Python_tutorial.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial Outline for Python:\n",
    "\n",
    "1. Introduction to Python\n",
    "2. Python Data Structures and related operations.\n",
    "    1. String\n",
    "    2. List\n",
    "    3. Tuple\n",
    "    4. Dictonaries\n",
    "    5. Set\n",
    "3. Conditional Statemets and Loops\n",
    "4. Python Functions, range() etc.\n",
    "5. Pyhton Libraries:\n",
    "    1. Numpy\n",
    "    2. Pandas\n",
    "    3. Matplotlib\n",
    "    4. Seaborn\n",
    "6. Pyhton Machine Learning Libraries and frameworks\n",
    "    1. Scikit Learn\n",
    "    2. Tensorflow\n",
    "    3. Keras\n",
    "    4. Pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction to Python:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. \n",
    "\n",
    "* Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting. \n",
    "\n",
    "* Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. \n",
    "\n",
    "* Python supports modules and packages, which encourages program modularity and code reuse. \n",
    "\n",
    "* The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.\n",
    "\n",
    "Source: https://docs.python.org/3/tutorial/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Python data structure and related operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data-types"
   ]
  },
  {
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i6NXusMKW8SRRIscSKFVEGAoHAAHYU+igAooooAKKKKACiiigAooooAKKKyPF+sN4f8K6xqcePNtLSWZM9CyoSo/PFZVakaNOVWe0U2/kXGLnJRW7Of17xtf3Gqy6X4dit5Ht32Xmo3QLwQN1MaqpBdwMZGQFyM88VmNp2sXDF5/Feqlz2gWCJB7ACP8AmTT/AA9pS6LotpaD5nRAZJD1kkPLufUliSfrWjXx1quJXtMRJ3fRSaS8tGr27u7b7LRfSRp06XuwivVq7f3mT/Y+pf8AQ063/wB/If8A43R/Y+pf9DTrf/fyH/43WtRS+rQ7y/8AApf5l3XZfcv8jJ/sfUv+hp1v/v5D/wDG6P7H1L/oadb/AO/kP/xutaij6tDvL/wKX+YXXZfcv8jJ/sfUv+hp1v8A7+Q//G6P7H1L/oadb/7+Q/8Axutaij6tDvL/AMCl/mF12X3L/Iyf7H1L/oadb/7+Q/8Axuj+x9S/6GnW/wDv5D/8brWoo+rQ7y/8Cl/mF12X3L/Iyf7H1L/oadb/AO/kP/xuj+x9S/6GnW/+/kP/AMbrWoo+rQ7y/wDApf5hddl9y/yMn+x9S/6GnW/+/kP/AMbo/sfUv+hp1v8A7+Q//G61qKPq0O8v/Apf5hddl9y/yMn+x9S/6GnW/wDv5D/8bo/sfUv+hp1v/v5D/wDG61qKPq0O8v8AwKX+YXXZfcv8jJ/sfUv+hp1v/v5D/wDG6P7H1L/oadb/AO/kP/xutaij6tDvL/wKX+YXXZfcv8jJ/sfUv+hp1v8A7+Q//G6F0vV4WDw+LNYVx/z08iRT9QYq1qKPq0Ojl/4FL/MLrsvuX+RBpfjTUdFvLey8RCK4tp3EcWr26eWiuThUlTJ2kngMPlyQMCu9rgdQsYtUsbi0nXfDMhjYH0IxWz8N9SuNV8E6XNdENcxo1vKwJ+ZonaMt9SUz+Nerl+IqRrfVaknJNNxb3Vmk0313Vm9d7s8vGUYqKqwVtbP+vkdLRRRX0R5IUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHl/wAYfhPe+KprTxV4PvYdB+I2jo39m6lKpMNyhwXtLpRy8EmACOqkK64ZVIs/Bn4zWnxW0+/tLuwl8OeMtFkW217w1eODPYTEZBB/5aQuAWjlX5XX0IZR6PXk3xm+DN34t1Cw8aeC7+Lw58TdFjZNP1R0JgvYScvZXijmS3cj/eRsOhBHIB6zRXnHwZ+M1p8VtPv7S7sJfDnjLRZFtte8NXjgz2ExGQQf+WkLgFo5V+V19CGUej0AebRf8jx4t/6+Lf8A9Jo606zIv+R48W/9fFv/AOk0dadfE0P+Xn+Op/6XI+p+zH/DH8kFFFFdIgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqFt/wAlE8P/APXref8AtKr9ULb/AJKJ4f8A+vW8/wDaVc9b7H+OH/pcRS+CXo/yZ6LRRRX2p8wFFcd8Tfi94T+D+k29/wCKdWSw+2TC2sbOKNp7y/nJAWG2t4w0k0hJHyopPOTgc15jN4W+In7RdrG3iqS/+FXw/mH7zwtYzr/bmqREHK3l3ExFoh4BhgJkI3BpVyUAB0HjD49tceILvwh8NdHPjzxlAyw3bRSeXpOjMwGGvrvBVSAd3kR75mA4QA7qh8C/s+u3iCy8Z/E7Wv8AhYPju3YS2ryQ+TpWjtkkCws8lUZd2PPkLzNj74B2j0nwX4H8P/Djw3ZeH/C+jWWgaJZrsgsbCFYo09TgdSTyWPJJJJJrcoAKKKKACiiigAooooAKKKKACiiigAooooAK5X4qf8k38S/9eE3/AKCa6quV+Kn/ACTfxL/14Tf+gmvNzP8A3Gv/AIJfkzpw38en6r8ynRRRXiHvhRRRQAUUUUAFFFFAHL6Z8TfDWsfEDWfBFnqXneKNHtYby+sfIlXyYZc+W3mFQjZx0ViR3ArqCdoJPSvmD4af8pAPjJ/2LGj/APs1fTznCMevFEvdpxn3Tf4tfoRGTc5R7Nfkn+pzXw3+JXhv4ueE7bxN4T1H+1dEuZJYorryJYdzRu0bjbIqsMMrDkc444rpq+Nfh3+1Q/g39jnw142tvBvh3S9U1nXpNC0/R9Gh/szSYbiS8ljWWXl/KTCF3bnLE9N3HpHh3x/8d/Cfjfw3YePvCnhfxJ4b164NsdX8BC8LaS2wsr3KTBt0RIA8wFQM5IHAPTOi1OUV0dte9k7fihTmqbafn9ybX6P7j337ZB9rFr58f2op5oh3jfsBwW29cZIGfepa+KfBKfFj/h4J8QfJuPBv2QaXppvxJBd7zpXnSeSsRD4FztzuLZQnoBX2tWHL7kZ33v8Ag2v687gpXnKFtv8AJP8Ar5BRRRUmgUUUUAFFFFABU/wp/wCRKh/6/L3/ANK5qgqf4U/8iVD/ANfl7/6VzVWE/wCRhD/BP/0qmc2M/wB3f+JflI6+iiivqzwAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDyb4zfBm78W6hYeNPBd/F4c+Juixsmn6o6EwXsJOWsrxRzJbuR/vI2HUgjnzbxx+3h4a+G/wjuvE3ibTJdD8V6TrNjomt+E7uT/SLKaaQb3VgP3kXkLPNHKo2yCPHB3BfprUI7maxnSynjtbtkIimliMqI2OGKBl3AemR9a/Kz9tT/gnv+0l8XvE7eKD4x0f4pfZkaK0to7WDR7uKLcXWMR4EbBckAtKW/M0AfofaTJceNPFUsTrJE81syuhyGBtoyCD3Fa1eD/sa3Xi6b4Q6fb+O9HvtD8V6fBb6Ze2morif/R4VgSRvXfHGj577s5PWveK+Jof8vP8AHU/9LkfU/Zj/AIY/kgqO4uYrO3luLiVIIIlLySyMFVFAySSeAAO9SV5H+1p8O/EfxX/Z28beFfCc4h17UbMJboZBGJ9rqzQ7iQF8xVZMk4+bnjNdD2HFJtJnNXH7f37Ptr4i/sR/idpZvd2zzI4bh7XOcf8AHwsZhx778V7tpOr2Ov6Xa6lpl7b6jp11Gs1vd2sqyxSoRkMrqSGBHcV8heFf21vhX4L8J6f4F+JvgTXPhGsdsthJpWveH3bTZMLtZYmiVhJGefmKgEc9Oa9N8B3nw0/Zb/Zp1PxH4b1+4134dWAudYtZkuoroBZZNwt7dkVV2+Y21VPILYJzmuqrTjTi3Z2Wz3T+7/NnJSqSqSS0u+mzX379tke90V8uTfFj9o/T/CL/ABDu/Angk+Fo7X+0pPCcN/df25Ha7d5/flfIaUJk7Ng6Yzniug8fftVGXQfhrB8MtJh8U+KviNEZ9CttRla3tre3WMSS3FyygsFjBAKryTkCo9jO/KtXe1uz13+567aPszRVY2v0te/kv+HXnqu59B0V8o+If2gPjR8LfiJ8MfBvjnw34Snfxdr0didf8Pm5ezNvsbzYvLlYPFcBvLKsTIjKX4BGK9S8G/F3WPEX7SXxE+H1zbWKaL4d0vTb20nijcXDvcCQuJGLlSBsGMKD1yTUum9Gtb3/AAV2L2sbuL3Vn97svxPXKK8X0L416xqH7VXjD4a3Nvp8Xh3RvDdrrMV0sbi5Mskm1w7F9uwDoAoPua4bwv8AG/41fHizu/FXwo8P+C7DwFHdTW+n3Hi+e7N3rKxOUaaNYBtgRmVgpfceM4pRg5LmW3/Ba/NO3Vj9pG7XVO3zsn+TPqGivlrxf+2JqVv+yf43+JOj6FbaZ408JXv9k6roGqlriG0vkuYopoy0bIZF2yblYEZyPcVU8efHz48fD/wMfinqPgrwevw+t4ory88PLe3La7BaOV/eGXHkbwGDGMA45G44q/ZS1baSvbfTVJr5NPfYrnVk11vp103+659V3F5BZiM3E8cAkdYk8xwu5ycBRnqT2Fc7J8TPDcPxIh8AvqWPFs2mNrCaf5EvNoJPLMnmbdn3+Nu7d3xjmvlT9sDUPH+ufF79nrUPBk/hlNPvNW8/R/7ahuWcXjW0jFp/LYZh8orgLht2cnFen2PjrUof2vvDvg/WdA8LyavJ8PzqV5r9nYt9tWYXYR4IZ2bcLYtlhGQTnBJp06XN8W95K3X3Y83/AA/l6k+0WvLtaLv096SX6/f6a/QdY3jPxjpHw98Kar4l1+7+waJpdu93eXXlPJ5USjLNtQFjgdgCa8Nk8e/tDfEG+1i78E+EPCPg/QbG+ls7RfH3277dqKxnHniKEKIY3/hLFjjnFcR40+N978cv2HfjZeazo0egeJNBt9V0DV7G3mM0K3UCAM0bEAlGDKRnpnGT1OUoS9m5ror/AC7/AIr7y1Jc6g+rt8/6T8j620XWLTxDo9hqunzfaLC+gjubebaV3xuoZWwwBGQQcEA1crjvgz/yR/wL/wBgGx/9J0rsa0rQVOrKC2TaMcPUdajCpLdpP70FULb/AJKJ4f8A+vW8/wDaVX68r+OPx28Nfs6yaJ4t8UfansttxZwW9nGrSz3Enl+XGCzKi5IPzOyqO5FcNb7H+OH/AKXE3l8EvR/kz6HmmS3ieWV1jiRSzO5wFA5JJ7CvCdW+OniX4qXr6P8AA/TLLWrZJDDe+PtZDjQrMgkEWwUhtQkG1hiEiJTjdKPu14hD8aPh98brpNS+MfxT0MeHwwktvh34Zup5dOAK9NRuVQNeuM4MXywcfckzmveLT9sT4Hafaw2tr450u2toUWOKGG3mVI0AwFUCPAAAAAFfanzB0nw3+A2jeBdWfxJql9e+NfHlxGEufFWvFJLrGDmO3RVEdrF8zfuoFRTkbtx5r0yvFv8Ahsv4NlQyeNoJ1JI3QWV1IAR2O2I460n/AA2Z8Hf+hw/8pl5/8ZoA9qorxT/hsr4RN9zxNdTD+9DoeoSD6ZWAjPtR/wANlfCX/oYdQ/8ACe1L/wCR6APa6K8U/wCGyPhS3+r1nWJ/XyPC+qy4+u22OPxo/wCGxvhb/wBBHxB/4SGsf/IlAHtdFeKf8NifDFv9Xc+KJ/XyPBOtyY+u2zOPxpf+Gwvhr6+MP/CC17/5CoA9qorxT/hr34fyf8e1n44vMfe8jwDrp2+mc2Y6/wBKX/hrjwT/ANAX4gf+G/1v/wCRKAPaqK8W/wCGsvCUik23hn4j3hUgMsHw91skZzyc2o9Kb/w1f4d/6Er4of8AhvNZ/wDkagD2uivFP+GrNBbiLwL8UJW/u/8ACAasn6vAB+tH/DVOkf8ARPvih/4Q2o//ABugD2uivFP+GptMbiL4dfFCVv7v/CFXyfq6AfrR/wANRWn/AETL4of+EjcUAe11yvxU/wCSb+Jf+vCb/wBBNfGX7dH7RniG1+EUfjXwJp3xH+HvinwpeRXMWpaloEtrYTwTSJDLb3Pm5iZWLRsA6tlo1AHzVxP7NP8AwUq8S/tFeF9b8F+K/h5qF5rI0+QSeJPDNuz2UY248y7jY/uB1JcMQSQAgrzcz/3Gv/gl+TOnDfx6fqvzPveiiivEPfCiiigAooooAKKKKAPhrXP2hvh9+z3+3h8Ur7x/r/8AYFrqXh3SYbWT7FcXPmOoLMMQxuRgEdcV758J/wBsf4P/AB28TyeGvA3i/wDtzWltZLs2v9mXlv8AukKhm3SwovBZeM55r2miq910lTktUmvvbfbzMuWSm5Re9unZJd/I+BvgLrGnaP8A8E+/D51/4eTfEjwrca1eQazp1unmyWtqb+bddrEFZpPLIDYTDDqCMEjmPC6eAdI+JPgaP9lHxf4wurqTW7ddb8Lxtey6HbacSTcyXK3SDynA4GWLZOAAcGv0gorq+sfvHUtu77/g+6/HV67WVan7W/nf5Xbd12ev4LTe/wAmzeOtF+FX7fniAeKrp9Hi8ZeHNL0/Q7iaCQw3l0s7oYFcKVD5ZeCR1+lfWVFFct1yRjba/wCLb/UtRtOUu9vwSX6BRRRUlhRRRQAUUUUAFT/Cn/kSof8Ar8vf/SuaoKn+FP8AyJUP/X5e/wDpXNVYT/kYQ/wT/wDSqZzYz/d3/iX5SOvooor6s8AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPNov+R48W/8AXxb/APpNHWnWZF/yPHi3/r4t/wD0mjrTr4mh/wAvP8dT/wBLkfU/Zj/hj+SCvMv2jJPiLafCvUL/AOFrxt4tsJYruOykjjf7dCjgzW43qQGdMgHg5xgjNem0V07NNdBaPRnyzJ/wUC+CuqaX/Y/iyPW9H8QXMWy48G6z4ZvJLwvjmEoIWjbJyB82D3xXk/hn9mnxZ4v/AGMPjNoWm+Hbjwmvi7XLjXvDHhG+HlTWdqskUkVu6E4heTyiNmfl3DNff1Fbe0jFNwVm/mtGnt6pb3M4xkpRcndRd9tdmt/R9Ej804U/ZKXwittceAfFsnxJFtsf4f8A2zxANSe724MAHmldpbjzM7ce/wAteseKvBUvwH1T4C/E+w8CX2i+EfCumXela54c0+eXVLnRLe7XcJd3LzLG5bzCMkZ4yBX2pRWrxDvzLe999NmmkvNSa3bXRrW/OsNG3K9rW213TTv5OKfRPqmfC3xo/aS8J/G34xfs9WXgj7ZrmiQ+NYbibxB9hmgtBMsTqLeN5UXe+HLMFyF2jPJwOr8RfErRv2cP2yPHPiX4gm60Lwp4u0HTYdM177HLPaNPbl1eB2jVtj/PkA9seor1j49fBnW/il46+EOtaVdWFva+EPEY1e/S8kdXlhEZXbEFRgXz2YqPevZqzU4qKstnL7nGK/ztp06h7Ocqs23vGOvmpSf+V/XofFnwf8Sr8WP22vihqdtp2oaVpGreBbWLTptStmt5bq388xi4ETgOis4faHAJChsYYV4h8K/h7+zx8IfCg8HfHzw9rXhn4g6RPNbPO15rQttYj8xjFPafZpPLYMpUbVAOQeOtfqFRTjVUFywVla2+u7d7/wDbzvpby2s/YXk5yd23fbT4Yxat1+FPuvvv+enxJ8J+HvD/APwTy+LN/wCGvhtqHwy0vV7+G6hsdX1G4uru9jF5bpHdSrOS8JdVB8vJxjOTnJ+i/wBsX/ky34hf9i7/AESvoCis60/a05U+/wA+iX6f8BHTTXJy+V/Ldp/p5+Z8d/tHa1beB9H/AGZvG2sedB4Y8P6vbS6rfxwPKtnHJYsiyOEBIXdwT71qW3iGy8Vft++GNc0S4j1DT9Q+FT3dlcLkJNG98GjYZGQCCD0719YUVr7Zc0pW1bk//Ao8v4aP+tMoUuSHJf7MV/4DJSv89vxPzf8AAnxC8L+NJNVj/aA8ffEqL4oNe3EU/wAOdJfU7S2WMSMIY7a2s4x5oZcfPvOc5J7mv8IbRNI/Yd/as0saPP4bktta1lhod4xa4sYmtYTFHISzEsFGMknJBOTX6UUUOtenOCVuaNvLdO9vl1b3370oPnU29nf89Px/D7uO+DP/ACR/wL/2AbH/ANJ0rsaKKyqz9pUlO27bFQp+xpQpXvypL7kFcn4s8C+HviR4i0fQfFGjWeu6PcWt55lnfRCRCR5WGGejDswwR2NdZVC2/wCSieH/APr1vP8A2lXHW+x/jh/6XE1l8EvR/kzi/wDhG/iV8BTJN4Zubn4o+AoRn/hHNUuCdd0+MDkWl25xdgdopyHIAAlJGG9F+GXxg8K/FzTri48PagZLqzYRX+lXkTW9/p8uM+XcW7gPE31GCOVJBBrtK86+JnwJ8OfEzUrPW3kvvDvi/T0KWHijQZ/s2oWynnYXwVljz1ilV0OTleTX2p8wei0V4Mvxe8ZfBENB8X9Pi1LwzG2yL4g+HoGNuq5wp1C0GXtie8ke+LJ5MfGfbdH1nT/EWl2up6VfW2p6bdRia3vLOZZYZkIyGR1JDA+oOKALlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHlfx8/Z18OftJaZoWh+M7i9m8K6de/2jcaNZzGBdQmVdsQmkX5/LUNLlFK5LKcjYM29c8A+G/hn8FNc0DwnoVh4d0a30+Xy7LTrdYY87MFiFHzMcDLHJPUk16TXK/FT/km/iX/rwm/9BNebmf8AuNf/AAS/JnThv49P1X5ni37S3x2vvgH4T8P6npfhdfF2pa3r1roNrpzaiLEGWcPsYymNwOUA5A+9nPFZHi79pLWPA+j+D9L1L4e3l38VPFHnfY/A+lalDcbBESXkkvCFjWILtJcjq2MHBNct+3h/yL/we/7KXof/AKFLX0PqGi6FHqieI76w09NRsbd401a4hjE1vCeXUSkZVO5GQPWvLioqipyX2mvkoxdu2718trHu1E+dKD+yn83KSv8ActPxPHPA/wC0trUnxI0nwH8Tfh3d/DXxDrkcsmiyDVIdTsdQ8td0kazxhdsgXnYyjI79M+818k6Rrtx+15+0T4U8TeHIXT4T/Di6uZ4dfkUquu6myGLbbZ+9DECcydGOQOxr62pVI2jFtWb6eXT0b7ej62WcG+aSvdLr59V8vzuuh80eKv2pPiNN8WvGXgz4b/BNviJb+FZLaC/1T/hKrbTAss0IlCCOaPnAJGQx6c4r1z4O+L/G/jTw3c3vjz4f/wDCuNWjumii0v8AtqDVPNhCKRN5sICrliy7TyNme4r56+G/wF+O8GvfFXU4vG2n/DH/AISHxXdaraSRaVb6xdXUG1Ug8wu/lxxhEA2Ab8lssvGfTv2Y/jN4k+IUnjbwh45trKLxx4I1JdN1G50xWW1vUdd8NxGp5TcvVexHbOBajH2dkk3ypve/2bvtu7W3126kOTVT3npzNLs7Xtvrqlft+R6B8XvixoXwT8A6l4t8RNObCzCqtvaR+ZPcyuwWOGJMjc7sQAMgc8kDJryGP9q/xT4S1DQ5/if8INU+H/hbW7yKws9cGr2+oCCaU4iW7hjAaAMeM/OATg4r6C1nQdM8RWsdtq2nWmp28cqzpDeQLMiyKcq4DAgMDyD1FfHf7ZkPxI0+a18Q+OrfSNZ+Amia1bX9/pPhmSWHVzEkieVLcmVWWWNJMMyRGMkY54JEUeTnjGfVryVtPx3tfTbzLrc/I3T3Sdu7etvlttr+B9pV5F8Xv2gH8A+K9K8FeFvCt74/+IGp2z3sOh2NxHbR29srbTcXNxJ8sMZb5QcEk8AV6tp99BqlhbXtrIJba4iWaKQdGRgCD+IIr4k0ub4ieLv27Pjhofg3VdO8NsmnaPHceJb2yF7NYWyQblgt4CyqzyySMxZyVUIflJIwqcOapyS6Jv7v618k7a2LlL925x8vxaX/AA3nvoe9/CH9oe98bePdT8AeM/BN78PfHljYrqg02a9iv7a6tGfZ5sFxGAGw3BBVSM+xx3Xxd8fH4V/C3xZ4xFh/ap0HTLjUfsPneT5/lRl9m/a23OMZ2nHoa8g+FvxF+IPgX47H4Q/EnWbTxm2oaQ+taF4qtdPSxlnSOQJNBcQoSgdcghkABHXk4HvniLw/p/izQNS0TVrZbzS9StpLS6t2YqJYpFKuuQQRkEjIINKqvdUoW1XnbdrrqtVZ+a7CpP3mp30flfZPpo97rydtzF+E/jl/id8MfCvi59POktrmmW+o/YWl80webGH2b9q7sbuu0Z9BXVV4d8dPDup6P4Z8J6TpHxB074RfDjTv9H1rVTeR216tskW2CC1mmVkjJIGXY7sDjPIPzx4Z+Kmh/Dv9pP4aeHvhv8d9d+KWkeJL6aw13R9c1j+2o4F8lmimiuNuIzuX7obnHTGa25I1qj9nom3Zb+l+3b8XpqZOcqNNOpq0tf1t3/rrofVXgr41f8Jh8cviN8Ov7G+yf8IfBp839pfat/2v7VEZMeXsGzbjH3mz7V6dXxFp3wy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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![datatype.png](attachment:datatype.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1: Integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "# In Python, integers are zero, positive or negative whole numbers without a fractional part.\n",
    "\n",
    "a = 2\n",
    "b = -23\n",
    "\n",
    "print(type(a))\n",
    "print(type(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2: Float\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The float type in Python represents the floating point number. Float is used to represent real numbers and is written with a decimal point dividing the integer and fractional parts. For example, 10.23 etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n",
      "<class 'float'>\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "a = 10.23\n",
    "n = 15.698\n",
    "c = 10.0\n",
    "\n",
    "print(type(a))\n",
