Linda Markowsky

Uploaded final versions of the Jupyter notebooks for the python example.

...@@ -5,8 +5,14 @@ ...@@ -5,8 +5,14 @@
5 "metadata": {}, 5 "metadata": {},
6 "source": [ 6 "source": [
7 "# A Brief Introduction to the Python Data Science Ecosystem\n", 7 "# A Brief Introduction to the Python Data Science Ecosystem\n",
8 - "### Presented by: L. Markowsky (UMaine ECE Department and CCI)\n", 8 + "### Presented by: L. Markowsky (linda.markowsky@maine.edu)\n",
9 - "---\n", 9 + "---"
10 + ]
11 + },
12 + {
13 + "cell_type": "markdown",
14 + "metadata": {},
15 + "source": [
10 "### References:\n", 16 "### References:\n",
11 "- McKinney, Wes. _Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Second Edition_, O'Reilly Media, September 2018. https://wesmckinney.com/pages/book.html (author's page) and https://github.com/wesm/pydata-book (datasets and Jupyter notebooks)\n", 17 "- McKinney, Wes. _Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Second Edition_, O'Reilly Media, September 2018. https://wesmckinney.com/pages/book.html (author's page) and https://github.com/wesm/pydata-book (datasets and Jupyter notebooks)\n",
12 "- Python Software Foundation. _Python 3.7.3 Documentation_, 2001-2019. https://docs.python.org/3/ (start with the official Python Tutorial)\n", 18 "- Python Software Foundation. _Python 3.7.3 Documentation_, 2001-2019. https://docs.python.org/3/ (start with the official Python Tutorial)\n",
...@@ -64,6 +70,8 @@ ...@@ -64,6 +70,8 @@
64 " - encourages execut-explore workflow instead of edit-compile-run workflow\n", 70 " - encourages execut-explore workflow instead of edit-compile-run workflow\n",
65 " - documentation: https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/examples_index.html\n", 71 " - documentation: https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/examples_index.html\n",
66 " - gallery of notebooks: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks\n", 72 " - gallery of notebooks: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks\n",
73 + " - notebook presentation of a paper: https://nbviewer.jupyter.org/github/cossatot/lanf_earthquake_likelihood/blob/master/notebooks/lanf_manuscript_notebook.ipynb\n",
74 + " - launching the Jupyter notebook: https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html\n",
67 "- ***NumPy*** - Numerical Python\n", 75 "- ***NumPy*** - Numerical Python\n",
68 " - ndarray - efficient multi-dimensional array object\n", 76 " - ndarray - efficient multi-dimensional array object\n",
69 " - element-wise array operations\n", 77 " - element-wise array operations\n",
...@@ -82,6 +90,7 @@ ...@@ -82,6 +90,7 @@
82 "- ***Matplotlib*** and ***Seaborn***\n", 90 "- ***Matplotlib*** and ***Seaborn***\n",
83 " - Matplotlib - popular Python plotting library\n", 91 " - Matplotlib - popular Python plotting library\n",
84 " - examples (with source code): https://matplotlib.org/gallery/index.html\n", 92 " - examples (with source code): https://matplotlib.org/gallery/index.html\n",
93 + " - plotting the coherence of two signals: https://matplotlib.org/gallery/lines_bars_and_markers/cohere.html#sphx-glr-gallery-lines-bars-and-markers-cohere-py\n",
85 " - Seaborn - high-level interface for statistical visualizations; built on matplotlib\n", 94 " - Seaborn - high-level interface for statistical visualizations; built on matplotlib\n",
86 " - examples (with source code): https://seaborn.pydata.org/examples/index.html\n", 95 " - examples (with source code): https://seaborn.pydata.org/examples/index.html\n",
87 " - both produce publication-quality plots\n", 96 " - both produce publication-quality plots\n",
...@@ -98,7 +107,8 @@ ...@@ -98,7 +107,8 @@
98 " - linear regression models\n", 107 " - linear regression models\n",
99 " - robust regression\n", 108 " - robust regression\n",
100 " - time series analysis\n", 109 " - time series analysis\n",
101 - " - examples, tutorials, and recipes: https://www.statsmodels.org/stable/examples/index.html" 110 + " - examples, tutorials, and recipes: https://www.statsmodels.org/stable/examples/index.html\n",
111 + " - robust linear models: https://www.statsmodels.org/stable/examples/notebooks/generated/robust_models_0.html"
102 ] 112 ]
103 }, 113 },
104 { 114 {
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414 "cell_type": "code", 424 "cell_type": "code",
415 "execution_count": 11, 425 "execution_count": 11,
416 "metadata": {}, 426 "metadata": {},
417 - "outputs": [], 427 + "outputs": [
428 + {
429 + "name": "stdout",
430 + "output_type": "stream",
431 + "text": [
432 + "None\n"
433 + ]
434 + },
435 + {
436 + "data": {
437 + "text/plain": [
438 + "[4, 3, 2, 1]"
439 + ]
440 + },
441 + "execution_count": 11,
442 + "metadata": {},
443 + "output_type": "execute_result"
444 + }
445 + ],
418 "source": [ 446 "source": [
419 "# Tab completion reveals attributes and methods. Try:\n", 447 "# Tab completion reveals attributes and methods. Try:\n",
420 "# x.reverse\n", 448 "# x.reverse\n",
...@@ -426,7 +454,9 @@ ...@@ -426,7 +454,9 @@
426 "# x.reverse()\n", 454 "# x.reverse()\n",
427 "# x\n", 455 "# x\n",
428 "\n", 456 "\n",
429 - "#x." 457 + "x.count(1)\n",
458 + "print(x.reverse())\n",
459 + "x"
430 ] 460 ]
431 }, 461 },
432 { 462 {
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