I. Python and Jupyter Primer.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In This Notebook:\n",
    "\n",
    "- [Why Python](#Why-Python)\n",
    "- [Jupyter Notebook Environment](#Jupyter-Notebook-Environment)\n",
    "- [Basic Python Syntax](#Basic-Python-Syntax)\n",
    "    - [Indentation](#Indentation)\n",
    "    - [Comments](#Comments)\n",
    "    - [Assigning Variables](#Assigning-Variables)\n",
    "    - [Installing Packages](#Installing-Packages)\n",
    "- [Data Types](#Data-Types)\n",
    "    - Individual Items: [Numbers](#Numbers), [Strings](#Strings), [Booleans](#Booleans), [None Type](#None-Type), [Type-Casting](#Type-Casting)\n",
    "    - Collection of Items: [Lists](#Lists), [Tuples](#Tuples), [Sets](#Sets), [Dicts](#Dicts), [Comprehensions](#Comprehensions)\n",
    "- [String Matching](#String-Matching)\n",
    "    - [Regex](#Regex)\n",
    "    - [Fuzzy Matching](#Fuzzy-Matching)\n",
    "- [Code Structures](#Code-Structures)\n",
    "    - [If-else-elif](#If-else-elif)\n",
    "    - [For Loops](#For-Loops)\n",
    "    - [While Loops](#While-Loops)\n",
    "    - [Try-Except](#Try-Except)\n",
    "    - [Functions](#Functions)\n",
    "    - [Classes](#Classes)\n",
    "- [Numpy Basics](#Numpy-Basics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python is a general purpose and high level language, making it easy and intuitive to use.  While it did not start as a data science tool when first launched in 1991, its application for data science took off in the 2010s after the development of key libraries including numpy and scipy for numerical processing and advanced mathematics (2005), pandas for spreadsheets and data management (2010), scikit-learn for machine learning (2010), as well as other subsequent tools for natural language processing (nltk), data visualization (matplotlib, seaborn, bokeh), deep learning (tensorflow), topic modeling (gensim), and many others. \n",
    "\n",
    "In addition to the new and growing suite of data analysis tools, python's roots as a general purpose language means it is powerful and useful in a wide range of settings, from programming back end servers (flask, django, etc), webscraping and crawling (urllib, scrapy, beautifulsoup), database management, and many other things.  This means python can serve as a single language used in every step of your workflow, making it production-ready.\n",
    "\n",
    "Additionally, while interpreted languages such as Python tend to have inferior performance to lower-level programming languages, many of these extensions for computation-intensive tasks are developed and built upon lower layer Fortran and C implementations for fast and vectorized operations.  \n",
    "\n",
    "For all these reasons, python is an increasingly popular language for data science and enjoys a robust and active development community.  If you are curious about a specific comparison between python and R for data science, check out this [Python vs R](https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis) article and infographic from datacamp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebook Environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before jumping into python, lets give a quick orentation to the Jupyter environment.  The web-based jupyter notebook environment was of the main tools developed by the [IPython](http://ipython.org/) (Interactive Python) project, and you will often see the terms IPython and Jupyter used interchangably.  \n",
    "\n",
    "These interactive jupyter notebooks have become one of the most useful tools in modern data science due to the fact that it allows the analyst to use the full power of the browser to directly explore the results of their coding and analysis.  Jupyter also allows you to create and share rich documents that combine markdown content with code (such as this notebook!).  And Jupyter is not limited to just Python.  The name Jupyter originated from the fact that the notebooks support Julia, Python and R, and they now support kernels (implementations of the language-agnostic Jupyter notebook environment) in over 40 languages. \n",
    "\n",
    "You can also download Jupyter to your local machine from https://jupyter.org/."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jupyter Interface"
   ]
  },
  {
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QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEYAknJixL2tUxmUibPUjpDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIxBJKSFyXsa5nMpEyepXSGEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiCCQlL0rY1zKZSZk8S+kMIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEEEhKXpSwr2UykzJ5ltIZQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIIZCUvChhX8tkJmXyLKUzhAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQQyApeVHCvpbJTMrkWUpnCAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAghkBS8qKEfS2TmZTJs5TOEAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBADIGk5EUJ+1omMymTZymdIQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBGAJJyYsS9rVMZlImz1I6QwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACMQSSkhcl7GuZzKRMnqV0hiTduX5Mh86f1Mmzh3RosNKvY+obeYAjAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMc4Gk5EUJ+1oWYlImz1L6gh/69dtDevHzNVozo6967f+awO+CX0wAIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIzGuBpORFCftalmFSJs9S+gIfuqdT/+9Mg77u/v9xaYFbcvkIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIzG+BpORFCfta1mFSJs9Suj/U19enlpYWvfbaa9q4ceOMv5zjOMdzjjtX/zz49pAaZ9TRNwgKv3jm+ly9TOpCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFZEEhKXpSwr2WykzJ5ltLzQx9++KFaW1vlXMfk5GTUZhWNO8dxjucc1zn+3PtzRyf/HoR11zih3xONerX71XhfJ+uV3ycfFn5R7d/PvSukIgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmD2BpORFCfta5jwpk2cpPd951wnkPso/zvHnXoffO+oMhX33f/1rfIZvD6ne7wp8SJfyGel7ujcW/xBsiQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIJF1gNPOKlm1aFutrxf9YozV7d+no4A2Nz04/urnDd+e4XnEdWr6aO2VRCQIIIIDA7AokJS9K2Ncy70mZPEvpamlpyXfgtX02W2OOj3OeOfVn9KRe98O6ToffJh37zwd6MB7n6476eoPOvvV9Z5SPCY/36c3P9+vMg5lc6Tfas7lLS52v9m9mcqDSfS8NFI7rHT/ie/XrJ1S3r0/v99zQ6MPSw8x4ZPymjh08ra47oSPdOa+X3Zr2XAp9xttHK+CvjQFdmLUzPcK1PGs1VnqgXzWaHdTajlm+NystI2L70Z6eWPf479PdWnugT0cGb8/gP55HdWzPI3pWRVzfbz7s3yc9OhZ+fv3mxVVYAM/bCsHYHAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoJxAJWFfMxS8Ysd+fZkrd+SEfUbYN2ET9rjLHVHb6ipVVVWptn3kcZ+c8yVEYOxch5rX1ar6icJaqapKafFTtWp8u1NDUzWhfDiizMH1ali+KL/OCvs2KN3er5FZyICNDLQpXV+jxalCbanf1ahhQ6sy013ON9tUW1U4VnqgkgnqV9rdz7mfor4WLS/U13l9KrhKzl3YNil5UcK+lrgRVFoAACAASURBVLlNyuRZStdrr72myclH+6NSzvGd88ylP8PnNmpNUdjXCfxO78vrCPzg8pv5Yxz6diZX+ggDkn5QzQ3oRYR980Fj77Ntp9R1exbXx3dnVbfFOb8lLEf4bCYLZ2b7+muDsG805E/qPTi3w61xw77mPV79xoAuTOs/ngn7Rq+VBHzC8zYBk0SJCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEByBIKw7ys6Xq5xzuSExm99qaN/Xe13AV5xoEfjybnU8pUS9i3vs+A/Jey74JdAWYAhdb5UHRlczQdaUyvVfM4e8shdaVWDHxC2BGCfaFDbjbIFlPlwTJlN5WpbpIaDl2WvrMxhH3HYNwgBp1Szo1+zGflNSl6UsK9l/SVl8iyla+PGjbbhWR97XOeJU/iD79vVNM1gb2kg+E315f/W+avO9Dph4Vd17FacKqK2eTxh3w1/u6bBC/av3p5BNe00AsHpAV2YiKq3wnE/VErYt0K5R7u5Py+EfaOh5364NQj7dmvPP+z3t3Pfd3X16eXXg3u8+p2LGo2+8IhP5r5HROHTH/bvE8vza/pH/W32JOz727hzVgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgngrEDvv61z+ungMr3MDvCzo6o5yFf1BeIDDHBQj7zvEJ+g3Lyym7wwvTprRyR0ZD971ycho716bG5W6AN1VXGtq92aE6t9tuVapGTR9f1pjbyTd3s197691Ov7Z9vdOU+Z7d7dXmhHqzGvNSvXcvq2NrjVL57ropNX5aYZx2NsK+/7xXmZ5M6ddnbdq7tVEr/QB0SnVHp9uCuBQnKXlRwr6lc6ekTJ6l9AUY9v1VZ/rCHXx36OSPNh372Pl+Y/9/9OmBs1nujPY7AeIv2nXdvlvM0ccT9t1zaYpyJu9rsP2EvA6gG3p/mmKHmB+XC8sRPouJ+Ag28+eFsG+07twPtwZh3xhh1Ie3dewdL/B7Qu99PYsdvKMR+WSuCPC8nSszQR0IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwLwQqDzsK+mrFq3YtCwf+H3l75W3J5oXcFzEAhMg7LvAJjz+5d7tUEM+MFulle8O2fe7n9F6N9Cb2pQxuujmlNmUcjsCR3X+HVLbc4VtUus6K+twO2VtOWU2uOd/plUR1duvaTbCvtv77cf2Ru/3K+0FpZ9sVtYbn+H3pORFCftaJjopk2cpfQGGfR+o7x9GWNcJ6HqBXRtQeGzyutq/CPZ//Uv39098e0j1n6/Ri2dmFvWV5kjY17nu3BVt3+yGAQ9cmp1fm+GHSi1hRMJn4dX2+N7780LYNxp9noV9nQv97qzq3Hv82U++j750Ppl/Ajxv59+cckUIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwG8oMK2w753jesUL+2aKw75fthdCwMt2H4/+LaVftbidgVv0pXnt/nHd8fGr+rx9o9b8D7eTcNMftWZviz7/Nv9rnM0986+9c7/i1jSa/UBbdq9WjVtrTXqdthw7o1G3a2bRAfxzL1PLV+Ynozq+u3BN+fHJcV3tbdHGHX90A88r9McdG9XSe1Xj5fo02fbbtUUfZAt+4drNCng9FwQI+86FWZiLNYwcrXPDuuuV8brmWgoderemsF0qHYRWcxmtd4PCS3aVibJeb1VNfruVaq0g3jbSXjtlbbkv1rvb1Kmjkua5jyPsKym4hlq13bTATmMoKXlRwr6WyU3K5FlKX3hhX68DrxPy9b9e1Kvdr8b7+qLe2G+jOt0H1PXBF7Xm83od+tamXMnYHAr7ygg3bnFDoBPXtHNLIQD8bEf5cOBwV3ehM/DOrIaMYJnXLdj7/nKP+5d2Y5t85+Hxm+pqP6W6193A8ZYTqts3oK5v/T71dtiHo7rQM6ANO0/o926Qsfr1E1p74Kx6/zNiX//c7nVO99z2ioLRe7fU29VXVJvj8Ic3Tmln1xUN2f7WPlu13fu+2HPbCW1ov6gh5y8Jjzvs65/PEvr2tYJ7obgTdbAu82vn4agGj59WfYx14q/JzafUdc8/UekLY53vPPuLLrR7HXDD3y3h6Mn7uto7qKY9Faw/415zrnXi66yadhbOVf16t5qOfxMrbF9RZ9/8VQfGS9+5GPxHsjc/e85rdPKOett79Af3vl+1b0CZH36RjJqXtn9TZOh55edn8r6GBge14Y0Tqnbvxz+8eVrvu/+xmd8xfL9tO6G1h87rqu1+8M80qfFvL2rfgR6t2mY8I/ac1vuDtyP+AzhYO9Ny9lw2h9atf4+646PX9P6Bbt/Mef4491rp9dzWkTfd2vddLDvHE9k+1++0eicKCEXO+lWj2bNF6+736R41Hb8W8X8ynNfL7nzk769QzYV9I55J/hzwAgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGCwLTCvkZn3y39xcFbL7Q607Bv79cH9UKTGxx2w7rL/O8r9EpmuGQKvXO/cvKMev662g0UW46RbtGX4VBenLDv4FUdbHaDx34twfFXvH1cw7bAb+5L7d8Rvd/qv/bo+AeF43hB5ZKLm+5AbkSZg+vV8NRipdxQYdUT1aqpX6/WnhGjw6jtBGO6/HGzGldXa5G7b+p3Napdt1ed18dsOxSN5W5m1LqhQTW/8zqXLlL16kbt/WyobHfSseud2ruutni/pxuUbu/XiC2oLal/e1U+sFjbXggDjfTsLap70fJaNb6didy/UHhOxfultPjZRu097VzrFGHfGTkXsZW8yd3sV9uuRtWac1hVsGwuY1I4kDOHaWP+3WvqKTiF3UpOPjakzreLz71oeY0atrepv5JgaMmB589A9u0a1Thz809tZTvjBqHVtPx+tueatSR/b6WUPlPOpF9p9x6sOxoffujT9Wp4ukaL6zsUtdfYp41u2LdRnVPf1kGRU4R9cwNpVXvPjedMm+Baqqbq7Ov0u/TDyIR9A/wF/Iqw79STv3Hjxqk3egxb3PtqhxHWNQO/03jdfUyFv/Z53X4P6ZLtL1wVXZcRvgsF6Co6jG1jP6jWpeLwpG1jZywIxi31wr6aDIKP2wd1NfJ6b+mIG1as77ot+WG4cFiyS7aw7/aOQdW74UIvFGx+f7nH7agcKn3iu6w2eME/N8hm7ue8rv/kRmkQ0K9vQL1fT+/coVJCbyc13H9aqyJq8mvcdkpdt0OoM65tUqNn+1QX5Zk+rczJvkIwe7MlvBq6kvhvy6xlfy2GQpNFBw/2L16vwbp8+W/nte+N0jXledZ/8r3cXGThyL5llzb0/lR0NvPNxNnTBY8tfRqcULDmS+Yv5PXjFe0pU09h/YVqyp84uKadHw2UrJOpgvVe7bMf9h3QvnfCvl5QOqg5Mux78ht17QvvH7zP38e3L0bfs9tOK2MLZU+OqfeDbj887M23+b36rUFdDf+HtfFMm5Zz1Lr111WP3u8ZiL7XtnTrva9/9qarMPM9Pe6957kWfey++UW9hwpu1Yev+WvaD/ue/EaZ1hPucQJf3yM9oAthC7/mLm1vr6xmW4WMIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIILGyBisO+k8N+p9tlmzbq89C/C3qB25mFfVdohRP0Tf+rjn41rAn3n+InbvWqxQ/NvqCjt4rnzjv3iiYnWLtCLxzt1Q2vvolRXf1so9/ld8X/OuP/+13+KDHCvoXjrta/fvalhr1/0M4Nq/eDNW6X32V64bNQCNnxetvrTLxGLb03NO4GVidundH+UHh4VsO+NzrU8EQhBFvlhu7C31NFITzD825GTU95IV3bMVJauTsbERbOKbt7ZRAutpw79VyrLpf0fBtTZmtN2f2qljeq44ZRp/vSD60e7lfnS9VueNFS9/K0+kvOK+n+ZbXWL4rYz7nWDu1dXRwo9quYibN/ENuLqR2d+Uw9FXVN/UqXmcPqlzrVtjXimiSNfdGkmpTF0J/PajV+PGQrnDGLQHbXksL6Mjv7DqTdNTdVkDUIm8cJyFpOHzE0pDZ3XVc901o2rFxygDJhXyfo662d0mdMJWHfnPq3um6V1ldScDCQlLwonX2DOfNfJWXy/IKNF48rhPu4zmNcWunLB2e03+/mO41wb2jfF8+4Pc1/aNeLzme9Z/Rr6VkrHAkCjuEAXYUHKt3cD6rFDPtOfq/3trvBMbPr59eDejYfejyh974OBVO9s173tgmFOf0aQuPOfkb4LB9SS5/WkUu3NZH/C+qvmrh1Tfve8oJs3ToS+gu3bp/Xy16gddspve/vO6mJH7/XsYNBOHDVRzdCf+EOulxWO8fIn/uO+5d959xmiNNybu+6o75fH/QDnKtaz+vqj05nVOfPpCYe3NbgR0Ft1a1Xijt8Gi7Tqu2HrOrdkGr1WwMavOWGDR/+rOHsQD5UnT9ufptQeNWtcnrfyqzlcuvAP1mwf1TY1wsz1n90TcM55+5z59oIPr58yvx1Lz8F4dPITqo/KXPADVYWBe6jw635knPfaE/aXZ/bevRe1lu7zo8I3dZgR4/fabo0rG4ce3OX8vM06l7P7e815P2Ho29jf1Fx2PfeRW3w1sZHxn/J+PPjXM8Jbe+5WQjIP/xJQ9/ece8do+YipyAc7a2rDX+7oWHvv5zv3dB7XjfbzSe0yjFLn9ax62OF+23yFw0P9vn3S2nQ+WddaA+CrWs7vLmXlF/Tg1rrBv6r3zkf+olXo+bpOPsuoeeXcY/m1+SWbu3z5n/yF41eP6/t3trY0qMu82cVjDmIDKCPX1KTO097jJ8o8cK+nnP9R1c0dM/9X6GJMV3tOuWvOTMknF894Zrza9Z95pXUfMoeurYvQ0YRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQWoEDssO/EuEZ/6NXB3TVux9wVWveZ8W+Vrp0XuJ1Z2HeZljXtUm+4MY5zjltH9YLbVfeVv5v/piz55960TC/879LapAmd+V9e8Ha/vjTnO0bY1wkQ7zrrpXzNnYd1dJfb4fftz4PfzCppvH+LGwRerZavLPuaYeBNyzR7Yd8htT5TCGqmnk0rc8PAzI0pe7DB79a78mAosHm/X+nlbsjziVo19wwp53XUvT+kzK5ad9+U6tpD+0oaenelG2BMqebVNmXvuud+OKZse6Pf7XPJ1n4jLJxT/3YvpJtSzdYOXfb3y2mop1m1XnA5VaeOm6Z/0Nk3lXICyimt3NGpy3fdbULXm9qUMc7rbJNTZoMXbK5WY3tWY27JubtZteXDwyml3OCr1z24cPQZOBdfQsk7p+NqoRuzcz0ZDRkh5dyIV1dhnqrfvhzaf0htz7nXlKpR08eXNebOYe5GRulnvest7F98TVJRV9anmtRxZcw3c/ZvXu0Fo1OqpNNsqMiF8/Z+Ruvd9VO0/s6k3TmuUasbZbOjZJX2gtfPRXfpte9rGX2Y09BAm5q8dZCqU5vtkWnZ1R+KCPvmrrSqzrtW6w8TxAv75kYuq3OH90MD1UoPGM8wv4jpvUhKXpSwr2V+kzJ5ltL1uEK4j+s8tmv0x749NHtdfT9/Ue0/FI48fO7V/HH3fz3zqK8UBBx/27DvpIZ7evzOmfnuvD5kEAIuDeI5G03qaocbxgsHKqPCcs5uZvhsy2n7X7iNbcIhTi+guXR7nwbHbSFkMyTYrSM/GNsYx10ade5bQWi2+Nw+TMSLXzR42A2BvpnVkHHaYAdjm3B33RnVZoRbbd09nQK+O+uHgZeGzx0UOI1XZdZyuXXgnynYv1zYt/5vob99O/tPjqnLDewudbvzeocd7z9VvpOqH74Mh9mNoGgo3Jpf85+4a35Lj46FuzPnT27eU+EursaxN08jTO5eXGVhX/N+6NLOrBdAl+TPT5dKQqIepNElN/ys8kKoTvC1vjOcypfk/8BAl5ZusQVJjQ7iO7PFP/VlBOdLQ9NucUbwvzhAO0Nn36Vc2Ldb739n+d+C0Ytqcn8Yodg0CJcvDT8v3csZ73XXbKib+pTOMp4rfnd296BFz5WINWs42p/1/mLgBQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwAIXCMK+bljVDdIuK/u9RuuOXS1u1OU6+oHb3ceLQq9FzF+1uIHhlsjA7Z8+vlq0S/Dmho7+uVDriqNFcV0j7LtFPePBHuaribO73HO/os/NrHCcsO+/HFRkVf97TeG4TWaIeFyf73Vd/9pr9crX9vVB/ckLMGfMoszKK3ztB/GiQoQ5ZTYt0uKnalSztTj8mt3thm7LBACH2uvckGKDOrxQrVPi3Q41uJ1fq7ebYd6g/pGj3r4rg4DjuWY/BFwbDh97u95oC0KEG4pr9jr7Op1uV75bGkDOB3o3eeHXtLLeMZ3v55q1JF+zPbwsjanTDwNXqSgYOwNns4TS10aIOHStwbbBNlWr2zQSfKAgKBwVkjTCwFWha1JWzV7Ye3Wrhrygt3F8ydg/tV4ZI4hctBlv8mHyIMhereZzBspYpxrd+6VoXRmb5F9eb9VKr6NyaK7Dm5Z/n1Xzk26Q3zveM2llzMVT/gDBp/7ar1J6wB0271Fr0NfZzgj7ejWU+Z76XYNaL44F552FV0nJixL2tUx2UibPUvqCCvve+fL1WQz7HlKhweKwOnucLsFvqi/iL3k29+ixIOAYDtBF7xPzEz+oVq6z76+aGL2lXqMLqS2MN9zVXQhMOgGycHjV6AhcHLQzQ4ShsJxzCUb47NlPvo+4qDs65nX3NQOXxr4l5zSPNHFNO93AXVF4zdg/+ty3dGSn2/HV7IJqHt/6+qaO7OlWXbpLL/dE/4U6CKH2qMvcbCa1GR1Bo12MQGDSwr6h8GMRvxHOLgqyGmvANh9RwUqVCbfKOGZxML6oImnyG+1x11/xuY0QajjcGjpEubexwr6Tv2j81g0dMTpdL00P6qp5HxvPip1njRBw0cmNms17UUFn36WbTyljey5OXNFOr6NwaF/vFMG1mN2mjbX6ZlahX1zj7Zr/gQM/CLvnvPEf/0bN03H2XULPL/Me7Yh6dknBc7NPg8YP205k+9wfrAgdN39FQWC/6JkVx1nSxNnTbri9zHNlGjUb2LxEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAQPHDviv0x5Zd+iDzpYaNfzMLE85W2NfeQdc526iO73YDtO0RYd8/H1Vkk0o/aPyKjpu/2TNO2PffogO7gaMRYJ7o1S43xLul3/YPsJ7eVR38l8I1zVpnXz+It0RNpyvoiJnLqMntylnS8dcrN/+93+80aoYUR9pr3a6+oRCwua9zjicWq+bpWjW7tfVvd4O4TzYXB3HN/ZyuwQe9rsHFxw/Cvo3qjMgF5r5Y79ZWpw4j3JjdvaQwXu7cRtjSvF5N1zl0XSVvxzrV9HSNan63KAhSlmwk+bWnzADzmDrq3UDnS51+R96S3f2QcyjsO+B1m11SHEwNH8Awafg4Aj28z4J7n9Pld71we5WqdxfFzOUEyf25erJJGSuj2Xm6SuFgd2Wk/WqyBGsXrW5Wvxnaj3NQf+27Yd9YQV/nwJWFfatSi1W3K6MRa+g8TqGl2yQlL0rYt3TulJTJs5S+gMK+XijXCebO/Kv+Py4VOEdP6nXneP/o0wMbcMVjjyfsm/9V827YrvzrE9pz6efSqzCClOavlc9v6HfttAT9osJyzo5GYK7SgKEZaDtm/kW6pPJJDX7gdtl963wQFpzBuUtOMd2BKJuZ1Hahzw/6lXMJ/Mxg5XQvxNuvzFqOulZv1/z3YP+ozr7VZUPXQTi7OMBtBEaLgqDOSY1gZUng3AiKhgOq1wf1bP5+OqH3yv5KhF/Ue8hdfwcuKfjPMOPYH3xTpFDJmyAg654jzj1u60Rszs/tqAqMmkMeftA2MlAbzG1x6Dk4V3At5po0uoqXzE+wr/MqWNPmc8ioeTrOpov5nDHu0X1XiusoevdDVnW2dWKExUs8zE7TobU1tXOZH7Dwa55izcZe20VXyhsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEFJhCEVEPhV8dhckLD2YNat9XtpLvjX/W55ReEmmSzFfZt+co8qvk6Rtg3Vlfh0PXGCfuGwsVFVWVeKe1WHHlMc0/ndXBNsxb2dbqzeh08nbDc9jZlrowoN1Vgzg+AThH0lBNAdAOl6zpVyCgaY891FHWaDV9x8fshtT5dOFZqe3/xR+F3N9pU64YVm04HH/ph36dbi38DbbCJNJB2w761avN/EfGQ2v7JvY6y5w5qLAr7TtfZrGsGr4NwdVq+XC6j9a5R46fW9Kh7xmCNmNc09G5NwSllHNNao2G31T+7dcuFOZhTdvdKtwN2lao3Zdz7JKRhhKarljeqdWDIv09zd7Nqe8nptJ1Syg3hzyzsm9OY14U5N6bLn6W10jtuqk4d/n0RqtH21gj7Nh3uVKPXDfr/2avLtu39MSPs+897lenJWL461LqrSY3PLHLv2Sqlnm1W1qvdP9b0XiQlL0rY1zK/SZk8S+kLJuz767eH9OIshHy9oPD+rwu/pv3eVzvy4eHXvzSTXzbpuGNBCO5RdvYtH/AtBAX/8D8H1PVt1BMuCEUW/0r6SXkBtOJx9/qjwnLOx374rFznYSOsZwQM7cFAu/nwyR43AGuECGdwbvtZphr9VRPjP2n0hxsa7M1q36FTWrXNC2iGunvOoLbYLn4I0TCZ6hKm/LzMWi63DvzjBvtHhX1LgpH+vs6LUXW9YwvWSvID6d06Yv5HpG8dGs8f1772nI+CrszeHMb4XhQ0jj520SVN8SaY7xjn39yl+gNnNThaeJYVHTrW/ETX7D0DlhZdo3mGYG6j5jC4FmNNGp2q4zzDCtuY91N0zWZ1ka+jXPx1Y57LcpTRi3rZDWA39f9kbDCpqx0nCs+lUMdi3yE07uw8tXOcsO8UNfvXVu65bFwKLxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBakQNmwrydy+7heaXK76Ta9ouORjYckwr5GZ1+/i/AyRYeXHeRHEfaVcgNpVZd08Vyk6tWNam7P6LKlk+fYp41+uK6qZF83FBseX93mBntH1LY6TnDWW1je9yD8Z4ZOvU+LvwedSc1t/bCvX0vxXvl31rBv3HMHQWbzvM5xp+NsqS7GUE5jY2MaOpdRZ/teNa1bqcVeULPKCOYaIcz0QLnDBvNlXlMsS/ew/Vvd+S7nXq6EefvZmDKbnJBuwScy6Ote/9jptGr8uQzfZ4vU0D6kzHZ3vKIgfQxgI0Cf2pSJ7gQdPpSxzrzrLHxPaf0X5bqJB/dcVdmAfeGEQ4fLdUYOFxXvfVLyooR9LfOZlMmzlL4wwr65Szp0cubdfL2g75rP9+tM/nnyQH3/cI77uk6O2nSnMxaE4B5l2HfD365p8IL96+qtnzQ+MTll8eO9pwrhtC3Gr6Q3OlTuzP5SeoyosJyzZaxQmT2s5wfiNhvBwNKz50es287g3BGnKR0evaFjH53W2vRUQcxQ+G4GtVmvtbQyw35qP9vu9rEya7ncOvAPFuwfFfbdc6H8Oo0MQ04GHWLru4L/ghzu6rYGLgsl2dee81ngPNXcGp8XBWGjj+1zxHgR1NGtPf+w39+DF25oaPQnTZT7SctY8xNdc6S7fw3B3FYU9jXuhbkZ9u3TYNklWea6rQH02zryZmHNmOvUY5zaeXbDvsUBZa8KviOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAgxQr7Or+h86sWrd7kBn7TLfoyIssVK+x7vqW0C64zGbE64QbB2GWhTruxzu0HcOd7Z193dY9ktHfdSi0KB3Tz71Na/N/2Kms0fw06xYYDh2Xe+0HPIDwaJ8QX3H9B+M8MnQafm6/s28YKqM4o7Ct557DWWKGzeUXlXo9d71TrhgbV/C7lB0eLg5XevBhhX/86q/TIw75eANVfA+WuZoF8dv+yWp/z5iulmh399o6+YY6xy+rYbsz1E9WqXbdXmZHChn6wOkZANnzo8u9zymzy6jXWUfmdpFDYN/VcWmnvup9sUn9Uj0oF93C858SQWp9x1/mU3aanKrrweVLyooR9LfOZlMmzlL4wwr4/tM9qV981/+jTAwczd0b7nW7B3cc0bMOd1lgQBnuUYd/i8OS0CpWMLptesHci26dqp3PllgFZc5jlQoRGkC+6PnvAMAg5Th1W/S06+05cGtAqt6OnF1Ksfv2E6vac1p7j59V7/bYmsgNux+HfIOx7+7zW5uub2i/+aimzlsutA+8Ek8H+xeshWANRQdHCIYzOvoeuaMI7rvv96ifhTqpBsNJ+3OC84Xtz9JTXLbpHXdMK/kcfO1R22bfBfRBaQ2X3snwYZ34UXfPUIdRgbu3WZoDaWJNGZ9yXT00HOrpmi0LpUJSL/+yawt2of+fZ8A9DWALot7Kqz9+X9uNO7Ty7Yd/i+7CUhxEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGFKxA37CtN6Mujq92Q7jL96YMzJf+W6yjGCdwG5zS64Do7z7ew70SvdrkB6Vf+Xu7fSa/q4L8UgtSvZMptN4N1+jCnoXOdant7vRqWLyoOji5vVtY99MjROvezOnW4IcP4Z308Yd8mN7hshm69IG5VudCpH4KtVdtN76qC4KF5PO9T87sXtiy7XUxn87hRr4fag46mXsB30fIa1dSvV/PBDvVfGdPQ4Vp3voyQphHCfORhXzr7Fk/fjU41LvcC2IWOvMUbTPfdkFqfLhy39vDQdA8SuV8Q8jfvjcjNCx8Y6yz1XJuGnMZ1RpfgJVv7I7oEB/dcvLCvlN29xF3nFdRXpvyk5EUJ+1omMSmTZyl9YYR9vzukeieUOytfL+rQt4Vfe//r1/vzx3z13OxFfaUgBBcOFNrmr6IxP6g2W7+O2epCyAAAIABJREFU/RcNHnY7lebDlL+o91Dh/bMd39tL82uwhNf8wFy5+uxhvYmzp+1B2ZIqghqXvnU+CGnP4NwlpwgP3Lukpi0Fl+o3+tT1w5hsjZPH+91OyZtDNjOp7UJf4BI0sA1XKPnbGcHK0q2KR77Lau3Obq3adlq94SRtfssya7ncOvDOEnndwRqo67zlbW35HoQnn/3Esh79IGW3jvwg6Yes6vLBylPqumc5XJlwq64MFELum0/oveu2facaC65pJvf9vA/76ob2ufeSdU6nYi43h1PuGyc4666lqGNdH9Sz+TVmXyd+Z+mdWTl/rR7qdDtN77uoccsxZyfsG7fmKbaz1McQAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAwhEIgrehTrc2gokvtT/tdvfd9Ce1nC/9B+c4Yd8v21csjM6+MroQ/1uvNRydZ/7hqNZ4oeBHFfYNz+fdy+rYVO0G6FJKn3E3OJNWKh+mXaLmc+Gdpno/ps51btDxuQ5FZ4Wd7VJa/FSNat52YsZBkDE1VdfS662qccO+TaeDeqYf9h1S2z+5NW/tDw5Y8irYrmzYN7xflHN4u/D7c82qdq9zUf1e9d+wt9IOQpBG2DeX0Xp337qj0bMgZdX8ZOHazWsaeremsC6m7KIazFtVWbvwxc3T9zfaVJdy11JqpZrPGC2zZ3rJfrC2kvvyslpX16hm+SIt2e3F+e2FBKHxRnXGLduvqbiD9NC7K93nSrXSA7Z1S9jXPgulo4R9S01E2NeCEhrauHFjaOTxvb138XU36PuiXu1+dQZfO/TXr74rdPV1cl//Ua81n7+odicoOGt/ygQkZ3oOP2BZLkxb2UmCTr59Grx3TTvzQTx7kC1/ZL+GUKDV+TAy3GnWFBGKNPbd0PuTuUPx6wmvxi5Vt38TfGbsH929MuLcwVGsr4IQb7fe/27Suo00qasdbqfZ2Qz7Gt2Xo13Mc1cQ9vXnMiIEaJiWdG/197WsA0/okhegDa9XYx7ezAaBbW8/77sf5nX2t7kHnXzru27LD1oeuGQNVqpcUHTiinbmQ5xdigy6e3VZvxvXZK5L67bRg/M/7GuE9bcP6qptWqN5VHYOy+7nfhi1bo217qylqD/+GovqfO6v2W4duRWsz6Z++zNtdsK+XSq5P/0LMJ4N0/L2D8QLBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBeS5QUdhX0sT5Fv3JDaYuS+/Xl6G87/DJdW6Qd6M+tzVrun1crzR5geF53tlX0nj/Fq3Ie61Wy1chLGdtTQ7r+Nte+HmZZquz70hPWg1P12jxM635hkXWZWyE9Ro/c0N5RlB0ya7yAUHbMYMOoeuVseX8nJ1yGTW5oUgvZNq/PVUICD4ZdBm2HX/ooBckLO7wOf2wr9E1tNy573aowQ3QejU79U3b2XZxxlgQ4m1U533jg6KXY+qod8OlVUbYV0aH5Q2ZiO6qkozgtHlNGogZ+L7eqpUWk6ISF8obM+i7vFEdN+Je+GW1/rcaLU5VqWgOQrv7677cGg3tI40F4funyzwHZKyjstuFTmA8P4o7SGfV7HU3Xp5WtuQ5UGnYd0itz3ghanOdh+qp4G1S8qKEfS2TmpTJs5S+IDr7Prj8Zj7su39aHTdtas5flC7pkNMp+It2Xa84cBZxzPxwssK+mgy6p+75yA1nul0prVcZFZZzNjYCc5UHbn9S5oDbZXh7nwbHbZPysy60e4FaJ5BsbDOjc1uv1B8cPdXjdteNCMU6W94+r5fdjqVLZzPsK6P7suNS8j9+4XNXEPY1gsTPfvBN6KcHf9XQ39yupJu7tO+Kz1F4cfu81rrhWGsIefKOjr3jzufmMmHfzV3aefbn0MGd+9PYPx0dCh3vdbsp7xnQPvd8O8/+Unq8/Ei5QK7hvLlHR24Vun+XHCj3jfZs79Lv092q/+SGYVbu2CVHiRyY/2Ffyf8Bg81devlEVLD2Zw1+cEJLt51Q3ZuDuuD8mof8nxk6Rz2/jOfH0i2n1TvFfbbK1mk6X99P6tpXWPcvfzSgl/P3SFTnbGm2wr5LYzwb6st20XZ5+YYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILBgBSoN+0oT6v23IJy6+uOrxXa3juoFNwy8onm/ztx2A64PJzScPah1W5dpWdMKNwA7/8O+RWHephe0f/CGxt1/B524/aU+2FvjhqMLAejZCvvmepr8Dr1Np23/ECrpXLOW5MOaZrfQnDKb3OBtVa1ao0KL9/vV9GSVFi2v0crdRkdcIxRbvb3fGjQNuog2qOOuu3yMTra1B53fp2r5YwQqU+s6ZTYgnUnY1wm9+qHVw7Zz59S/3euCXBzKnL6z5fqMoewObw6iQ9O5gbTf/beqKOwrjX3a6M5/VHfVIbU9552j+JryHX+9sObqVg35/25vFChj/1SjOr15NDdZMK/NMGqd2qLumQgPP+geFcw31v3KqHsj4tjBOkip8VPzjgl2GOtpcp8D4XUQbGN9FRn2lXJngrVZvSMbeg5UFvYdOlznruUqpTaVCa9bi7QPJiUvStjXMn9JmTxL6Qsi7Kuf+vTm52tU39OuS2MRITwbTpmx4Quvq9455n9cKrPVdD4qH/YNwnzhAGSMc/lBtWnsG3l4o/OjG94s19lSxq+x35MN/diQEZirPOwbCq1uO6X3L93WRP4vC5Oa+PF7HTvYrWq3xlXtoXDqTM8d6SPJ79bZpeq3BjR4ywunTmriwW1d6DqtOj/o6wT9Qt1uZ1rbvUtq8o6fPq1j18c04eScJ3/RcHZQa7cFodqlmysI+8oIWG/uUt1fBnVs8JoGB89r318C66XWsG3QtXTplm7ty94p1KRfNXHrmt7bU6ip2q27eD0Ygc38fJ7Q9p7vNVq4KE3cvqH33cDk0s0ntOeS522ZpHsXtcFdE0ud71v6NGj5IcjCnsb17slqOPwX0dwVt7O1c5xuoyZJD3/W8KWstqc96x4du22Ezct1DbaUHTUUPB9Cayhqh6hx/1lR7jjGPIS6EU8dQg2ec1FdZYNrCa/Jn9V7yHPsUt3B87r6oxfQdtbPDR1p9UL9XXr55B3jKqNr9jYKzmt5Tka5GPdofh2F77NLZ7XBu8/SA7oQ8d/ATg1+AN1dl9WHrxmhcK/KwvepnZ0W9APuDxuE5tJas9tBOPxsmKLm4qp4hwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggsBAFKg/7Op1Ze7XL7877go4W/VblCV099oIb5vU6+Brft27U5+eP65V8IHgBhH2dRZX7Uvt3BAHpZV5n5Pz3FVpztEX/6o7NVthXMgOIK5X+7LLGvH/vfJjTyLk2NfqBzjaNmIv/fkbr3c67VakarW/PFu07NNAa7FtVWxJuHHrX676b0sodnbrsBUFzY8q2N/oB1ZXvmsFaM1CbUs3WDl2+6xb8MKehnmbVPuF19ywNVM4o7Cunu68X5nVqzmjIi8Xczart1Ro/bFhVFQ5EzsDZNA+9DkLEVap+qU1Zz8JZTiOX1bmrVovcrrpOTeGwrzP/fpg35c6/m5XI3ezX3vpFhU7KEZ15zSBx6qkmdVwZ8wObuRsZNa/29k+prt2cx9CFLIC3QTfrqvx6z/RkVP4rqxHvXnR8jLB50VyH1n3quTZrl27z/MUddp2DG+ugqlqN5r18f0j9bzf46yjq+JFTWCbsK+XUv3WJu8aq1XzOPIoR9v3nvZFWne17td5fZ1WqStWp46Z5nOm/TkpelLCvZY6TMnmW0hdG2NdpGntxRz6cu8bpxjtrX6+rKEdmA654LAjBLQ0F6JxDlQ2iTXUuP/RlCbFNtW+5z3/Iqs4PTDq/fr7MxhPXgkCkF2b7yP1xFCN8VhzuNI9XPqw38V02CNX5NQXBQCeIV/fBFY2aOUvn8LNwbrPK4tc/68JHRvjVVte2UzqSzfrB0z2XjAJnobapXDZ81Od22g0HK4uvpOTdvWva6QdYi53zocdtp9RVFGoNjjBx/azqvRCyxWTDqW90zA39Fq8HYw38pS/6/E7gtt8MegbnDl79UhQcLResdPYZ7gq6Feevb3OPukaDo+n2JSPQa/FwrnNLt977OhxANq7Jct8bZyj7Mng+hIKdZfeyfOg/K8odJ7rmqUOowXOu8rBvoXNzxgj0Fuai1Lv+k+9DQdnomj2FwNDynIxyMe7RDYf7tMqynp0aq984rUzE/eCdXxNXtNPYf2fWCzL7W/gvpnaOE/bt0fs9A2Vq7lPvj8bzyD87LxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAIBKYV9nWa4fRvCQK9u49rOPRPU+Pffq6WvWv0RzcUXJNepy3HzmjUCRzeWWBhX4f74ai+zLRo444/um41Wr17iz7IOv9w/aVaZj3sK+lGhxq8gGxRMNQNzVZVyQlyZrwwbrAspBudRqA32L4QLPVCtyvVfM5MLXoHyCm7e2VRQLZov6qUanb0F3XmLew5pszW4mBt8X5VqnqiQa1XSs8507CvVP7cqafSSr9UuO7a9qJo9MycPbKS70PqCAVywxbO3HV+mna7staqLRyEvN+v9FNB997i/Z05SKsxIuzrlDP2RZNqvNC3df0sUsPBy34IuOQSFsRAVs1PRtwfVjNn29K5Gvo0CMEXz1Ph2KlnmxXuy+jxlg/7Srp/Wa1TrKVF9a267AXcvQNP9b1s2Nc5r/FDA8ublfWPZ4R9I41Cpk/Uaq/1WeMftKIXScmLEva1TGtSJs9Sul577TVNTob+tmTbcAZjzvGd8/zWfx58d0xvdtfPSti3/os31fmfDx7BJQUhuMSEfWV0ad13UeNTqdy6qJ1vnvC77C5956LyeUkjMFcc7jQPOHVYz/nL7YWeAW14IzhH9esntOFQVoO3I4Jzs3Vus9Si179qNJvVzn0n9Acv4LrlhOr29enI4G2N52/BwPHZju+DvWertoejGjx+Wmu9cG7+/APq+va+EXauMOzrVBk+7uYu/T7do6bjVzRUuLDgWsKvxm+qq/2U6l53g5rbTmjtgbMaHHU6cAdzXbwegvH8PeKcvyM4Rn6u2y/q6r14Xbwnsn3uWjyh976e4lk4eV8Xjp/SKq9L6+Yu7bsSuqiHP+lq76Ca9pzQ743Q5qqdp7Sz65qG8x2IQ/sY12q778NbR70PgqrlQrpRexvjUaFWYxNzfsI1Tx1CDZ5z0wr75uv4VePXL2rfgZ6i+fh9urvMvR5aO0XXU3gTGE4v7Jtfq6PX9J5xr//hjVPa0/u9/2t0LKc1hn7R4GH3ftg+qKtlluTUzvHCvsfuSBPfXdQeo+bfp09pT8+Nwv9JYlTHSwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmKsCQdh319nIX2k7veIfjqi/Pa2Gp6v9Dp5VVYtUvbpRzR9nNRb+zbjmWR6OKftxsxpXh/Z9ukHrD2aKu5Oa+7mvx653au+6WlV7gePUYtXUp9U2EArLhvYt2S9GvTMP+xaKGLvYoXR9jRa7IddFy2vV+HZGIw8l7xwlYV9n15k4h64/eDumy2H/J6pVu645MMxl1OTW2vDxWLCr98qr66nFbvi6MPd7e5w5CEKX1mtyjjE2pM63G1W73OvkW6W8ya4OZW0hce+8C+W7EXi1hXTtY6Vh3zzXSKb0flndqL2fDVmC8QHwlGHf/KY5jfTsVePqYG1XPVEd634MzhR6ZVx7aUfhwrZjnzb6of+gk3ew7uw+btDXqc95TrX35++/0Nln9DYpeVHCvpZpTsrkWUpXS0uLnPof5R/n+M555syfnx/owfgMvv6/eCHCOXO9j7yQIKS6odf9NfCP/JycYOEKTB3YrMTGD/tOEays5Jhsu8AEYgXy45oEYd+i0H/c3dkOAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTmmcCoPt/7R63580bt+o/h6Gv74ajW5Dv7/kkHr0dvxicIzK5AELpc/0Vpt+TZPRdHQ2DuCCQlL0rY17JmkjJ5ltLV19en1tZW20ezNuYc3zkPf+apwA9Z1eU7mJ5W7yz/cNg8FeOyZiQwm2HfX9R7qNBFta7z1oyqYucFLDCbYd+JK9qZf55268gPC9iUS0cAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAV/g6sd/0jInyPvnD3TD+ttBJ3TmA3ebfzlY9jeI+gflBQJlBUbUUb9YNU4H5k+Hore83qqaKqeL6hI1n4vejE8QmG8CScmLEva1rLykTJ6l9PzQhx9+mA/8OtcxOWn9W0HUrpHjznGc4zlBX+f4/JmvAj9r8IMTWrq5S9WHr4ms73yd57l0XbMY9r19Xi97wUqyvnNpkpNVy6yFfSc1fLIn/zxd+mZWZX4mN1k+VIsAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAjMTuH5Qq/Nde5dp9V+P6+qPQTpj4scb6j36glbkP1+hV/4+OrNzsTcCrkB29xJVOUHep/fqslUlp/6t7jZPNitr3YZBBOanQFLyooR9LesvKZNnKd0fcjrvtrS06LXXXtPGjRtn/OUcxzkeHX194vnzYvyGMr3XNHjhkt7/S7eqCUvOn7lNxJXMLOw7nD2v3gvX1NszoLXbCl19CaonYuLnbpEzCvveVm/PFQ1euKJj7T36ff552qWd2V/m7vVSGQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIPHaB4b9vVI0b+M13+S15XaN1x67SpO2xz8w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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main Jupyter interface gives a view of all the folders, notebooks (with the .ipynb extension for IPython Notebook), and other files available in your workspace, and will look something like this:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "You can click on any notebook to run it.  To create a new Python notebook, use the New -> Python 3.6 button in the upper right.  Note that you can also launch a new R notebook if you want to explore the Jupyter environment within R.  You can use New -> Terminal to launch a unix terminal, which can be handy for managing your computing environment.  \n",
    "\n",
