Run notebooks with updates and custom kernel
This commit is contained in:
parent
79a2e45e49
commit
3125c82096
13 changed files with 102 additions and 114 deletions
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@ -536,9 +536,9 @@
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],
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],
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"metadata": {
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"metadata": {
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "intro-to-data-science",
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"language": "python",
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"language": "python",
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"name": "python3"
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"name": "intro-to-data-science"
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},
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},
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"language_info": {
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"language_info": {
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"codemirror_mode": {
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"codemirror_mode": {
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@ -550,7 +550,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.12.4"
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},
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},
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"toc": {
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"toc": {
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"base_numbering": 1,
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"base_numbering": 1,
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@ -149,9 +149,9 @@
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],
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],
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"metadata": {
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"metadata": {
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "intro-to-data-science",
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"language": "python",
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"language": "python",
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"name": "python3"
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"name": "intro-to-data-science"
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},
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},
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"language_info": {
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"language_info": {
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"codemirror_mode": {
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"codemirror_mode": {
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@ -163,7 +163,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.12.4"
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},
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},
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"toc": {
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"toc": {
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"base_numbering": 1,
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"base_numbering": 1,
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@ -507,7 +507,7 @@
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"\n",
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"\n",
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"The indented line constitues the `for`-loop's body. In the example, we simply take each of the numbers in `numbers`, one at a time, and add it to a `total` that is initialized at `0`. In other words, we calculate the sum of all the elements in `numbers`.\n",
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"The indented line constitues the `for`-loop's body. In the example, we simply take each of the numbers in `numbers`, one at a time, and add it to a `total` that is initialized at `0`. In other words, we calculate the sum of all the elements in `numbers`.\n",
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"\n",
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"\n",
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"Many beginners struggle with the term \"loop.\" To visualize the looping behavior of this code, we use the online tool [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B1,%202,%203,%204%5D%0A%0Atotal%20%3D%200%0A%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20total%20%3D%20total%20%2B%20number%0A%0Atotal&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false). That tool is helpful for two reasons:\n",
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"Many beginners struggle with the term \"loop.\" To visualize the looping behavior of this code, we use the online tool [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B1,%202,%203,%204%5D%0A%0Atotal%20%3D%200%0A%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20total%20%3D%20total%20%2B%20number%0A%0Atotal&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false). That tool is helpful for two reasons:\n",
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"1. It allows us to execute code in \"slow motion\" (i.e., by clicking the \"next\" button on the left side, only the next atomic step of the code snippet is executed).\n",
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"1. It allows us to execute code in \"slow motion\" (i.e., by clicking the \"next\" button on the left side, only the next atomic step of the code snippet is executed).\n",
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"2. It shows what happens inside the computer's memory on the right-hand side."
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"2. It shows what happens inside the computer's memory on the right-hand side."
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]
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]
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],
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],
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"metadata": {
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"metadata": {
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "intro-to-data-science",
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"language": "python",
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"language": "python",
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"name": "python3"
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"name": "intro-to-data-science"
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},
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},
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"language_info": {
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"language_info": {
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"codemirror_mode": {
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"codemirror_mode": {
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.12.4"
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},
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},
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"toc": {
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"toc": {
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"base_numbering": 1,
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"base_numbering": 1,
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@ -178,9 +178,9 @@
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],
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],
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"metadata": {
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"metadata": {
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "intro-to-data-science",
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"language": "python",
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"language": "python",
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"name": "python3"
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"name": "intro-to-data-science"
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},
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},
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"language_info": {