    "print(type(n))\n",
    "print(type(c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3: Complex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In complex data type there is real and imaginary both the parts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1+2j)\n",
      "<class 'complex'>\n"
     ]
    }
   ],
   "source": [
    "a = complex(1,2)\n",
    "print(a)\n",
    "print(type(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4: Boolean\n",
    "\n",
    "Python comes with Booleans (with predefined True and False displays that are basically just the integers 1 and 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a boolian variable\n",
    "\n",
    "a = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another example could be in case checking\n",
    "\n",
    "2 > 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 == 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5: List:\n",
    "\n",
    "- The most versatile data-structure in python is the list, which can be written as a list of comma-separated values (items) between square brackets. \n",
    "\n",
    "- Lists might contain items of different types, but usually the items all have the same type.\n",
    "\n",
    "- Lists are ordered and changeable. Allows duplicate members as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lists are constructed with brackets [] and commas separating every element in the list.\n",
    "\n",
    "Let's go ahead and see how we can construct lists!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign a list to an variable named my_list\n",
    "\n",
    "my_list = [1,2,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just created a list of integers, but lists can actually hold different object types. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = ['A string',23,100.232,'o']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The len() function will tell you how many items are in the sequence of the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's explore how list stores the the elements:\n",
    "\n",
    "list stores the elements index wise:\n",
    "- first element is stored at index 0\n",
    "- second element is stored at index 1 .. and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexing and Slicing\n",
    "\n",
    "\n",
    "- Indexing: for accessing particular elements from list using index values \n",
    "- Slicing: for accessing some part of list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = ['one','two','three',4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one\n",
      "two\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Grab element at location first, second and fourth\n",
    "\n",
    "print(my_list[0])\n",
    "print(my_list[1])\n",
    "print(my_list[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "# Grab element at last location\n",
    "\n",
    "print(my_list[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cherry', 'orange', 'kiwi']\n"
     ]
    }
   ],
   "source": [
    "# slicing\n",
    "\n",
    "# Grab the elements from 'cherry' to 'Kiwi'\n",
    "\n",
    "thislist = [\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"melon\", \"mango\"]\n",
    "print(thislist[2:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'orange']\n"
     ]
    }
   ],
   "source": [
    "# Grab everything UP TO index 4\n",
    "\n",
    "thislist = [\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"melon\", \"mango\"]\n",
    "print(thislist[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['orange', 'kiwi', 'melon', 'mango']\n"
     ]
    }
   ],
   "source": [
    "# Grab index 3 and everything past it\n",
    "\n",
    "thislist = [\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"melon\", \"mango\"]\n",
    "print(thislist[3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'banana', 'cherry', 'orange', 10, 25, 15.2, 'new item', 5]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatination in list or joining two lists\n",
    "\n",
    "# We can also use \"+\" to concatenate lists.\n",
    "\n",
    "thislist1 = [\"apple\", \"banana\", \"cherry\", \"orange\", 10,25,15.2]\n",
    "\n",
    "thislist1 + ['new item',5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this actually does not change the original list:\n",
    "\n",
    "To make the changes in the original list we have to reassign the list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'banana', 'cherry', 'orange', 10, 25, 15.2]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thislist1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'banana', 'cherry', 'orange', 10, 25, 15.2, 'new item', 5]\n"
     ]
    }
   ],
   "source": [
    "thislist1 = thislist1 + ['new item',5]\n",
    "print(thislist1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic List Methods\n",
    "\n",
    "If you are familiar with another programming language, you might start to draw parallels between arrays in another language and lists in Python. Lists in Python however, tend to be more flexible than arrays in other languages for a two good reasons: they have no fixed size (meaning we don't have to specify how big a list will be), and they have no fixed type constraint (like we've seen above).\n",
    "\n",
    "Let's go ahead and explore some more special methods for lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new list\n",
    "list1 = [1,2,3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### list.append(x)\n",
    "\n",
    "Add an item to the end of the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append\n",
    "list1.append('append me!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 'append me!']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### list.pop(i)\n",
    "\n",
    "Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 'append me!']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pop off the 0 indexed item\n",
    "list1.pop(0)\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign the popped element, remember default popped index is -1 which is last element\n",
    "\n",