    "You will also notice a tab for Notebook Extensions.  With its usage growing within the data analysis community, Jupyter enjoys a lot of active development to improve the computing environment.  If you find yourself using Jupyter notebooks frequently, these extensions can be a useful way to improve your workflow and productivity.  Here's a neat article on some recommended extensions you might find useful: https://towardsdatascience.com/jupyter-notebook-extensions-517fa69d2231."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run a cell, you can either hit the \"Run\" button up top, or type `Shift-Enter` to run a cell and select the cell below.  It's also sometimes useful to use `Alt-Enter`, which runs current cell then <i>creates</i> a new cell below, or `Ctrl-Enter` to run and remain in the current cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "#Type Shift-Enter to run the cell and view the output\n",
    "print('Hello')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also several useful options in the Cell menu, such as running all cells above, all cells below, or all cells in the entire notebook.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserting New Cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the advantages of Jupyter is that you can tinker and execute your code in chunks, running one cell at a time.  To add new cells at any point to play around with some code (a great idea!), you can use the Insert -> New Cell Above/Below menu options, or you can use the `+` button (next to save button) to insert new cell below.  You can also use the other handy shortcut buttons to cut and paste cells or move them up and down throughout the notebook.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interrupting Running Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you run code and get stuck in a long or infinite loop (we've all been there), you can kill the running code by hitting the STOP button (next to the run button). If the whole notebook seems to be frozen or just doesn't seem to be acting right, it might be worth restarting the Kernel using the Kernel menu options above.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tab Completion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use tab completion to view a variable name with just the first few letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run this cell to load the variables\n",
    "player_name = 'Tiger Woods'\n",
    "player_city = 'Jupiter, FL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment following line, then type TAB after the 'player' to see the different possible variables\n",
    "#player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tab completion also can let you know the avaialble methods (functions) or attributes (characteristics) of a given object.  Place the cursor `player_name.[HERE]` and then hit TAB to see the available string methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment following line, then type TAB after period\n",
    "#player_name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tab can also be used within a function to learn more about a specific function.  Move the cursor `my_name.split([HERE])` and type \"Shift-Tab\" to see documnetation and options for this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tiger', 'Woods']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Type \"Shift-Tab\" within the parentheses\n",
    "player_name.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tab completion is also useful to get the name of a method within a function.\n",
    "#Move cursor to after the word 'max' and hit TAB to reveal the function option called 'maxsplit'\n",
    "#player_name.split(max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tiger', 'Woods']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Having learned about the split() method, we can try it out!\n",
    "player_name.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a ? after any variable or function to learn more about it.  Run any of the cells below to display the help info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_name?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_name.split?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Use wildcard to search for a method\n",
    "player_name.*find*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use double ?? to get full source code (if available)\n",
    "player_name.rfind??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Magic Commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter provies many useful shortcuts we can execute using a % symbol, known as magic commands.  These are not python commands, but rather are jupyter/IPython commands that facilitate common tasks or provide useful information about the running python code.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-11-10\n"
     ]
    }
   ],
   "source": [
    "#Run another .py file\n",
    "%run scripts/print_todays_date.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41.6 µs ± 4.99 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "#Use timeit to see how long a script takes\n",
    "import numpy as np\n",
    "a = np.random.randn(100,100)\n",
    "%timeit np.dot(a,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\matthew.d.smith\\\\Documents\\\\Data Science\\\\Army_Python_Data_Analysis_Tutorial\\\\Army_ORSA_Python_Data_Analysis_Tutorial'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Can also use magic command with command line commands, such as pwd (to show present working directory)\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'pwd' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "#Can also trigger terminal commands with !\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Magic commands are also critical to the use and integration of the visualization package matplotlib in Jupyter using the `%matplotlib` magic command, as discussed more in the visualization lesson.  To view and explore the full list of commands, see https://ipython.readthedocs.io/en/stable/interactive/magics.html.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keyboard Shortcuts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These can be very useful.  See Help -> Keyboard shortcuts for full list.  \n",
    "\n",
    "As an example, type Esc-a (Press and release ESC, and then press and release \"a\") to insert new cell above, or Esc-b to insert new cell below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Markdown offers many powerful ways to add pretty formatting to the notebook.  To inspect the markdown code, go to a cell and hit 'Enter' to get into edit mode.  To convert the markdown code back into the formatted display, simply \"run\" the cell (e.g. with Shift-Enter, or clicking the Run button).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make equations in Latex:\n",
    "\n",
    "$$\n",
    "y = \\begin{bmatrix}y_1 \\\\ y_2 \\\\ \\vdots \\\\ \\\\ y_n \\end{bmatrix}_{n \\times 1}\n",
    "=\n",
    "\\begin{bmatrix}\n",
    "1 & x_{1,1} & x_{1,2} & \\ldots & x_{1,p} \\\\\n",
    "1 & x_{2,1} & x_{2,2} & \\ldots & x_{2,p} \\\\\n",
    "\\vdots  & \\ddots \\\\\n",
    "\\vdots \\\\\n",
    "1 & x_{n,1} & x_{n,2} & \\cdots & x_{n,p}\n",
    "\\end{bmatrix}_{n \\times (p+1)}\n",
    "\\begin{bmatrix}\\beta_0 \\\\ \\beta_1 \\\\ \\vdots \\\\ \\\\ \\beta_p \\end{bmatrix}_{(p+1) \\times 1}\n",
    "=X\\beta\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make lists:\n",
    "- point 1\n",
    "- point 2\n",
    "  - sub point\n",
    "  - sub point\n",
    "  \n",
    "Format text using html-like tags to <i>italics</i> or <b>bold</b>, or use shortcuts to *italics* or **bold**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also highlight when you have a code snippet with backticks (same button as the ~, in upper left), `print('Hello')`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can even embed images or GIFs (foreshadowing of Lesson 2 on the Pandas data management library..)\n",
    "\n",
    "<img src=\"Images/angry_panda.gif\" width=\"500\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For much more, see [this Jupyter markdown tutorial](https://www.datacamp.com/community/tutorials/markdown-in-jupyter-notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Python Syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python explicitly uses indentation, not braces, to parse the written code.  After a colon follows a block of code, all of which must be indented by same amount of whitespace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That is True\n"
     ]
    }
   ],
   "source": [
    "#So this will work:\n",
    "if (2>1):\n",
    "    print('That is True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#But this won't work (uncomment next 2 lines and run to see):\n",
    "# if (2>1):\n",
    "# print('That is True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments use a #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is a comment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's no built-in way to do multi-line comments, but a couple tricks you can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First trick is to use a multi-line string in between three single or double quotes\n",
    "\n",
    "'''\n",
    "This is a multi-line string,\n",
    "but python will just see it and ignore it\n",
    "so we can use it as a comment\n",
    "'''\n",
    "#Resume code\n",
    "a=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "#Another trick is to use jupyter shortcut to add or remove comments from a range of lines\n",
    "#Highlight the following lines then press CTRL and the \"/\" key together to comment all lines\n",
    "#Then press CTRL-/ again to uncomment\n",
    "\n",
    "a = 1\n",
    "b = 2\n",
    "print(a+b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assigning Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables are assigned using a simple equal sign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also assign multiple variables at once as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2.2 three\n"
     ]
    }
   ],
   "source": [
    "a, b, c = 1, 2.2, 'three'\n",
    "print(a,b,c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can assign multiple variables to the same value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 7 7\n"
     ]
    }
   ],
   "source": [
    "a = b = c = 7\n",
    "print(a,b,c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While python has quite a few built in data types and coding structures, nearly any python data analysis workflow will require importing packages to handle the specialized tasks.  Thankfully, the syntax and mechanics of reading in packages are easy, as shown below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.141592653589793\n"
     ]
    }
   ],
   "source": [
    "#We can import an entire package to use it:\n",
    "import math\n",
    "print(math.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we run `import package`, we import the entire package, including all its modules, and sub-modules, and sub-sub-modules, etc.  Sometimes it makes sense to only import a specific module from a package if that's all we need, which we can do with the following syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import sqrt\n",
    "sqrt(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It's also common to use an alias for modules we import\n",
    "import numpy as np\n",
    "\n",
    "#Now we can reference any numpy modules off of np\n",
    "np.sqrt(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a package is not available in your current computing environment, you may have to *install* it into your environment before you *import* it into your current workspace.  For example, the leaflet package is not automatically installed in the current Azure notebooks environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This might not work if leaflet pacakge is not installed in the current python environment\n",
    "#import leaflet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you run into this issue, you can simply install the package.  All python pacakges are available from the public package repository, known as [PyPI](https://pypi.python.org/pypi), which stands for Python Package Index.  We can run the `pip` command from the terminal command line to install the package into our current environment, or we can use the ! command to run a terminal command directly from the notebook as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment and run to install leaflet\n",
    "# !pip install leaflet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we should be ablet to import and use leaflet\n",