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"language_info": {
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"codemirror_mode": {
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"codemirror_mode": {
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@ -192,7 +192,7 @@
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.12.4"
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},
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},
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"toc": {
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"toc": {
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"base_numbering": 1,
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"base_numbering": 1,
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@ -112,9 +112,9 @@
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],
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],
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"metadata": {
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"metadata": {
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "intro-to-data-science",
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"language": "python",
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"language": "python",
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"name": "python3"
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"name": "intro-to-data-science"
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},
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},
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"language_info": {
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"language_info": {
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"codemirror_mode": {
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"codemirror_mode": {
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.12.4"
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},
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},
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"toc": {
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"toc": {
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"base_numbering": 1,
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"base_numbering": 1,
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"\n",
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"\n",
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"Let's execute the function with `numbers` as the input. We see the same `6` below the cell as we do above where we run the code without a function. Without the `return` statement in the function's body, we would not see any output here.\n",
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"Let's execute the function with `numbers` as the input. We see the same `6` below the cell as we do above where we run the code without a function. Without the `return` statement in the function's body, we would not see any output here.\n",
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"\n",
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"\n",
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"To see what happens in detail, take a look at [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B1,%202,%203,%204%5D%0A%0Adef%20add_evens%28numbers%29%3A%0A%20%20%20%20%22%22%22Sum%20up%20all%20the%20even%20numbers%20in%20a%20list.%22%22%22%0A%20%20%20%20result%20%3D%200%0A%0A%20%20%20%20for%20number%20in%20numbers%3A%0A%20%20%20%20%20%20%20%20if%20number%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20result%20%3D%20result%20%2B%20number%0A%0A%20%20%20%20return%20result%0A%0Atotal%20%3D%20add_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) again. You should notice how there are two variables by the name `numbers` in memory. Python manages the memory with a concept called **namespaces** or **scopes**, which are just fancy terms for saying that Python can tell variables from different contexts apart."
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"To see what happens in detail, take a look at [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B1,%202,%203,%204%5D%0A%0Adef%20add_evens%28numbers%29%3A%0A%20%20%20%20%22%22%22Sum%20up%20all%20the%20even%20numbers%20in%20a%20list.%22%22%22%0A%20%20%20%20result%20%3D%200%0A%0A%20%20%20%20for%20number%20in%20numbers%3A%0A%20%20%20%20%20%20%20%20if%20number%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20result%20%3D%20result%20%2B%20number%0A%0A%20%20%20%20return%20result%0A%0Atotal%20%3D%20add_evens%28numbers%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) again. You should notice how there are two variables by the name `numbers` in memory. Python manages the memory with a concept called **namespaces** or **scopes**, which are just fancy terms for saying that Python can tell variables from different contexts apart."
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]
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]
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},
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},
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{
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{
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"traceback": [
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/tmp/user/1000/ipykernel_707190/1049141082.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mresult\u001b[49m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'result' is not defined"
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"\u001b[0;31mNameError\u001b[0m: name 'result' is not defined"
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]
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]
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "intro-to-data-science",
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"language": "python",
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"language": "python",
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"name": "python3"
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"name": "intro-to-data-science"
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},
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},
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"language_info": {
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"language_info": {
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"codemirror_mode": {
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"codemirror_mode": {
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.12.4"
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},
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},
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"toc": {
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"toc": {
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"base_numbering": 1,
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"base_numbering": 1,
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],
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],
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"metadata": {
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"metadata": {
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"kernelspec": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "intro-to-data-science",
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"language": "python",
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"language": "python",
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"name": "python3"
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"name": "intro-to-data-science"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.12.4"
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},
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},
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"toc": {
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"toc": {
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"base_numbering": 1,
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"base_numbering": 1,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"ename": "AttributeError",
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"data": {
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"evalue": "'int' object has no attribute 'is_integer'",
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"output_type": "error",