    "popped_item = list1.pop()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'append me!'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popped_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### list.insert(i, x)\n",
    "\n",
    "Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's define list1 again\n",
    "\n",
    "list1 = [1,4,5,6,'list',20.3,'lesson','random']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 14, 5, 6, 'list', 20.3, 'lesson', 'random']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1.insert(2,14)\n",
    "list1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### list.remove(x)\n",
    "\n",
    "Remove the first item from the list whose value is equal to x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "list1.remove(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 14, 6, 'list', 20.3, 'lesson', 'random']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# New list\n",
    "\n",
    "new_list = ['a','e','x','b','c']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### list.reverse()\n",
    "\n",
    "Reverse the elements of the list in place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c', 'b', 'x', 'e', 'a']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### list.sort(reverse=False)\n",
    "\n",
    "Sort the items of the list in place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 'e', 'x']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list.sort()\n",
    "new_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nesting Lists\n",
    "\n",
    "A great feature of of Python data structures is that they support nesting. This means we can have data structures within data structures. For example: A list inside a list.\n",
    "\n",
    "Let's see how this works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's make three lists\n",
    "lst_1=[1,2,3]\n",
    "lst_2=[4,5,6]\n",
    "lst_3=[7,8,9]\n",
    "\n",
    "# Make a list of lists to form a matrix\n",
    "matrix = [lst_1,lst_2,lst_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6: Strings\n",
    "\n",
    "\n",
    "- Strings are used in Python to record text information, such as names. \n",
    "\n",
    "- Strings in Python are actually a sequence, which basically means Python keeps track of every element in the string as a sequence. \n",
    "- For example, Python understands the string \"hello' to be a sequence of letters in a specific order. This means we will be able to use indexing to grab particular letters (like the first letter, or the last letter).\n",
    "\n",
    "now we will leqrn more about strings:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a String\n",
    "\n",
    "To create a string in Python you need to use either single quotes or double quotes. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# String of a word\n",
    "\n",
    "'hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'string with single quotes'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# String of entire phrase\n",
    "\n",
    "'string with single quotes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'string with double quotes'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"string with double quotes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing a String\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World 1\n",
      "Hello World 2\n",
      "Use \n",
      " to print a new line\n",
      "\n",
      "\n",
      "See what I mean?\n"
     ]
    }
   ],
   "source": [
    "# We can use a print statement to print a string.\n",
    "\n",
    "print('Hello World 1')\n",
    "print('Hello World 2')\n",
    "print('Use \\n to print a new line')\n",
    "print('\\n')\n",
    "print('See what I mean?')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How elements are stored in a string\n",
    "\n",
    "## ----  add pictures explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Length of string\n",
    "\n",
    "string = 'hello, how are you'\n",
    "len(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign s as a string\n",
    "s = 'Hello World'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H\n",
      "e\n",
      "l\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# String indexing\n",
    "\n",
    "print(s[0])\n",
    "print(s[1])\n",
    "print(s[3])\n",
    "print(s[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "World\n",
      "ello W\n"
     ]
    }
   ],
   "source": [
    "# String slicing\n",
    "\n",
    "print(s[0:5])\n",
    "print(s[6:])\n",
    "print(s[1:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello Worl'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything but the last letter\n",
    "s[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything, but go in steps size of 1\n",
    "s[::1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HloWrd'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grab everything, but go in step sizes of 2\n",
    "s[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dlroW olleH'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can use this to print a string backwards or string reversal\n",
    "s[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important to note that strings have an important property known as immutability. This means that once a string is created, the elements within it can not be changed or replaced. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-976942677f11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Let's try to change the first letter to 'x'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "# Let's try to change the first letter to 'x'\n",
    "s[0] = 'x'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something we can do is concatenate strings!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World concatenate me!'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate strings!\n",
    "s + ' concatenate me!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can reassign s completely though!\n",
    "s = s + ' concatenate me!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World concatenate me!'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Built-in String methods\n",