    "#import leaflet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Types\n",
    "Python has built in data types to handle individual items, such as numbers, strings, or booleans, as well as composite data structures to handle a collection of items, including lists, sets, dicts, and tuples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Number can be int or float types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(int, float)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "b = 1.2\n",
    "type(a),type(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Even though ints and floats are different types, python will usually transition naturally from one to the other when it makes sense.  Of course, sometimes the distinction matters so always good to understand what type you are working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3333333333333335 <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "a = 10 #int\n",
    "b = 3 #int\n",
    "c = a / b #int or float?  \n",
    "print(c,type(c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "In addition to basic +, -, *, /, here's the sytax for other arithmetic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2**3 =  8\n",
      "10%3 =  1\n",
      "10//3 =  3\n"
     ]
    }
   ],
   "source": [
    "#exponents\n",
    "print('2**3 = ',2**3)\n",
    "\n",
    "#modulo (remainder)\n",
    "print('10%3 = ',10%3)\n",
    "\n",
    "#Divisor (floor division)\n",
    "print('10//3 = ',10//3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Any arithmetic operators can also be used to update a variable when joined with the equal sign."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 10\n",
    "a += 10 #same as a = a+10, and results in a = 20\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Any of these will work as well.  Create a new cell below and try them out.  \n",
    "- `a -= 5`\n",
    "- `a *= 5`\n",
    "- `a /= 5`\n",
    "- `a **= 2`  Same as a = a**2 (a squared)\n",
    "- `a %= 5`  a = a%5, or a modulo 5\n",
    "- `a //= 5` a = a//5, or floor division"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Python has a tremendous amount of built-in functionality to work with and analyze string data, which is cited as one reason for its popularity.   \n",
    "\n",
    "Strings can be defined with single or double quotes.  This flexibility is nice, and also means we can create a string that contains one of the quote types if we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "He said \"Hello.\"\n"
     ]
    }
   ],
   "source": [
    "s1 = 'this works'\n",
    "s2 = \"this workds too\"\n",
    "#Make a string that contains double quotes by enclosing in single quotes\n",
    "s3 = 'He said \"Hello.\"'\n",
    "print(s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also define an *empty string*, which can be useful way to indicate that we have a string object which doesn't have any data yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_empty = \"\"\n",
    "s_empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also create a multi-line string using 3 single or 3 double quotes, which can be a useful functionality for natural language processing, formatting output, or several other applications. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\tI want an officer for a secret and dangerous mission.\n",
      "\tI want an Army ORSA.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "s = \"\"\"\n",
    "\\tI want an officer for a secret and dangerous mission.\n",
    "\\tI want an Army ORSA.\n",
    "\"\"\"\n",
    "#The '\\t' codes a Tab spacing when used in the print command\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can automatically inject variable values into a larger string using the `format` funtion, which is very useful for creating properly formatting strings for display or coding purposes.  The syntax is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Value of name is 'Tiger Woods'\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The input to the format function is injected inbetween the {} brackets\n",
    "name = \"Tiger Woods\"\n",
    "\"Value of name is '{}'\".format(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`format` also has many options for how to display and format numerical data, as shown below, or see https://pyformat.info/ for more info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is 1.123\n"
     ]
    }
   ],
   "source": [
    "a = 1.12345\n",
    "print(\"a is {:.3f}\".format(a)) #Format as float with 3 decimal points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123456789\n",
      "123,456,789\n"
     ]
    }
   ],
   "source": [
    "#Pring number with commas\n",
    "b = 123456789\n",
    "print(b)\n",
    "print(\"{:,}\".format(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name =  Tiger woods \n",
      "\n",
      "name.upper() =  TIGER WOODS\n",
      "name.lower) =  tiger woods\n",
      "name.title() =  Tiger Woods\n"
     ]
    }
   ],
   "source": [
    "#We can manipulate the case, which can be useful for matching and cleaning string data\n",
    "name = 'Tiger woods'\n",
    "print(\"name = \",name,\"\\n\")\n",
    "\n",
    "print(\"name.upper() = \",name.upper())\n",
    "print(\"name.lower) = \",name.lower())\n",
    "print(\"name.title() = \",name.title())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can grab a substring from a string object using the format `name[start:end]`, which makes this a good time to bring up an important point in how python handles indexing in general:\n",
    "- The starting index of the first element of any python sequence is always 0, not 1.  You can think of the index as specifying the *offset* from the beginning.  \n",
    "- The ending index specifies the first item that we *don't* include, meaning we go up to, but not including, the end index\n",
    "\n",
    "Hence, our name will have the following indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run script to import useful function called display_indices\n",
    "%run scripts/useful_functions.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><td>0</td><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td><td>7</td><td>8</td><td>9</td><td>10</td></tr><tr><td>T</td><td>i</td><td>g</td><td>e</td><td>r</td><td> </td><td>w</td><td>o</td><td>o</td><td>d</td><td>s</td></tr></table"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from scripts.useful_functions import display_indices\n",
    "display_indices(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tiger'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How would we get just the first name?  We start at index 0, then stop before reaching element 5\n",
    "name[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name[:5] = \"Tiger\"\n",
      "name[5:] = \" woods\"\n",
      "name[-1] = \"s\"\n",
      "name[-3:] = \"ods\"\n"
     ]
    }
   ],
   "source": [
    "#If you leave off the start, python assumes index 0 by default\n",
    "print('name[:5] = \"{}\"'.format(name[:5]))\n",
    "\n",
    "#If you leave off end index, python just keeps going until the end\n",
    "print('name[5:] = \"{}\"'.format(name[5:]))\n",
    "\n",
    "#You can also go backwards from the end using negative index\n",
    "print('name[-1] = \"{}\"'.format(name[-1]))\n",
    "print('name[-3:] = \"{}\"'.format(name[-3:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Try to grab just the last name:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name.find('oo') = 7\n",
      "name.find('o') = 7\n",
      "name.find('xyz') = -1\n"
     ]
    }
   ],
   "source": [
    "#We can search for substrings with the find function, which returns the index where substring starts\n",
    "print(\"name.find('oo') = {}\".format(name.find('oo')))\n",
    "\n",
    "#Note that find returns only the FIRST occurence of the substring\n",
    "print(\"name.find('o') = {}\".format(name.find('o')))\n",
    "\n",
    "#If substring does not appear, find returns -1\n",
    "print(\"name.find('xyz') = {}\".format(name.find('xyz')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Some other useful string functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s = ' apples, oranges, bananas.  '\n",
      "\n",
      "s.strip() = 'apples, oranges, bananas.'\n",
      "s.strip(' .') = 'apples, oranges, bananas'\n",
      "s.lstrip() = 'apples, oranges, bananas.  '\n",
      "s.replace('apples','kiwis') = ' kiwis, oranges, bananas.  '\n",
      "s.replace('apples','') = ' , oranges, bananas.  '\n",
      "s.count(',') = '2'\n"
     ]
    }
   ],
   "source": [
    "s = \" apples, oranges, bananas.  \"\n",
    "print(\"s = '{}'\\n\".format(s))\n",
    "\n",
    "#remove any 'whitespace' from beginning or end of string\n",
    "print(\"s.strip() = '{}'\".format(s.strip()))\n",
    "\n",
    "#If we want to remove any spaces OR period:\n",
    "print(\"s.strip(' .') = '{}'\".format(s.strip(' .')))\n",
    "\n",
    "#Or we can only strip leading characters with s.lstrip(), or rear characters with s.rstrip()\n",
    "print(\"s.lstrip() = '{}'\".format(s.lstrip()))\n",
    "\n",
    "#Replace substring\n",
    "print(\"s.replace('apples','kiwis') = '{}'\".format(s.replace('apples','kiwis')))\n",
    "\n",
    "#We can REMOVE a substring by replacing it with an empty string \n",
    "print(\"s.replace('apples','') = '{}'\".format(s.replace('apples','')))\n",
    "\n",
    "#Count occurences\n",
    "print(\"s.count(',') = '{}'\".format(s.count(',')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "One subtle point you may have noticed is that running a command like `s.strip()` doesn't update the variable s itself, it only returns a *new* string value which we can choose to use or ignore.  To actually update the string, you need to re-assign the output of the function back to the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " apples, oranges, bananas.  \n",
      "apples, oranges, bananas\n"
     ]
    }
   ],
   "source": [
    "s = \" apples, oranges, bananas.  \"\n",
    "s.strip(' .')\n",
    "#s hasn't changed\n",
    "print(s)\n",
    "\n",
    "s = s.strip(' .')\n",
    "#Now s is actually updated\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Another useful string function is split(), which splits a string into a list of substrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s = \" apples, oranges, bananas.  \"\n",
      "s.split() = ['apples,', 'oranges,', 'bananas.']\n",
      "s.split(sep=', ') = [' apples', 'oranges', 'bananas.  ']\n"
     ]
    }
   ],
   "source": [
    "s = \" apples, oranges, bananas.  \"\n",
    "print('s = \"{}\"'.format(s))\n",
    "\n",
    "#By default s.split() will split on spaces\n",
    "print(\"s.split() = {}\".format(s.split()))\n",
    "\n",
    "#Note that the first element is 'apples,', a string that CONTAINS a comma\n",
    "#To choose characters we split on, use the 'sep' argument\n",
    "print(\"s.split(sep=', ') = {}\".format(s.split(', ')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The join() method will join, or combine elements of a sequence into a single string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apples - oranges - bananas'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" - \".join(['apples','oranges','bananas'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can also check if our string startswith or endswith anything in particular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \" apples, oranges, bananas.  \"\n",
    "\n",
    "s.startswith('apples') #False\n",
    "s.startswith(' apples') #True\n",
    "s.endswith('bananas') #False\n",
    "s.endswith('.  ') #True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Booleans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Booleans in python are designated with the protected words True and False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True False\n"
     ]
    }
   ],
   "source": [
    "a = True\n",
    "b = False\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = True, b = False\n",
      "a&b = False\n",
      "a|b = True\n",
      "a^b = True\n",
      "not a = False\n"
     ]
    }
   ],
   "source": [
    "#Boolean expressions\n",
    "a, b = True, False\n",
    "print('a = {}, b = {}'.format(a,b))\n",
    "\n",
    "#a AND b\n",
    "print('a&b = {}'.format(a&b))\n",
    "\n",
    "#a OR b\n",
    "print('a|b = {}'.format(a|b))\n",
    "\n",
    "#a XOR b\n",
    "print('a^b = {}'.format(a^b))\n",
    "\n",
    "#negation\n",
