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edit some notebooks
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‎Chapter1/good_practices.ipynb

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@@ -1519,12 +1519,16 @@
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"id": "9d24d6af",
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"metadata": {},
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"source": [
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"To simplify checking if a Python object is of different types, you can group those types into a tuple within an instance call."
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"The `isinstance()` function in Python is used to check if an object is an instance of a specified type or class. When checking for multiple types, we can optimize our code by using a tuple of types instead of multiple `isinstance()` calls or conditions.\n",
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"\n",
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"Let's break it down:\n",
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"\n",
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"1. Traditional approach (less efficient):"
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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": 6,
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"execution_count": 1,
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"id": "90c6d002",
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"metadata": {},
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"outputs": [
@@ -1533,21 +1537,31 @@
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"output_type": "stream",
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"text": [
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"True\n",
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"True\n"
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"True\n",
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"False\n"
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]
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}
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],
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"source": [
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"def is_number(num):\n",
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" return isinstance(num, int) or isinstance(num, float)\n",
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"\n",
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"print(is_number(2))\n",
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"print(is_number(1.5))"
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"print(is_number(2)) # True\n",
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"print(is_number(1.5)) # True\n",
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"print(is_number(\"2\")) # False"
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]
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},
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{
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"cell_type": "markdown",
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"id": "57d2acb4",
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"metadata": {},
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"source": [
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"2. Optimized approach using a tuple:"
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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": 9,
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"execution_count": 2,
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"id": "f29bba13",
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"metadata": {},
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@@ -1556,16 +1570,110 @@
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"output_type": "stream",
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"text": [
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"True\n",
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"True\n"
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"True\n",
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"False\n"
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]
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}
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],
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"source": [
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"def is_number(num):\n",
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" return isinstance(num, (int, float))\n",
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"\n",
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"print(is_number(2))\n",
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"print(is_number(1.5))"
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"print(is_number(2)) # True\n",
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"print(is_number(1.5)) # True\n",
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"print(is_number(\"2\")) # False"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d9b34fe6",
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"metadata": {},
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"source": [
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"Benefits of using a tuple:\n",
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"\n",
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"1. Conciseness: The code is more readable and compact.\n",
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"2. Performance: It's slightly more efficient, especially when checking against many types.\n",
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"3. Maintainability: Easier to add or remove types to check against."
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]
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},
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{
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"cell_type": "markdown",
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"id": "80fb0047",
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"metadata": {},
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"source": [
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"You can extend this concept to check for more 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": 7,
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"id": "1371324d",
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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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"True\n",
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"True\n",
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"True\n",
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"False\n"
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]
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}
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],
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"source": [
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"def is_sequence(obj):\n",
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" return isinstance(obj, (list, tuple, str))\n",
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"\n",
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"print(is_sequence([1, 2, 3])) # True\n",
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"print(is_sequence((1, 2, 3))) # True\n",
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"print(is_sequence(\"123\")) # True\n",
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"print(is_sequence(123)) # False"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7020a0d1",
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"metadata": {},
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"source": [
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"For broader type checking, use Python's abstract base classes:"
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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": 8,
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"id": "7036c0f7",
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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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"True\n",
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"True\n",
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"True\n",
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"False\n"
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]
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}
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],
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"source": [
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"from collections.abc import Sequence\n",
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"\n",
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"def is_sequence(obj):\n",
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" return isinstance(obj, Sequence)\n",
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"\n",
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"print(is_sequence([1, 2, 3])) # True\n",
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"print(is_sequence((1, 2, 3))) # True\n",
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"print(is_sequence(\"123\")) # True\n",
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"print(is_sequence(123)) # False"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b941c3df",
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"metadata": {},
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"source": [
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"In this case, we're checking if an object is either a Sequence (like lists, tuples, strings) or a Mapping (like dictionaries)."
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]
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},
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{

‎Chapter5/SQL.ipynb

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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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},

‎Chapter5/best_python_practice_tools.ipynb

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"hash": "484329849bb907480cd798e750759bc6f1d66c93f9e78e7055aa0a2c2de6b47b"
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},
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"kernelspec": {
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"display_name": "Python 3.8.9 ('venv': venv)",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},

‎Chapter5/better_pandas.ipynb

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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "2314aa9f",
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"metadata": {},
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{
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"metadata": {
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"celltoolbar": "Tags",
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"kernelspec": {
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"display_name": "venv",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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},

‎Chapter5/feature_engineer.ipynb

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‎Chapter5/feature_extraction.ipynb

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"application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 30;\n var nbb_unformatted_code = \"import numpy as np\\nfrom distfit import distfit\\n\\nX = np.random.normal(0, 3, 1000)\\n\\n# Initialize model\\ndist = distfit()\\n\\n# Find best theoretical distribution for empirical data X\\ndistribution = dist.fit_transform(X)\\ndist.plot()\";\n var nbb_formatted_code = \"import numpy as np\\nfrom distfit import distfit\\n\\nX = np.random.normal(0, 3, 1000)\\n\\n# Initialize model\\ndist = distfit()\\n\\n# Find best theoretical distribution for empirical data X\\ndistribution = dist.fit_transform(X)\\ndist.plot()\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ",
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" var nbb_formatted_code = \"import numpy as np\\nfrom distfit import distfit\\n\\nX = np.random.normal(0, 3, 1000)\\n\\n# Initialize model\\ndist = distfit()\\n\\n# Find best theoretical distribution for empirical data X\\ndistribution = dist.fit_transform(X)\\ndist.plot()\";\n",
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@@ -359,7 +376,24 @@
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"application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 6;\n var nbb_unformatted_code = \"import pandas as pd\\nfrom fastai.tabular.core import cont_cat_split\\n\\ndf = pd.DataFrame(\\n {\\n \\\"col1\\\": [1, 2, 3, 4, 5],\\n \\\"col2\\\": [\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\"],\\n \\\"col3\\\": [1.0, 2.0, 3.0, 4.0, 5.0],\\n }\\n)\\n\\ncont_names, cat_names = cont_cat_split(df)\\nprint(\\\"Continuous columns:\\\", cont_names)\\nprint(\\\"Categorical columns:\\\", cat_names)\";\n var nbb_formatted_code = \"import pandas as pd\\nfrom fastai.tabular.core import cont_cat_split\\n\\ndf = pd.DataFrame(\\n {\\n \\\"col1\\\": [1, 2, 3, 4, 5],\\n \\\"col2\\\": [\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\"],\\n \\\"col3\\\": [1.0, 2.0, 3.0, 4.0, 5.0],\\n }\\n)\\n\\ncont_names, cat_names = cont_cat_split(df)\\nprint(\\\"Continuous columns:\\\", cont_names)\\nprint(\\\"Categorical columns:\\\", cat_names)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ",
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" var nbb_formatted_code = \"cont_names, cat_names = cont_cat_split(df, max_card=3)\\nprint(\\\"Continuous columns:\\\", cont_names)\\nprint(\\\"Categorical columns:\\\", cat_names)\";\n",
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"data": {
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" var nbb_cell_id = 22;\n",
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@@ -1493,9 +1595,9 @@
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},
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"kernelspec": {
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"display_name": "Data-science",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "data-science"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
@@ -1507,7 +1609,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.6"
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"version": "3.11.6"
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},
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"toc": {
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"base_numbering": 1,

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