From 6b304eb0ca9f2b7cbd31198f9e126c19736a6071 Mon Sep 17 00:00:00 2001
From: ShivanS93 <shivan.sivakumaran@gmail.com>
Date: Sun, 26 Sep 2021 12:59:32 +1300
Subject: [PATCH] adding zero_2_hundred

---
 README.MD                           |   11 +
 zero_2_hundred/Pipfile              |   12 +
 zero_2_hundred/Pipfile.lock         |  107 +++
 zero_2_hundred/README.MD            |   14 +
 zero_2_hundred/f.png                |  Bin 0 -> 18651 bytes
 zero_2_hundred/f_better.png         |  Bin 0 -> 19974 bytes
 zero_2_hundred/zero_2_hundred.ipynb | 1087 +++++++++++++++++++++++++++
 7 files changed, 1231 insertions(+)
 create mode 100644 zero_2_hundred/Pipfile
 create mode 100644 zero_2_hundred/Pipfile.lock
 create mode 100644 zero_2_hundred/README.MD
 create mode 100644 zero_2_hundred/f.png
 create mode 100644 zero_2_hundred/f_better.png
 create mode 100644 zero_2_hundred/zero_2_hundred.ipynb

diff --git a/README.MD b/README.MD
index 8ba74c3..2c0e10c 100644
--- a/README.MD
+++ b/README.MD
@@ -12,4 +12,15 @@ Pytest is a great tool that allows us to write automated test for our code. This
 
 [Check it out!](https://git.chch.tech/Shivan/learning_python/src/branch/main/intro_pytest)
 
+## Zero to Hundred in Python
+
+26th September 2021
+
+Programming is a powerful tool and Python is an easy way to get started.
+
+[Check it out!](https://git.chch.tech/Shivan/learning_pythong/src/branch/main/zero_2_hundred)
+
 ## What's next?
+- Classes
+- Django intro
+- Setting up postgres
diff --git a/zero_2_hundred/Pipfile b/zero_2_hundred/Pipfile
new file mode 100644
index 0000000..4af3735
--- /dev/null
+++ b/zero_2_hundred/Pipfile
@@ -0,0 +1,12 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+pandas = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.8"
diff --git a/zero_2_hundred/Pipfile.lock b/zero_2_hundred/Pipfile.lock
new file mode 100644
index 0000000..93fd3ed
--- /dev/null
+++ b/zero_2_hundred/Pipfile.lock
@@ -0,0 +1,107 @@
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diff --git a/zero_2_hundred/README.MD b/zero_2_hundred/README.MD
new file mode 100644
index 0000000..351642e
--- /dev/null
+++ b/zero_2_hundred/README.MD
@@ -0,0 +1,14 @@
+# Zero to Hundred in Python
+
+By Shivan Sivakumaran. 26th September 2021
+
+## An overview
+
+Programming is a powerful tool. Python is a general purpose programming language that makes it easy to start the journey of learning how to code.
+
+Here is a quick demo from zero to hundred.
+
+## Further resources
+
+- [Blog post](https://shivansivakumaran.com/coding/zero-to-hundred-python/)
+- [Video]()
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HcmV?d00001