    "\n",
    "Objects in Python usually have built-in methods. These methods are functions inside the object (we will learn about these in much more depth later) that can perform actions or commands on the object itself.\n",
    "\n",
    "We call methods with a period and then the method name. Methods are in the form:\n",
    "\n",
    "#### *object.method(parameters)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World concatenate me!'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HELLO WORLD CONCATENATE ME!'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upper Case a string\n",
    "s.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello world concatenate me!'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lower case\n",
    "s.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello', 'World', 'concatenate', 'me!']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split a string by blank space (this is the default)\n",
    "s.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hello ', 'orld concatenate me!']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split by a specific element (doesn't include the element that was split on)\n",
    "s.split('W')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7: Tuple:\n",
    "\n",
    "A tuple is a collection which is ordered and unchangeable. In Python tuples are written with round brackets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Constructing Tuples\n",
    "\n",
    "The construction of a tuples use () with elements separated by commas. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a tuple\n",
    "t = (1,2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check len just like a list\n",
    "len(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('one', 2, 3, 4, 6, 'two')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can also mix object types\n",
    "t = ('one',2,3,4,6,'two')\n",
    "\n",
    "# Show\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'one'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use indexing just like we did in lists\n",
    "t[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4, 6)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing just like a list\n",
    "t[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Tuple Methods\n",
    "\n",
    "Tuples have built-in methods, but not as many as lists do. Let's look at two of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .index to enter a value and return the index\n",
    "\n",
    "t.index('one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .count to count the number of times a value appears\n",
    "t.count('one')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Immutability\n",
    "\n",
    "It can't be stressed enough that tuples are immutable. To drive that point home:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-43-1257c0aa9edd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m'change'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "t[0]= 'change'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of this immutability, tuples can't grow. Once a tuple is made we can not add to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'append'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-b75f5b09ac19>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'nope'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
     ]
    }
   ],
   "source": [
    "t.append('nope')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When to use Tuples\n",
    "\n",
    "You may be wondering, \"Why bother using tuples when they have fewer available methods?\" To be honest, tuples are not used as often as lists in programming, but are used when immutability is necessary. \n",
    "\n",
    "If in your program you are passing around an object and need to make sure it does not get changed, then a tuple becomes your solution. It provides a convenient source of data integrity.\n",
    "\n",
    "You should now be able to create and use tuples in your programming as well as have an understanding of their immutability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8: Sets\n",
    "\n",
    "- Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. \n",
    "- Basic uses include membership testing and eliminating duplicate entries. \n",
    "- Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.\n",
    "\n",
    "- Curly braces ```{}``` or the ```set()``` function can be used to create sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We add to sets with the add() method\n",
    "x.add(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add another element\n",
    "x.add(2)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now try to add same element\n",
    "\n",
    "x.add(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list with repeats\n",
    "list1 = [1,1,2,2,3,4,5,6,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3, 4, 5, 6}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cast as set to get unique values\n",
    "set(list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.9: Dictionaries: \n",
    "\n",
    "- Now we're going to switch gears and learn about mappings in Python.\n",
    "- If you're familiar with other languages you can think of these Dictionaries as hash tables.\n",
    "\n",
    "    - ```So what are mappings?``` Mappings are a collection of objects that are stored by a key, unlike a sequence that stored objects by their relative position. This is an important distinction, since mappings won't retain order since they have objects defined by a key.\n",
    "\n",
    "- A dictionary is a collection which is unordered and changeable. In Python dictionaries are written with curly brackets {}.\n",
    "- A Python dictionary consists of a key and then an associated value. That value can be almost any Python object.\n",
    "\n",
    "\n",
    "-- add visual explanation here as well\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a dictionary with {} and : to signify a key and a value\n",