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"True"
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"traceback": [
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]
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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},
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"execution_count": 9,
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"\u001b[0;32m/tmp/user/1000/ipykernel_306555/2418692311.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"metadata": {},
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"\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
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"output_type": "execute_result"
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]
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}
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}
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],
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],
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"source": [
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"source": [
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"a.is_integer()"
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"a.is_integer() # Note: In Python versions < 3.12 this cell raises an `AttributeError`"
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]
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]
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},
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{
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"traceback": [
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/tmp/user/1000/ipykernel_306555/2667408552.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmore_numbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmore_numbers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(\u001b[38;5;241m10\u001b[39m)\n",
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"\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
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"\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
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]
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]
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}
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}
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"traceback": [
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/tmp/user/1000/ipykernel_306555/3320204082.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mto_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mto_words\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzero\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
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"\u001b[0;31mKeyError\u001b[0m: 'zero'"
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"\u001b[0;31mKeyError\u001b[0m: 'zero'"
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]
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]
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}
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}
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],
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],
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"metadata": {
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"metadata": {
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"pygments_lexer": "ipython3",
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"Requirement already satisfied: numpy in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (1.21.1)\n",
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"Requirement already satisfied: numpy in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (2.0.0)\n"
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"Requirement already satisfied: matplotlib in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (3.4.3)\n",
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"Requirement already satisfied: packaging>=20.0 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from matplotlib) (24.1)\n",
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||||||
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|
"Requirement already satisfied: pyparsing>=2.3.1 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from matplotlib) (3.1.2)\n",
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||||||
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|
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||||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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||||||
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||||
"\u001b[0;32m/tmp/user/1000/ipykernel_1264563/568665770.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
"Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mm1\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||||
"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
|
|
||||||
"\u001b[0;31mValueError\u001b[0m: shapes (5,) and (2,5) not aligned: 5 (dim 0) != 2 (dim 0)"
|
"\u001b[0;31mValueError\u001b[0m: shapes (5,) and (2,5) not aligned: 5 (dim 0) != 2 (dim 0)"
|
||||||
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|
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|
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||||||
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|
||||||
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||||||
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||||||
"pygments_lexer": "ipython3",
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"version": "3.8.12"
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|
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|
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||||||
"Requirement already satisfied: pandas in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (1.3.3)\n",
|
"Requirement already satisfied: pandas in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (2.2.2)\n",
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||||||
"Requirement already satisfied: numpy>=1.17.3 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from pandas) (1.21.1)\n",
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"Requirement already satisfied: numpy>=1.26.0 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2.0.0)\n",
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"Requirement already satisfied: python-dateutil>=2.7.3 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from pandas) (2.8.2)\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n",
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"Requirement already satisfied: pytz>=2017.3 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from pandas) (2021.3)\n",
|
"Requirement already satisfied: pytz>=2020.1 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2024.1)\n",
|
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"Requirement already satisfied: six>=1.5 in /home/webartifex/repos/intro-to-data-science/.venv/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
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"Requirement already satisfied: tzdata>=2022.7 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from pandas) (2024.1)\n",
|
||||||
"Note: you may need to restart the kernel to use updated packages.\n"
|
"Requirement already satisfied: six>=1.5 in /home/instructor/Repositories/intro-to-data-science/.venv/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
|
||||||
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||||||
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||||||
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|
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||||||
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|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||||
"Int64Index: 694 entries, 192594 to 211519\n",
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"Index: 694 entries, 192594 to 211519\n",
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||||||
"Data columns (total 19 columns):\n",
|
"Data columns (total 19 columns):\n",
|
||||||
" # Column Non-Null Count Dtype \n",
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" # Column Non-Null Count Dtype \n",
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||||||
"--- ------ -------------- ----- \n",