    "print('not a = {}'.format(not a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### None Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Python has a None type, used to indicate that a variable exists but isn't assigned a value yet.  This is similar to the NA type in R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None <class 'NoneType'>\n"
     ]
    }
   ],
   "source": [
    "a = None\n",
    "print(a,type(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Using None is a useful way to take some action only if a variable exists.  For example, often times a function argument will have None as its default value, meaning it will get ignored unless the user specifies a value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b exists!\n"
     ]
    }
   ],
   "source": [
    "a = None\n",
    "b = 3\n",
    "\n",
    "if a:\n",
    "    print('a exists!')\n",
    "    \n",
    "if b:\n",
    "    print('b exists!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Type-Casting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Python allows variables of one type to be cast in another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_int = 10\n",
    "my_float = 20.0\n",
    "my_str = '30'\n",
    "my_bool = True\n",
    "\n",
    "#Try these to see what happens\n",
    "int(my_float) #20\n",
    "float(my_int) #10.0\n",
    "str(my_float) #'20.0'\n",
    "int(my_str) #30\n",
    "int(my_bool) #1\n",
    "int(not my_bool) #0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Note that the boolean casting essentially gives us a way to indicate if a variable exists and contains data.  This can be useful when we start building control structures, such as if-else statements that should take a certain action if the variable contains data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bool(\"string data\") = True\n",
      "bool(\"\") = False\n",
      "bool(5) = True\n",
      "bool(None) = False\n"
     ]
    }
   ],
   "source": [
    "print('bool(\"string data\") = {}'.format(bool(\"string data\")))\n",
    "print('bool(\"\") = {}'.format(bool(\"\")))\n",
    "print('bool(5) = {}'.format(bool(5)))\n",
    "print('bool(None) = {}'.format(bool(None)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are one of the workhorse data types in python.  Lists are a sequence of python objects, defined in square brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_list = [5,4,3,2,1]\n",
    "type(num_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 2, 1, 0.5, 'Blast Off!', [1, 2, 3]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lists don't have to contain only one data type\n",
    "#Each list element can be any python object, even another list\n",
    "x = [3,2,1,0.5,'Blast Off!',[1,2,3]]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What's the length of this list?  6 or 8?\n",
    "#Length is only 6, because the last list [1,2,3] counts as a single element of the list x\n",
    "len(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed earlier, indexing in python always starts with 0 for the first element, and the ending index specifies the first element to ignore, i.e. we stop before reaching the end index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name_list =  ['Alice', 'Bob', 'Charlie', 'Denise', 'Eddie'] \n",
      "\n",
      "name_list[1] = Bob\n",
      "name_list[0:3] = ['Alice', 'Bob', 'Charlie']\n",
      "name_list[:3] = ['Alice', 'Bob', 'Charlie']\n",
      "name_list[3:] = ['Denise', 'Eddie']\n",
      "name_list[-1] = Eddie\n",
      "name_list[-3:] = ['Charlie', 'Denise', 'Eddie']\n"
     ]
    }
   ],
   "source": [
    "#List indexing starts at 0\n",
    "name_list = ['Alice','Bob','Charlie','Denise','Eddie']\n",
    "#Make a copy for later\n",
    "name_list_copy = name_list.copy()\n",
    "print('name_list = ',name_list,'\\n')\n",
    "\n",
    "print('name_list[1] = {}'.format(name_list[1]))\n",
    "print('name_list[0:3] = {}'.format(name_list[0:3]))\n",
    "\n",
    "#Python assumes start is 0 if not specified\n",
    "print('name_list[:3] = {}'.format(name_list[:3]))\n",
    "\n",
    "#Python assumes end index is the end of the sequence if not specified\n",
    "print('name_list[3:] = {}'.format(name_list[3:]))\n",
    "\n",
    "#We can use negative indexes to go backwards from the end\n",
    "print('name_list[-1] = {}'.format(name_list[-1]))\n",
    "print('name_list[-3:] = {}'.format(name_list[-3:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can find the index of a given item with the index() method\n",
    "name_list.index('Denise')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many built in functions to work with and manipulate lists.  A few key ones are given below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Bob', 'Charlie', 'Denise', 'Eddie', 'Fred']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add new elements to end with append\n",
    "name_list.append('Fred')\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice',\n",
       " 'Bob',\n",
       " 'Charlie',\n",
       " 'Denise',\n",
       " 'Eddie',\n",
       " 'Fred',\n",
       " ['Grace', 'Henry', 'Ignacio']]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What if we want to add a few more names?\n",
    "new_names = ['Grace','Henry','Ignacio']\n",
    "name_list.append(new_names)\n",
    "#What is name_list now?? Take a look\n",
    "name_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm, that's probably not what we wanted.  Lets delete the last element, then try again to add it.\n",
    "\n",
    "There are a few different list methods for removing elements:\n",
    "- `name_list.remove(x)` removes item x from the list, e.g. `name_list.remove(new_names)`\n",
    "- `del name_list[i]; del name_list[i:j]` deletes the item at index i, or items in index range i to j, e.g. `del name_list[-1]`\n",
    "- `name_list.pop(i)` pops out the element at index i.  That is, it removes that item from the list AND returns it to the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Bob', 'Charlie', 'Denise', 'Eddie', 'Fred']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove the list of new_names from name_list\n",
    "name_list.remove(new_names)\n",
    "\n",
    "#Note: we also could have removed the last element with name_list = name_list[:-1]\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Charlie', 'Denise', 'Eddie', 'Fred']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here's how del would work\n",
    "del name_list[0:2] #Remove the first 2 elements\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fred ['Charlie', 'Denise', 'Eddie']\n"
     ]
    }
   ],
   "source": [
    "#Using pop\n",
    "deleted_name = name_list.pop(-1)\n",
    "print(deleted_name, name_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset name_list to its original version, and try again to add the list new_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Bob', 'Charlie', 'Denise', 'Eddie']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_list = name_list_copy.copy()\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Bob', 'Charlie', 'Denise', 'Eddie', 'Grace', 'Henry', 'Ignacio']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combine two lists with the + operator\n",
    "name_list = name_list + new_names \n",
    "#We also could have done name_list += new_names\n",
    "#We also could have done name_list.extend(new_names)\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Bob', 'Reggie', 'Denise', 'Eddie', 'Grace', 'Henry', 'Ignacio']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reassign a given index\n",
    "name_list[2] = 'Reggie'\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True False\n"
     ]
    }
   ],
   "source": [
    "#Check if an item is in a list\n",
    "print('Henry' in name_list,'Susan' in name_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can count the occurences of a given item with count() method\n",
    "y = ['a','b','c','a','a']\n",
    "y.count('a')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A note of caution: be careful when making a copy of a list.  In python, if you try to make a copy by saying `name_list2 = name_list`, then we don't get a new variable.  Instead, there is still only one python object in memory, and that one object has two references (names) pointing to it.  Hence, changing one variable will change the other.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Xander', 'Reggie', 'Denise', 'Eddie', 'Grace', 'Henry', 'Ignacio']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_list2 = name_list\n",
    "name_list2[1] = 'Xander'\n",
    "\n",
    "#What does name_list now look like??\n",
    "name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alice', 'Xander', 'Reggie', 'Denise', 'Eddie', 'Grace', 'Henry', 'Ignacio']\n",
      "['Alice', 'Xander', 'Reggie', 'Denise', 'Eddie', 'Grace', 'Henry', 'Ignacio']\n"
     ]
    }
   ],
   "source": [
    "#To avoid this issue, you can make a copy of a list to get a truly new variable\n",
    "name_list2 = name_list.copy()\n",
    "#Now name_list2 can be manipulated without altering name_list\n",
    "name_list2[1] = 'Xander'\n",
    "print(name_list)\n",
    "print(name_list2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Be careful trying to perform arithmetic on each item in a list.  For multiplication, `n*list` or `list*n` will repeat the list n times.  To perform arithmatic to each element, we either need to use a list comprehension or utilize numpy, both of which are discussed later in this lesson. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_list = [1,2,3,4]\n",
    "3*num_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_list*3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python also has built-in functions to sort lists, either numerically or alphabetically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_list = [3, 2, 6, 7, 3, 1, 4]\n"
     ]
    }
   ],
   "source": [
    "num_list = [3,2,6,7,3,1,4]\n",
    "print('num_list = {}'.format(num_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 3, 4, 6, 7]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can act on the list with python's build in sorted()\n",
    "sorted(num_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 6, 4, 3, 3, 2, 1]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(num_list,reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 2, 6, 7, 3, 1, 4]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note that at this point, the num_list hasn't been altered\n",
    "num_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To sort the list itself, we can either assign the sorted output to the list with `num_list = sorted(num_list)`, or we can use the list methods sort() or reverse() to act on and alter the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 3, 4, 6, 7]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To alter the list itself, we can assign the \n",
    "num_list.sort()\n",
    "num_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 6, 4, 3, 3, 2, 1]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_list.reverse() #Same as num_list.sort(reverse=True)\n",
    "num_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Tuples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Tuples are similar to lists in that they are a sequence of objects, but they are different in that they are **immutable**, meaning they cannot be changed.  \n",
    "\n",
    "Tuples are defined between () as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 4, 3, 2, 1, 0.5, 'Blast Off!')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup = (5,4,3,2,1,0.5,'Blast Off!')\n",
    "my_tup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also define tuples with just commas and no parentheses\n",
    "my_tup2 = 1,2,3\n",
    "my_tup2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As mentioned, tuples are immutable, meaning we cannot alter them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#This will give an error if you try to run it\n",
    "#my_tup[0] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You may have two burning questions at this point:\n",
    "1. How the heck do you pronounce tuple?  Turns out there are 2 accepted answers in practice.  As Guido van Rossum, the creator of Python [tweeted](https://twitter.com/gvanrossum/status/86144775731941376), \"I pronounce tuple too-pull on Mon/Wed/Fri and tub-pull on Tue/Thu/Sat. On Sunday I don't talk about them. :)\"\n",
    "2. More importantly, why do we need lists and tuples?  Turns out there can be advantages to having an immutable data type that cannot be changed, i.e. so a human cannot mess it up.  Those from a Lean Six Sigma background may be familiar with concept of [Poka-Yoke](https://en.wikipedia.org/wiki/Poka-yoke), or mistake-proofing a process.  Additionally, if you don't need all the built in functionality of a list, then a tuple is a simplier way to accomplish your task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_tup = (5, 4, 3, 2, 1, 0.5, 'Blast Off!')\n",