    "\n",
    "my_dict = {'key1':'value1','key2':'value2'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1': 'Ram', 'Gender': 'Male', 'year': 1998}\n"
     ]
    }
   ],
   "source": [
    "# Create and print a dictionary:\n",
    "\n",
    "dict1 = {\n",
    "  \"1\": \"Ram\",\n",
    "  \"Gender\": \"Male\",\n",
    "  \"year\": 1998\n",
    "}\n",
    "print(dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-96-90e2046cfae3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdict1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "dict1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value2'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call values by their key\n",
    "my_dict['key2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it's important to know that dictonaries are flexible in holding data types\n",
    "\n",
    "my_dict = {'key1':123,'key2':[12,23,33],'key3':['item0','item1','item2']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': 'Ram', 'Gender': 'Male', 'year': 1998}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': 'Ram', 'Gender': 'Male', 'year': 2020}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change Values\n",
    "\n",
    "dict1[\"year\"] = 2020\n",
    "\n",
    "dict1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Conditional Statements and Loops\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1: if, elif, else Statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```if``` Statements in Python allows us to tell the computer to perform alternative actions based on a certain set of results.\n",
    "\n",
    "Verbally, we can imagine we are telling the computer:\n",
    "\n",
    "\"Hey ```if``` this case happens, perform some action\"\n",
    "\n",
    "We can then expand the idea further with ```elif``` and ```else``` statements, which allow us to tell the computer:\n",
    "\n",
    "\"Hey ```if``` this case happens, perform some action. ```Else```, if another case happens, perform some other action. ```Else```, if none of the above cases happened, perform this action.\"\n",
    "\n",
    "Let's go ahead and look at the syntax format for if statements to get a better idea of this:\n",
    "\n",
    "\n",
    "```if case1:\n",
    "    perform action1\n",
    "elif case2:\n",
    "    perform action2\n",
    "else: \n",
    "    perform action3```"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![conditional_statements.jpg](attachment:conditional_statements.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "three\n"
     ]
    }
   ],
   "source": [
    "# Example\n",
    "\n",
    "x = 3\n",
    "\n",
    "if x == 2:\n",
    "    print('two')\n",
    "elif x == 3:\n",
    "    print('three')\n",
    "elif x == 4:\n",
    "    print('four')\n",
    "else:\n",
    "    print('something else')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2: For loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```A for loop acts as an iterator in Python;``` it goes through items that are in a sequence or any other iterable item. \n",
    "\n",
    "Objects that we've learned about that we can iterate over include strings, lists, tuples, and even built-in iterables for dictionaries, such as keys or values.\n",
    "\n",
    "\n",
    "Here's the general format for a for loop in Python:\n",
    "\n",
    "```for item in object:\n",
    "    statements to execute```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's uderstand it with an example\n",
    "\n",
    "# let's just take a list of numbers from 1 to 10\n",
    "\n",
    "list1 = [1,2,3,4,5,6,7,8,9,10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": [
    "# Now iterate through all the numnbers from the list and print numebrs\n",
    "\n",
    "for num in list1:\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "# Example: Let's print only the even numbers from that list!\n",
    "\n",
    "for num in list1:\n",
    "    if num % 2 == 0:\n",
    "        print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example to do : sum all the numbers using for loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3: While loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to for loop there is while loop as well:\n",
    "\n",
    "For more detailed information you may visit: https://docs.python.org/3/tutorial/controlflow.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Python function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1: Python Functions:\n",
    "\n",
    "- Functions allow us to not have to repeatedly write the same code again and again. \n",
    "    - If you remember back to the lessons on strings and lists, remember that we used a function len() to get the length of a string. Since checking the length of a sequence is a common task you would want to write a function that can do this repeatedly at command.\n",
    "- Functions will be one of most basic levels of reusing code in Python, and it will also allow us to start thinking of program design."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to define a function ?\n",
    "\n",
    "The keyword ```def``` introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters.\n",
    "\n",
    "#### def keyword\n",
    "Let's see how to build out a function's syntax in Python. It has the following form:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def name_of_function(arg1,arg2):\n",
    "    '''\n",
    "    This is where the function's Document String (docstring) goes.\n",
    "    When you call help() on your function it will be printed out.\n",
    "    '''\n",
    "    # Do stuff here\n",
    "    # Return desired result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simple function example \n",
    "\n",
    "def say_hello():\n",
    "    return 'Hello, welcome to IIT Kharagpur'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, welcome to IIT Kharagpur'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calling function \n",
    "\n",
    "hello = say_hello()\n",
    "hello"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example: Addition function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_numbers(n1,n2):\n",