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||||||
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|
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|
||||||
"<class 'pandas.core.frame.DataFrame'>\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
|
||||||
"Int64Index: 694 entries, 192594 to 211519\n",
|
"Index: 694 entries, 192594 to 211519\n",
|
||||||
"Data columns (total 19 columns):\n",
|
"Data columns (total 19 columns):\n",
|
||||||
" # Column Non-Null Count Dtype \n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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||||||
"<class 'pandas.core.frame.DataFrame'>\n",
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||||||
"Int64Index: 694 entries, 192594 to 211519\n",
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"Index: 694 entries, 192594 to 211519\n",
|
||||||
"Data columns (total 19 columns):\n",
|
"Data columns (total 19 columns):\n",
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||||||
" # Column Non-Null Count Dtype \n",
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" # Column Non-Null Count Dtype \n",
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||||||
"--- ------ -------------- ----- \n",
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||||||
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||||||
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||||||
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||||||
"1254 78\n",
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||||||
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||||||
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||||||
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||||||
"73919 14\n",
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"74791 3\n",
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"74791 3\n",
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||||||
"Name: customer_id, dtype: int64"
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"Name: count, dtype: int64"
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||||||
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||||||
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|
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|
||||||
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|
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|
||||||
"15924.78"
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"np.float64(15924.78)"
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||||||
]
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"885.0"
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"3.5"
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"83.7"
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"12.5"
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"22.94636887608069"
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]
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],
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"metadata": {
|
"metadata": {
|
||||||
"kernelspec": {
|
"kernelspec": {
|
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"display_name": "Python 3 (ipykernel)",
|
"display_name": "intro-to-data-science",
|
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"language": "python",
|
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|
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"name": "python3"
|
"name": "intro-to-data-science"
|
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},
|
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"language_info": {
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"codemirror_mode": {
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|
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"name": "python",
|
"name": "python",
|
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"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
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"version": "3.8.12"
|
"version": "3.12.4"
|
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},
|
},
|
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"toc": {
|
"toc": {
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"base_numbering": 1,
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|
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],
|
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"metadata": {
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"kernelspec": {
|
"kernelspec": {
|
||||||
"display_name": "Python 3 (ipykernel)",
|
"display_name": "intro-to-data-science",
|
||||||
"language": "python",
|
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|
||||||
"name": "python3"
|
"name": "intro-to-data-science"
|
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},
|
},
|
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"language_info": {
|
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"codemirror_mode": {
|
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|
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|
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|
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"name": "python",
|
"name": "python",
|
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"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.8.12"
|
"version": "3.12.4"
|
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},
|
},
|
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"toc": {
|
"toc": {
|
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"base_numbering": 1,
|
"base_numbering": 1,
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|
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|
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|
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"text/plain": [
|
"text/plain": [
|
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"['DESCR',\n",
|
"['DESCR',\n",
|
||||||
" 'data',\n",
|
" 'data',\n",
|
||||||
|
" 'data_module',\n",
|
||||||
" 'feature_names',\n",
|
" 'feature_names',\n",
|
||||||
" 'filename',\n",
|
" 'filename',\n",
|
||||||
" 'frame',\n",
|
" 'frame',\n",
|
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|
@ -511,9 +512,7 @@
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {
|
"metadata": {},
|
||||||
"needs_background": "light"
|
|
||||||
},
|
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
@ -552,9 +551,7 @@
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {
|
"metadata": {},
|
||||||
"needs_background": "light"
|
|
||||||
},
|
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
@ -606,9 +603,7 @@
|
||||||
"<Figure size 576x576 with 16 Axes>"
|
"<Figure size 576x576 with 16 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {
|
"metadata": {},
|
||||||
"needs_background": "light"
|
|
||||||
},
|
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
@ -959,7 +954,7 @@
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"0.9333333333333333"
|
"np.float64(0.9333333333333333)"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 26,
|
"execution_count": 26,
|
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|
@ -986,7 +981,7 @@
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"0.9523809523809523"
|
"np.float64(0.9523809523809523)"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 27,
|
"execution_count": 27,
|
||||||
|
@ -1019,9 +1014,7 @@
|
||||||
"<Figure size 432x288 with 1 Axes>"
|
"<Figure size 432x288 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {
|
"metadata": {},
|
||||||
"needs_background": "light"
|
|
||||||
},
|
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
@ -1150,9 +1143,9 @@
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
"kernelspec": {
|
"kernelspec": {
|
||||||
"display_name": "Python 3 (ipykernel)",
|
"display_name": "intro-to-data-science",
|
||||||
"language": "python",
|
"language": "python",
|
||||||
"name": "python3"
|
"name": "intro-to-data-science"
|
||||||
},
|
},
|
||||||
"language_info": {
|
"language_info": {
|
||||||
"codemirror_mode": {
|
"codemirror_mode": {
|
||||||
|
@ -1164,7 +1157,7 @@
|
||||||
"name": "python",
|
"name": "python",
|
||||||
"nbconvert_exporter": "python",
|
"nbconvert_exporter": "python",
|
||||||
"pygments_lexer": "ipython3",
|
"pygments_lexer": "ipython3",
|
||||||
"version": "3.8.12"
|
"version": "3.12.4"
|
||||||
},
|
},
|
||||||
"toc": {
|
"toc": {
|
||||||
"base_numbering": 1,
|
"base_numbering": 1,
|
||||||
|
|
Loading…
Reference in a new issue