \n", - "60 | \n", - "