    "    '''\n",
    "    input: n1,n2 as argument\n",
    "    return: addition of those numbers\n",
    "    '''\n",
    "    return n1+n2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "addition = add_numbers(2,3)\n",
    "print(addition)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2: Important inbuilt function\n",
    "\n",
    "### range()\n",
    "\n",
    "Python range function basically is an inbuilt function which returns the sequence of the given number between the given range.\n",
    "\n",
    "- Syntax looks like this\n",
    "\n",
    "```range(start, stop, step)```\n",
    "\n",
    "- it will always start from given start number and will upto stop but not that exact number\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": [
    "# Example:\n",
    "\n",
    "\n",
    "# print the number from 1 to 10\n",
    "\n",
    "for i in range(1,11):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Python Libraries:\n",
    "    1. Numpy\n",
    "    2. Pandas\n",
    "    3. Matplotlib\n",
    "    4. Seaborn etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are going to learn about some importnat libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1: Numpy:\n",
    "\n",
    "NumPy is a powerful linear algebra library for Python. What makes it so important is that almost all of the libraries in the PyData ecosystem (pandas, scipy, scikit-learn, etc.) rely on NumPy as one of their main building blocks. Plus we will use it to generate data for analysis examples later on!\n",
    "\n",
    "NumPy is also incredibly fast, as it has bindings to C libraries. For more info please visit: https://numpy.org/\n",
    "\n",
    "We will only learn the basics of NumPy. To get started we need to install it!\n",
    "\n",
    "```pip install numpy (or) conda install numpy```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy arrays essentially come in two flavors: vectors and matrices. \n",
    "\n",
    "Vectors are strictly 1-dimensional (1D) arrays and matrices are 2D (but you should note a matrix can still have only one row or one column)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why use Numpy array? Why not just a list?\n",
    "\n",
    "There are lot's of reasons to use a Numpy array instead of a \"standard\" python list object. Our main reasons are:\n",
    "* Memory Efficiency of Numpy Array vs list\n",
    "* Easily expands to N-dimensional objects\n",
    "* Speed of calculations of numpy array\n",
    "* Broadcasting operations and functions with numpy\n",
    "* All the data science and machine learning libraries we use are built with Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin our introduction by exploring how to create NumPy arrays.\n",
    "\n",
    "## Creating NumPy Arrays from Objects\n",
    "\n",
    "### From a Python List\n",
    "\n",
    "We can create an array by directly converting a list or list of lists:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------\n",
    "-----------------------------\n",
    "\n",
    "## Although all the exapmples and explanations are given in this notebook, but you can go through this official numpy page for best visualization and understanding.\n",
    "\n",
    "------------------------------\n",
    "------------------------------\n",
    "\n",
    "Ref: https://numpy.org/doc/stable/user/absolute_beginners.html\n",
    "\n",
    "Ref2: https://betterprogramming.pub/numpy-illustrated-the-visual-guide-to-numpy-3b1d4976de1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [1,2,3]\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_matrix = [[1,2,3],[4,5,6],[7,8,9]]\n",
    "my_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(my_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Built-in Methods to create arrays\n",
    "\n",
    "There are lots of built-in ways to generate arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### arange\n",
    "\n",
    "Return evenly spaced values within a given interval. [[reference](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.arange.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linspace \n",
    "Return evenly spaced numbers over a specified interval. [[reference](https://www.numpy.org/devdocs/reference/generated/numpy.linspace.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.26315789, 0.52631579, 0.78947368, 1.05263158,\n",
       "       1.31578947, 1.57894737, 1.84210526, 2.10526316, 2.36842105,\n",
       "       2.63157895, 2.89473684, 3.15789474, 3.42105263, 3.68421053,\n",
       "       3.94736842, 4.21052632, 4.47368421, 4.73684211, 5.        ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,5,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=green>Note that `.linspace()` *includes* the stop value. To obtain an array of common fractions, increase the number of items:</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### eye\n",
    "\n",
    "Creates an identity matrix [[reference](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.eye.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random \n",
    "Numpy also has lots of ways to create random number arrays:\n",
    "\n",
    "### rand\n",
    "Creates an array of the given shape and populates it with random samples from a uniform distribution over ``[0, 1)``. [[reference](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.rand.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4223209 , 0.94492077])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.17258662, 0.91442273, 0.52654456, 0.4536185 , 0.22691342],\n",
       "       [0.65254301, 0.14659406, 0.08355   , 0.54036603, 0.33241189],\n",
       "       [0.01436881, 0.41527728, 0.10467875, 0.76678177, 0.47435695],\n",
       "       [0.82408576, 0.11605832, 0.50291298, 0.19548843, 0.78229317],\n",
       "       [0.39512595, 0.08979817, 0.22252853, 0.4149035 , 0.35563182]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(5,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### randn\n",
    "\n",