      "my_tup[2] = 3\n",
      "my_tup[-3:] = (1, 0.5, 'Blast Off!')\n"
     ]
    }
   ],
   "source": [
    "#tuples can be indexed the same way as lists\n",
    "print('my_tup = {}'.format(my_tup))\n",
    "print('my_tup[2] = {}'.format(my_tup[2]))\n",
    "print('my_tup[-3:] = {}'.format(my_tup[-3:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since tuples are immutable, they only have 2 methods: index() and count()\n",
    "#You can confirm these are the only two by typing my_typ.[TAB] to see list of available methods\n",
    "my_tup.index('Blast Off!') #returns index of item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tup.count('Blast Off!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "A common usage is to **unpack** tuples into individual variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = 1, b = 2, c = 3\n"
     ]
    }
   ],
   "source": [
    "a, b, c = my_tup2\n",
    "print(\"a = {}, b = {}, c = {}\".format(a,b,c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Even though we can't change a tuple, we can use a trick to update it by converting it to a list, changing the list, then converting back to a tuple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_tup = (5, 4, 3, 2, 1, 0.5, 'Blast Off!')\n",
      "my_tup = (100, 4, 3, 2, 1, 0.5, 'Blast Off!')\n"
     ]
    }
   ],
   "source": [
    "print('my_tup = {}'.format(my_tup))\n",
    "temp_list = list(my_tup)\n",
    "temp_list[0] = 100\n",
    "my_tup = tuple(temp_list)\n",
    "print('my_tup = {}'.format(my_tup))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Sets "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Whereas a list is an ordered sequence of objects, with items possibly repeated, sets are a sequence of unique objects.  Sets are defined as comma-separated values between {}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 'five', 'four'}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_set = {1,2,3,'four','five'}\n",
    "my_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 'five', 'four', 'six'}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add a single item with add() method\n",
    "my_set.add('six')\n",
    "my_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 7, 8, 'five', 'four', 'nine', 'six'}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add collection of items with update()\n",
    "my_set.update([7,8,'nine'])\n",
    "my_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 7, 8, 'four', 'nine', 'six'}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove single item with remove()\n",
    "my_set.remove('five')\n",
    "my_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 7, 8, 'nine', 'six'}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Can also remove an item with discard()\n",
    "my_set.discard('four')\n",
    "my_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#Note: the discard() method will not raise an error if the item is not in the list, but remove() method will raise an error.\n",
    "my_set.discard('Hello') #Will run without raising an error that stops your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There also are useful methods for comparing and combining two sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "a = {1,2,3,4,5}\n",
    "b = {1,2,3,'four','five'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Intersection (elements in a AND b)\n",
    "#Any of these will work\n",
    "a.intersection(b)\n",
    "b.intersection(a)\n",
    "a & b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5, 'five', 'four'}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Union (elements in a OR b)\n",
    "#Any of these will work\n",
    "a.union(b)\n",
    "b.union(a)\n",
    "a | b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4, 5}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Difference (elements in one but not the other)\n",
    "a.difference(b)\n",
    "a - b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4, 5, 'five', 'four'}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Symmetric differnce (elements in one set or the other, but not in both)\n",
    "a.symmetric_difference(b)\n",
    "a ^ b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5, 'five', 'four'}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can use any of these set methods to update the value of at set\n",
    "#Set a equal to union of a and b:\n",
    "a |= b #same as a = a|b\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Can also check if one set is subset or superset of another\n",
    "b.issubset(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.issuperset(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Can also check if a value is in a set\n",
    "'one' in a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Dicts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Last but not least for the composite data structures is the dict, which contains a mapping of keys to values.  The dict data type is one of the most important data types in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice', 2: 'Bob', 'three': 20, 'four': [1, 2, 3, 4]}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {1:'Alice',\n",
    "     2:'Bob',\n",
    "     'three':20,\n",
    "     'four':[1,2,3,4]}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can now reference any value by referencing its key\n",
    "d['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#If we reference a key that isn't in the dict, we'll get a key error\n",
    "#d[10] #Will return a key error.  Uncomment and run to see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'default value since key is not in dict'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To avoid that issue, we can use the get() method to specify a default value to return if key isn't found\n",
    "d.get(10,'default value since key is not in dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice', 2: 'Bob', 'three': 20, 'four': [1, 2, 3, 4], 5: (3, 2, 1)}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can add new key-value pairs\n",
    "d[5] = (3,2,1)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice', 2: 'Bob', 'three': 20, 'four': [1, 2, 3, 4], 5: 'new value'}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Or reassign an existing key, since dict can only have one entry per key\n",
    "d[5] = 'new value'\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice Smith',\n",
       " 2: 'Bob',\n",
       " 'three': 20,\n",
       " 'four': [1, 2, 3, 4, 100],\n",
       " 5: 'new value'}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also manipulate an existing value in place.  E.g. we can append a value to the list at d['four']\n",
    "d['four'].append(100)\n",
    "d[1] += ' Smith'\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice Smith', 2: 'Bob', 'three': 20, 'four': [1, 2, 3, 4, 100]}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#delete a key\n",
    "del d[5]\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if item is one of the keys in the dict\n",
    "2 in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice Smith',\n",
       " 2: 'Bob',\n",
       " 'three': 20,\n",
       " 'four': [1, 2, 3, 4, 100],\n",
       " 7: 'Smith',\n",
       " 8: 'Jones'}"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Combine two dicts\n",
    "d2 = {7:'Smith',8:'Jones'}\n",
    "d.update(d2)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 'three', 'four', 7, 8]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can access sequence of keys or values from a dict\n",
    "list(d.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice Smith', 'Bob', 20, [1, 2, 3, 4, 100], 'Smith', 'Jones']"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(d.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([(1, 'Alice Smith'), (2, 'Bob'), ('three', 20), ('four', [1, 2, 3, 4, 100]), (7, 'Smith'), (8, 'Jones')])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Or we can access sequence of (key,value) tuples with the items() method\n",
    "#This can be useful for looping through the items, as we'll see in the section on for loops\n",
    "d.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice Smith',\n",
       " 2: 'Bob',\n",
       " 'three': 20,\n",
       " 'four': [1, 2, 3, 4, 100],\n",
       " 7: 'Smith',\n",
       " 8: 'Jones',\n",
       " 10: {7: 'Smith', 8: 'Jones'}}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also have nested dicts, where the value is itself a dict\n",
    "#This is essentially the format of the popular JSON data format\n",
    "d[10] = d2\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This nested dict structure if very common in many data analysis applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comprehensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprehensions are an extremely useful technique for quickly creating composite data structures (such as lists, tuples, sets, dicts) by automatically grabbing and altering each element in a sequence.  That probably made no sense, so lets look at some examples.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_list = [1,2,3,4,5,6,7,8,9,10]\n",
    "\n",
    "#What if want a list that replaces each element in num_list with a str version of that element?\n",
    "#We could start with an empty list, then append one item at a time as we loop through the items in num_list\n",
    "#OR we can concisely build it using a comprehension as follows:\n",
    "str_list = [str(i) for i in num_list]\n",
    "str_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2', '4', '6', '8', '10']"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can add conditions, e.g. only capture even numbers\n",
    "[str(i) for i in num_list if i%2==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is common to use comprehensions to create dict objects.  In these cases, we can take advantage of the `zip` function which combines multiple sequences along side each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Alice', 2: 'Brianna', 3: 'Charlie', 4: 'Doug'}"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys = [1,2,3,4]\n",
    "values = ['Alice','Brianna','Charlie','Doug']\n",
    "d = {k:v for k,v in zip(keys,values)}\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another handy python function is enumerate, which zips together any sequence with a matching sequences of indexes.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Alice\n",
      "1 Brianna\n",
      "2 Charlie\n",
      "3 Doug\n"
     ]
    }
   ],
   "source": [
    "for i, n in enumerate(values):\n",
    "    print(i,n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Alice', 1: 'Brianna', 2: 'Charlie', 3: 'Doug'}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Which again we can use to create a dict comprehension\n",
    "d = {i:n for i, n in enumerate(values)}\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# String Matching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned, python has a reputation for providing an extensive set of tools for working with string data.  Now that we've covered strings and lists, it's worth taking a quick look at Regex (regular expression) and Fuzzy Matching, two popular technqiues for searching and cleaning string data, which is often a crucial step in data analysis workflow.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regular Expression, aka regex or regexp, is a popular technique used in many programming languages to search for specific patterns within a string.  Python has a built-in package `re` to handle regex techniques.  A couple quick examples are given below.  For a good quick orientation see the [W3 Python regex tutorial](https://www.w3schools.com/python/python_regex.asp), or for more in depth overview see the [python regex documentation](https://docs.python.org/3/howto/regex.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Say we want to extract the phone numbers from this string\n",
    "s1 = \"\"\"Alice's number is 111-222-3333, and Bob's is 444-555-6666\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract the phone numbers, we first have to identify the regex **pattern** we are looking for.  Here, the pattern would be 3 digits (regex character '\\d'), followed by a '-', followed by 3 more digits, etc.  Next, we need to find that pattern in the string using one of the re searching functions.  Here's how it looks using `re.findall()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['111-222-3333', '444-555-6666']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here's how we can find all matches of a phone number pattern\n",