    "Returns a sample (or samples) from the \"standard normal\" distribution [σ = 1]. Unlike **rand** which is uniform, values closer to zero are more likely to appear. [[reference](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.randn.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.01553943, 0.04771713])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.00889029, -0.29258098, -0.08728811, -0.78132545, -0.93482449],\n",
       "       [ 1.22721088, -0.2989267 ,  0.66981707, -1.44403639, -1.4617547 ],\n",
       "       [ 0.64264665, -0.22600588, -0.02173558,  0.05771542,  2.07531435],\n",
       "       [ 1.20274783, -0.14197603, -0.38765877, -0.5462867 ,  1.98890202],\n",
       "       [-2.78736702, -1.87233057, -0.72528991, -0.45801773,  0.93888827]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(5,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### randint\n",
    "Returns random integers from `low` (inclusive) to `high` (exclusive).  [[reference](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.randint.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 65, 21, 32, 69, 51, 84, 62, 72, 12])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array Attributes and Methods\n",
    "\n",
    "Let's discuss some useful attributes and methods for an array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(25)\n",
    "ranarr = np.random.randint(0,50,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 10, 43, 15, 34, 10, 17, 49, 14, 32])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape\n",
    "Returns an array containing the same data with a new shape. [[reference](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.reshape.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(5,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bracket Indexing and Selection\n",
    "The simplest way to pick one or some elements of an array looks very similar to python lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get a value at an index\n",
    "arr[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get values in a range\n",
    "arr[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100, 100, 100, 100, 100,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Setting a value with index range (Broadcasting)\n",
    "arr[0:5]=100\n",
    "\n",
    "#Show\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset array, we'll see why I had to reset in  a moment\n",
    "arr = np.arange(0,11)\n",
    "\n",
    "#Show\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing a 2D array (matrices)\n",
    "\n",
    "The general format is **arr_2d[row][col]** or **arr_2d[row,col]**. I recommend using the comma notation for clarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5, 10, 15],\n",
       "       [20, 25, 30],\n",
       "       [35, 40, 45]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))\n",
    "\n",
    "#Show\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 25, 30])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Indexing row\n",
    "arr_2d[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format is arr_2d[row][col] or arr_2d[row,col]\n",
    "\n",
    "# Getting individual element value\n",
    "arr_2d[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Getting individual element value\n",
    "arr_2d[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 15],\n",
       "       [25, 30]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2D array slicing\n",
    "\n",
    "#Shape (2,2) from top right corner\n",
    "arr_2d[:2,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similarly there are many numpy operations as well which can be performed in an numpy array\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(0,10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr + arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr - arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr * arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-47-11ca4d9cc351>:3: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arr/arr\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will raise a Warning on division by zero, but not an error!\n",
    "# It just fills the spot with nan\n",
    "arr/arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-48-9b0f118908c2>:2: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  1/arr\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([       inf, 1.        , 0.5       , 0.33333333, 0.25      ,\n",
       "       0.2       , 0.16666667, 0.14285714, 0.125     , 0.11111111])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Also a warning (but not an error) relating to infinity\n",
    "1/arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   8,  27,  64, 125, 216, 343, 512, 729], dtype=int32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 1.        , 1.41421356, 1.73205081, 2.        ,\n",
       "       2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking Square Roots\n",
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,\n",
       "       5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03,\n",
       "       2.98095799e+03, 8.10308393e+03])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating exponential (e^)\n",
    "np.exp(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.84147098,  0.90929743,  0.14112001, -0.7568025 ,\n",
       "       -0.95892427, -0.2794155 ,  0.6569866 ,  0.98935825,  0.41211849])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trigonometric Functions like sine\n",
    "np.sin(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-f71dc4fc6401>:2: RuntimeWarning: divide by zero encountered in log\n",
      "  np.log(arr)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([      -inf, 0.        , 0.69314718, 1.09861229, 1.38629436,\n",
       "       1.60943791, 1.79175947, 1.94591015, 2.07944154, 2.19722458])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking the Natural Logarithm\n",
    "np.log(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 18, 21, 24])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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