    "pat = '\\d\\d\\d-\\d\\d\\d-\\d\\d\\d\\d'\n",
    "re.findall(pat,s1) #Return a list of all matches of the pattern in the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['111-222-3333', '444-555-6666']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can make the pattern more concise by specifying how many of a given item we want\n",
    "#Here, \\d{n} means we want exactly n occurences of a digit\n",
    "pat = '\\d{3}-\\d{3}-\\d{4}'\n",
    "re.findall(pat,s1) #Return a list of all matches of the pattern in the string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['111-222-3333', '444.555.6666']"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What if the numbers can use a - or . to separate numbers?\n",
    "s2 = \"\"\"Alice's number is 111-222-3333, and Bob's is 444.555.6666\"\"\"\n",
    "\n",
    "#We can specify a set of characters to match by putting them in square brackets\n",
    "pat = '\\d{3}[-.]\\d{3}[-.]\\d{4}'\n",
    "#[-.] means match a SINGLE character that is either a \"-\" or a \".\"\n",
    "re.findall(pat,s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['111-222-333', '444.555.666']"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also use number ranges\n",
    "pat = '[0-9]{3}[-.][0-9]{3}[-.][0-9]{3}'\n",
    "re.findall(pat,s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What happens if the pattern doesn't exist?\n",
    "pat = '[0-9]{9}' #Look for 9 straight digits, which doesn't exist in our string\n",
    "#In this case, we'll just get an empty string back\n",
    "re.findall(pat,s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Fuzzy Matching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Fuzzy matching provides a way to evaluate how similar two strings are.  It was originally developed by SeatGeek to identify when the same events are given multiple similar labels.  See https://www.geeksforgeeks.org/fuzzywuzzy-python-library/ for a good intro and tutorial of its use.  We give a few quick examples below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#May have to install fuzzywuzzy if not available in local environment\n",
    "#!pip install fuzzywuzzy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'fuzzywuzzy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-133-f4bc68d49cd0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mfuzzywuzzy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfuzz\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mfuzzywuzzy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mprocess\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'fuzzywuzzy'"
     ]
    }
   ],
   "source": [
    "from fuzzywuzzy import fuzz \n",
    "from fuzzywuzzy import process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The fuzz modules gives a 0-100 score of how similar two strings are.  It is primarily based on the Levenshtein distance, which is how many characters need to be changed to cause one string to match the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "s1 = 'John Public'\n",
    "s2 = 'John Q. Public'\n",
    "s3 = 'John Q. Public!'\n",
    "s4 = 'Public, John Q.'\n",
    "fuzz.ratio(s1,s1), fuzz.ratio(s1,s2), fuzz.ratio(s2,s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "fuzz.partial_ratio will ignore certain punctuation and just focus on the tokens (i.e. meaningful words), which can be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fuzz.ratio(s2,s3), fuzz.partial_ratio(s2,s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The token_sort_ratio method ignores the *order* of the words (tokens), which can be useful if the order of words is sometimes reversed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fuzz.token_sort_ratio(s2,s4)\n",
    "#Yields perfect match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The process module is used to find the closest match(es) of a string in another sequence of strings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "query = 'John Publik'\n",
    "choices = ['John Q. Public','Joe B. Bloggs','John Smith']\n",
    "process.extractOne(query, choices) #returns tuple with closest match, and the matching score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### If-else-elif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That is True!\n"
     ]
    }
   ],
   "source": [
    "#Syntax for if-else control structures\n",
    "if 5>3:\n",
    "    print(\"That is True!\")\n",
    "else:\n",
    "    print(\"That is False!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c\n"
     ]
    }
   ],
   "source": [
    "#We can also use the elif keyword if we have subsequent conditions\n",
    "if 1>10:\n",
    "    print('a')\n",
    "elif 5>10:\n",
    "    print('b')\n",
    "elif 15>10:\n",
    "    print('c')\n",
    "else:\n",
    "    print('d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That is False!\n"
     ]
    }
   ],
   "source": [
    "#We can create composite booelan statements\n",
    "if (5>3 & 7=='7'):\n",
    "    print(\"That is True!\")\n",
    "else:\n",
    "    print(\"That is False!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That is False!\n"
     ]
    }
   ],
   "source": [
    "#if statements are also a useful way to check if a variable exists and has a value (i.e. is non-empty)\n",
    "#if we feed in a None type or empty variable (empty string, list, etc), if will return False\n",
    "a = None\n",
    "b = [] #empty list\n",
    "c = [1] #non empty list\n",
    "d = \"\" #empty string\n",
    "e = \"1\" #non empty string\n",
    "#Try feeding in these different variables to the \"if\" condition\n",
    "if (b):\n",
    "    print(\"That is True!\")\n",
    "else:\n",
    "    print(\"That is False!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### For Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "for loops in python generally take advantage of the ability to loop through the elements of a sequence, such as a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "num_list =[1,2,3,4,5,6,7,8,9,10]\n",
    "for n in num_list:\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "#python has a built in function range() which gives a sequence of numbers \n",
    "#range(n) gives sequence of integers from 0 to n-1 (that is, n total numbers)\n",
    "for n in range(5):\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "12\n",
      "14\n",
      "16\n",
      "18\n"
     ]
    }
   ],
   "source": [
    "#We can also optionally specify start or step of the range, as long as all parameters are integers\n",
    "for n in range(10,20,2):\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "for loops also have the standard continue and break keywords, which can be very useful for flow control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "#Only print even numbers, and break out of the loop when we get past 10\n",
    "for n in range(20):\n",
    "    if n%2==1:\n",
    "        #number is odd, so skip ahead to next value in the for loop with continue\n",
    "        continue\n",
    "    if n>10:\n",
    "        break\n",
    "    print(n) #Should only print even numbers <= 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### While Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#syntax for while loops:\n",
    "i = 0\n",
    "my_list = []\n",
    "while(i<10):\n",
    "    my_list.append(i)\n",
    "    i += 1\n",
    "my_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Try-Except"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "If you've coded before in any language, you know that an error in the code can bring the whole workflow to a crashing halt.  The python try-except control structure is a very useful way to try and execute some code that may or may not return an error without having to worry about crashing the notebook and thus preventing any subsequent code from running.\n",
    "\n",
    "Consider this code where we want to try and type-cast a string as a float, which will raise an exception (python error) if the string does not represent a viable number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "2.2\n",
      "Could not convert \"three\" to a float\n",
      "Could not convert \"Dog\" to a float\n",
      "3.14\n"
     ]
    }
   ],
   "source": [
    "str_list = ['1','2.2','three','Dog','3.14']\n",
    "for s in str_list:\n",
    "    try:\n",
    "        print(float(s))\n",
    "    except:\n",
    "        print('Could not convert \"{}\" to a float'.format(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There are also different types of exceptions, and sometimes it makes sense to handle them differently.  For example, `float(s)` can return a ValueError if s cannot be translated to a float (e.g. for s = 'Dog'), or it can return a TypeError if s is not a valid argument (e.g. if s is a list).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "2.2\n",
      "Got a ValueError for float(three)\n",
      "Got a ValueError for float(Dog)\n",
      "Got a TypeError for float([1, 2, 3])\n",
      "3.0\n"
     ]
    }
   ],
   "source": [
    "x_list = ['1','2.2','three','Dog',[1,2,3],3]\n",
    "for x in x_list:\n",
    "    try:\n",
    "        print(float(x))\n",
    "    except ValueError:\n",
    "        print(\"Got a ValueError for float({})\".format(x))\n",
    "    except TypeError:\n",
    "        print(\"Got a TypeError for float({})\".format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success for converting \"1\" to float\n",
      "Success for converting \"2.2\" to float\n",
      "Could not convert \"three\" to a float\n",
      "Could not convert \"Dog\" to a float\n",
      "Success for converting \"3.14\" to float\n"
     ]
    }
   ],
   "source": [
    "#We can use the else control to execute code only if the try command SUCCEEDS\n",
    "\n",
    "str_list = ['1','2.2','three','Dog','3.14']\n",
    "for s in str_list:\n",
    "    try:\n",
    "        f = float(s)\n",
    "    except:\n",
    "        #If try command raised an error, we get here\n",
    "        print('Could not convert \"{}\" to a float'.format(s))\n",
    "    else:\n",
    "        #If try command succeeded (did not raise an error) we get here\n",
    "        print('Success for converting \"{}\" to float'.format(s))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success for converting \"1\" to float\n",
      "Done dealing with 1\n",
      "Success for converting \"2.2\" to float\n",
      "Done dealing with 2.2\n",
      "Could not convert \"three\" to a float\n",
      "Done dealing with three\n",
      "Could not convert \"Dog\" to a float\n",
      "Done dealing with Dog\n",
      "Success for converting \"3.14\" to float\n",
      "Done dealing with 3.14\n"
     ]
    }
   ],
   "source": [
    "#And \"finally\", we can cover the finally control which will execute code regardless of whether the try command raised an error\n",
    "str_list = ['1','2.2','three','Dog','3.14']\n",
    "for s in str_list:\n",
    "    try:\n",
    "        f = float(s)\n",
    "    except:\n",
    "        #If try command raised an error, we get here\n",
    "        print('Could not convert \"{}\" to a float'.format(s))\n",
    "    else:\n",
    "        #If try command succeeded (did not raise an error) we get here\n",
    "        print('Success for converting \"{}\" to float'.format(s))\n",
    "    finally:\n",
    "        #Always runs this\n",
    "        print('Done dealing with {}'.format(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Basic function syntax\n",
    "def cube(x):\n",
    "    return x**3\n",
    "cube(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can add as many arguments as we want\n",
    "def make_list(x,y,z):\n",
    "    return [x,y,z]\n",
    "make_list(1,2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1000)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#And we can assign default values\n",
    "def power(base,power=2):\n",
    "    return base**power\n",
    "#If we only specify base, then power gets default value of 2\n",
    "power(10), power(10,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In python functions, arguments without defaults are called positional arguments, and those with default values are called keyword arguments.  Any keyword arguments must come after the positional ones.  If you use the name to reference any argument (positional or keyword), you can specify them in any order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This will work correctly, even though arguments are out of order\n",
    "power(power=3,base=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also common to set default values to None, allowing the function to evaluate whether or not that argument exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def power(base,power=None):\n",
    "    if power:\n",
    "        return base**power\n",
    "    else:\n",
    "        return base\n",
    "power(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also accept an unknown number of positional arguments using \"*args\" argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n",
      "['a', 'b', 'c', 'd', 'e']\n"
     ]
    }
   ],
   "source": [
    "def make_list(*args):\n",
    "    #All positional arguments are passed to a tuple called args\n",
    "    return list(args)\n",
    "print(make_list(1,2,3,4))\n",
    "print(make_list('a','b','c','d','e'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Matt', 1, 2, 3, 4]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#*args can come after any number of other positional arguments\n",
    "def make_list_with_name(name,*args):\n",
    "    return [name] + list(args)\n",
    "make_list_with_name('Matt',1,2,3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also collect an unknown number of keyword arguments as \"**kwargs\".  Inside the function, these are available as a dict matching keyword names to values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyword arguments: {'a': 1, 'b': 2, 'c': 10}\n"
     ]
    }
   ],
   "source": [
    "def print_kwargs(**kwargs):\n",
    "    #within the funciton, kwargs is a dict matching any keyword names to values\n",
    "    print('Keyword arguments:', kwargs)\n",
    "print_kwargs(a=1,b=2,c=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Matt Smith\n",
      "Additional positional args:  [1, 2, 3, 4]\n",
      "Additional keyword args:  {'a': 1, 'b': 2}\n"
     ]
    }
   ],
   "source": [
    "#We can create a function that pulls fixed arguments with additional positional and keyword arguments\n",
    "def lots_of_args(first_name,last_name,*args,**kwargs):\n",
    "    print('Name: {} {}'.format(first_name,last_name))\n",
    "    print('Additional positional args: ',list(args))\n",
    "    print('Additional keyword args: ',kwargs)\n",
    "lots_of_args('Matt','Smith',1,2,3,4,a=1,b=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lambda Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes we need to utilize some function in one specific instance, but don't need to use it again so there's no need to assign a name to the function.  We can handle these situations with a **lambda function**, similar to what R calls anonymous functions.  Though this may seem strange if you haven't seen them before, they are actually quite useful in many data analysis applications.  For example, it's common to use lambda functions to apply transormations to data structures, or to use them as inputs to functions that take functions as inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define a function that takes a function as an input\n",
    "def apply_func(func,num):\n",
    "    return func(num)\n",
    "\n",
    "#We could define and feed in a new function, such as the cubed function from before\n",
    "def cube(x):\n",
    "    return x**3\n",
    "apply_func(cube,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Or we could pass in a lambda function, a more concise approach\n",
    "apply_func(lambda x: x**3,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an object-oriented program, everything in python (numbers, strings, lists, functions, anything else you can think of) is an object.  Objects are any custom data structure that has properties (called attributes) and functions (called methods).  \n",
    "\n",
    "Classes are the tool used to build new objects.  If you plan to do any development in python, classes are how you will build new tools and packages.  Even if you don't do development yourself, it's still helpful to understand the basics of how classes work so you can understand the code and packages you are dealing with.  \n",
    "\n",
    "A few basics are given below.  For a more thorough treatment, I'd recommend the Classes chapter in the [Introducing Python](https://learning.oreilly.com/library/view/introducing-python-2nd/9781492051374/ch10.html#ch_objects) book on O'Reily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Classes are defined with the class keyword\n",
    "class Cat:\n",
    "    color='black'\n",
    "\n",
    "a_cat = Cat()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, `a_cat` is an object, and is an instance of the `Cat` class.  a_cat has a single property at this point, but doesn't haven't any methods yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'black'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See color attribute\n",
    "a_cat.color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'brown'"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Change the color attribute\n",
    "a_cat.color='brown'\n",
    "a_cat.color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also assign methods to objects by defining the functions within the class.  One quirk of this syntax that takes a bit to get used to is that the first argument of the function definition within a class always refers to the object itself (and it's customary to refer to this argument as \"self\").  Any subsequent arguments accept any arguments passed in when that object method is called.\n",
    "\n",
    "That probably made no sense, so lets look at an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meow\n",
      "Woof\n"
     ]
    }
   ],
   "source": [
    "#Define a speak() method that takes an optional argument of \"phrase\" with a default value of \"Meow\"\n",
    "class Cat:\n",
    "    color='black'\n",
    "    \n",
    "    #First argument to any class function refers to the object itself\n",
    "    #It is customary to call this first argument 'self'\n",
    "    def speak(self,phrase='Meow'):\n",
    "        return phrase\n",
    "\n",
    "#Now if we create a Cat object, we can envoke its speak() method\n",
    "cat2 = Cat()\n",
    "print(cat2.speak())\n",
    "print(cat2.speak('Woof'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One special function name for all classes is `__init__()` (that's two underscores on each side, sometimes refered to as \"dunder\" for double-under).  Whenever a new object is created, python automagically will look for that class's `__init__` function, and if it exists it will execute that function.  This is typically used to initialize the object with intial properties provided by the user, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brown\n",
      "blue Mr. Bigglesworth 10\n"
     ]
    }
   ],
   "source": [
    "class Cat:\n",
    "    def __init__(self,color='black',name=None,age=None):\n",
    "        self.color=color\n",
    "        if name:\n",
    "            self.name = name\n",
    "        if age:\n",
    "            self.age = age\n",
    "            \n",
    "        def speak(self,phrase='Meow'):\n",
    "            return phrase\n",
    "\n",
    "#Now we can create Cat objects and specify their color or name\n",
    "cat1 = Cat(color='brown')\n",
    "print(cat1.color)\n",
    "cat2 = Cat(color='blue',name='Mr. Bigglesworth',age=10)\n",
    "print(cat2.color,cat2.name,cat2.age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another key concept is *inheritance*, where a subclass inherits all the properties and methods of its parent class (aka superclass), but with some additional features.  Lets create a subclass of Cat and give it a new method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To define a subclass, use the parent class as an argument to the class definition\n",
    "class aging_Cat(Cat):\n",
    "    def make_older(self,yrs = 1):\n",
    "        self.age += yrs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This subclass aging_Cat will inherit all the features of the parent class Cat, unless we override them in the definition of aging_Cat (which is perfectly acceptable).  For example, aging_Cat has the same `__init__` function as the Cat class, and we've given it an *additional* method, `make_older()`, to make the cat older."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat3 = aging_Cat(age=10)\n",
    "cat3.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat3.make_older(5)\n",
    "cat3.age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy, short for numerical python, is a popular package for numerical computing in python.  It provides data structures, algorithms, and computation for working with arrays and other scientific applications.  It is also the foundation on which pandas, scikit-learn, and many other python packages are built and in many ways helped usher in the growth of python as a tool for data analysis.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a =  [1 2 3 4]\n",
      "b =  [[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "#main data type is ndarray\n",
    "import numpy as np\n",
    "\n",
    "a = np.array([1,2,3,4]) #1d array\n",
    "b = np.array([[1,2],[3,4]]) #2d array\n",
    "print(\"a = \",a)\n",
    "print(\"b = \",b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you recall from earlier, python's built-in range method only takes interger inputs.  Numpy has a method np.arange() that improves that functionality, allowing any numerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "np.arange(5) =  [0 1 2 3 4]\n",
      "np.arange(2,5) =  [2 3 4]\n",
      "np.arange(2.5,5,.5) =  [2.  2.5 3.  3.5 4.  4.5]\n"
     ]
    }
   ],
   "source": [
    "#np.arange useful way to get a sequence (range) of values\n",
    "#Specify np.arange(start,end,step), where sequence stops BEFORE reaching end value\n",
    "print('np.arange(5) = ',np.arange(5))\n",
    "print('np.arange(2,5) = ',np.arange(2,5))\n",
    "print('np.arange(2.5,5,.5) = ',np.arange(2,5,.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "np.linspace(1,5,15) =  [1.         1.28571429 1.57142857 1.85714286 2.14285714 2.42857143\n",
      " 2.71428571 3.         3.28571429 3.57142857 3.85714286 4.14285714\n",
      " 4.42857143 4.71428571 5.        ]\n"
     ]
    }
   ],
   "source": [
    "#np.linspace gives us sequence with desired number of equally spaced values\n",
    "print('np.linspace(1,5,15) = ',np.linspace(1,5,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "[[1. 1.]\n",
      " [1. 1.]\n",
      " [1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "#Also useful is zeros or ones\n",
    "a = np.zeros((2,3))\n",
    "print(a)\n",
    "b = np.ones((3,2))\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np can do matrix multiplication with the @ symbol\n",
    "b@a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.36458247, 0.66061714, 0.7361513 , 0.02552003, 0.1112763 ,\n",
       "       0.81461769, 0.8266278 , 0.92851561, 0.17959056, 0.21242157])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.random has a lot of useful tools to generate random numbers\n",
    "\n",
    "#Create 10 random uniform samples from 0 to 1\n",
    "np.random.random(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1, 10,  7, 10,  1, 10,  7,  4,  9,  4,  7,  2,  9,  3,  4,  7,\n",
       "        9, 10,  7])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create 20 random int's from 1 to 10 \n",
    "a = np.random.randint(low=1,high=11,size=20)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.1, 3.2233522922572395, 7.0)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Also many built in math operators\n",
    "np.mean(a), np.std(a), np.median(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   1, 100,  49, 100,   1, 100,  49,  16,  81,  16,  49,   4,\n",
       "        81,   9,  16,  49,  81, 100,  49], dtype=int32)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Numpy will also broadcast a function to each element of the array if it makes sense to do so\n",
    "np.power(a,2) #Square each element of the array a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "np.hstack and np.vstack are useful ways to flatten out a list of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], 4, (5, 6)]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_list = [[1,2,3],4,(5,6)]\n",
    "num_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get this as one flat list:\n",
    "np.hstack(num_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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