diff --git a/zero_2_hundred/zero_2_hundred.ipynb b/zero_2_hundred/zero_2_hundred.ipynb
new file mode 100644
index 0000000..e2431e0
--- /dev/null
+++ b/zero_2_hundred/zero_2_hundred.ipynb
@@ -0,0 +1,1087 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "auburn-huntington",
+   "metadata": {},
+   "source": [
+    "# Getting started with Python / Interactive Python with Jupyter Lab\n",
+    "\n",
+    "## Installing miniconda\n",
+    "\n",
+    "- https://docs.conda.io/en/latest/miniconda.html\n",
+    "- https://youtu.be/YqUb9XALnDE?t=209"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "labeled-unknown",
+   "metadata": {},
+   "source": [
+    "### Printing and Variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "asian-catalog",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello World\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "robust-customs",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# comments are done like this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "chicken-broadcast",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = 10\n",
+    "string = \"This is a string\"\n",
+    "\n",
+    "print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "suburban-august",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a string\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "dramatic-brand",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number: 10, and string: This is a string\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"number: {number}, and string: {string}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "female-spain",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'int'> and <class 'str'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{type(number)} and {type(string)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "immediate-worst",
+   "metadata": {},
+   "source": [
+    "### Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "favorite-damages",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8...\n",
+    "\n",
+    "def f(n: int) -> int:\n",
+    "    \"\"\"\n",
+    "    Fibonacci sequence\n",
+    "    ---\n",
+    "    params\n",
+    "    n (int) number requested in sequence\n",
+    "    \n",
+    "    returns\n",
+    "    interger in fibonacci sequence\n",
+    "    \"\"\"\n",
+    "    if n == 0:\n",
+    "        return 0\n",
+    "    if n == 1:\n",
+    "        return 1\n",
+    "    if n == 2:\n",
+    "        return 1\n",
+    "    if n >= 3:\n",
+    "        return f(n-1) + f(n-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "reliable-nudist",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Fibonacci sequence\n",
+       "---\n",
+       "params\n",
+       "n (int) number requested in sequence\n",
+       "\n",
+       "returns\n",
+       "interger in fibonacci sequence\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/JupyterlabProjects/Saxon/<ipython-input-20-d0995f361bf4>\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "?f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "leading-literacy",
+   "metadata": {},
+   "source": [
+    "### More data type - Lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "alternate-fault",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(0, 10)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r = range(10)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "tutorial-buying",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst = list(r)\n",
+    "lst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "south-czech",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "administrative-class",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst[1:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "grateful-addiction",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "brown-density",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst[::-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "exciting-fundamental",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "4\n",
+      "0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "def f1(x: int) -> int:\n",
+    "    return x + 1\n",
+    "\n",
+    "def f2(x: int) -> int:\n",
+    "    return x ** 2\n",
+    "\n",
+    "def f3(x: int) -> float:\n",
+    "    return 1 / x\n",
+    "\n",
+    "funcs = [f1, f2, f3]\n",
+    "\n",
+    "for func in funcs:\n",
+    "    print(func(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "together-lancaster",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0, 1, 1, 2, 3, 5, 8, 13, 21, 34, "
+     ]
+    }
+   ],
+   "source": [
+    "for i in lst:\n",
+    "    output = f(i)\n",
+    "    print(output, end=\", \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "governmental-karma",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output = []\n",
+    "for i in lst:\n",
+    "    output.append(f(i))\n",
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "latter-plumbing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[f(i) for i in rng]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "offensive-documentation",
+   "metadata": {},
+   "source": [
+    "### Importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "organizational-easter",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "288 ns ± 246 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "302 ns ± 252 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "304 ns ± 204 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "623 ns ± 288 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "957 ns ± 326 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "2.39 µs ± 880 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "2.28 µs ± 424 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "3.56 µs ± 411 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "5.62 µs ± 447 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "9.05 µs ± 598 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "14.4 µs ± 623 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "23.9 µs ± 2.03 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "36.9 µs ± 534 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "61.1 µs ± 3.57 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "96.1 µs ± 506 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "155 µs ± 677 ns per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "252 µs ± 5.16 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "485 µs ± 183 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "807 µs ± 125 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "1.45 ms ± 245 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "1.84 ms ± 152 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "3.27 ms ± 529 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "4.65 ms ± 601 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "7.25 ms ± 602 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "12 ms ± 1.27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "18.7 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "30.7 ms ± 3.21 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "63.5 ms ± 22.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "115 ms ± 38.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "130 ms ± 9.28 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "251 ms ± 50.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "392 ms ± 73.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
+      "629 ms ± 104 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "times = []\n",
+    "for i in range(33):\n",
+    "    t = %timeit -n1 -o f(i)\n",
+    "    times.append(t.best)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "demanding-circulation",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>f</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.580001e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.819999e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.979997e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.560002e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.710000e-07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              f\n",
+       "0  1.580001e-07\n",
+       "1  1.819999e-07\n",
+       "2  1.979997e-07\n",
+       "3  4.560002e-07\n",
+       "4  7.710000e-07"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame(data=times, columns=[\"f\"])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "complete-warren",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "g = df.plot(title=\"F time\")\n",
+    "g.set_xlabel(\"n\")\n",
+    "g.set_ylabel(\"time (s)\")\n",
+    "plt.savefig(\"f.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "empirical-tolerance",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "f    3.693825\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dress-series",
+   "metadata": {},
+   "source": [
+    "### More data types - Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "fifth-stereo",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dct = {\n",
+    "    \"key1\": \"value1\",\n",
+    "    \"key2\": [\"value2\", \"value3\"],\n",
+    "    \"random name for a key\": 1,\n",
+    "    \"key4\": (2000, 3000)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "periodic-estate",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2000, 3000)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct[\"key4\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "buried-plant",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dct['key5'] will error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "precise-possession",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dct.get(\"key5\") is None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "perfect-commander",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "key: key1, value: value1\n",
+      "key: key2, value: ['value2', 'value3']\n",
+      "key: random name for a key, value: 1\n",
+      "key: key4, value: (2000, 3000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k,v in dct.items():\n",
+    "    print(f\"key: {k}, value: {v}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "prompt-maldives",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cache = {}\n",
+    "\n",
+    "def f_better(n: int) -> int:\n",
+    "    \"\"\"\n",
+    "    Fibonacci sequence - !!with caching!!\n",
+    "    ---\n",
+    "    params\n",
+    "        n (int) number requested in sequence\n",
+    "    \n",
+    "    returns\n",
+    "        interger in fibonacci sequence\n",
+    "    \"\"\"\n",
+    "    if all([cache.get(n-1), cache.get(n-2)]):\n",
+    "        return cache[n-1] + cache[n-2]   \n",
+    "    elif n == 0:\n",
+    "        return 0\n",
+    "    elif n == 1 or n == 2:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        cache[n] = f_better(n-1) + f_better(n-2) \n",
+    "        return cache[n]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "north-logistics",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>f</th>\n",
+       "      <th>f_better</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.580001e-07</td>\n",
+       "      <td>0.000001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.819999e-07</td>\n",
+       "      <td>0.000001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.979997e-07</td>\n",
+       "      <td>0.000001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.560002e-07</td>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.710000e-07</td>\n",
+       "      <td>0.000006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              f  f_better\n",
+       "0  1.580001e-07  0.000001\n",
+       "1  1.819999e-07  0.000001\n",
+       "2  1.979997e-07  0.000001\n",
+       "3  4.560002e-07  0.000003\n",
+       "4  7.710000e-07  0.000006"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "times = []\n",
+    "for i in range(35):\n",
+    "    t = %timeit -n1 -o -q f_better(i)\n",
+    "    times.append(t.best)\n",
+    "    \n",
+    "df['f_better'] = pd.DataFrame(data=times)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "honest-rings",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = df.plot(title=\"F time\")\n",
+    "g.set_xlabel(\"n\")\n",
+    "g.set_ylabel(\"time (s)\")\n",
+    "plt.savefig(\"f_better.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "egyptian-seminar",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{3: 2,\n",
+       " 4: 3,\n",
+       " 6: 8,\n",
+       " 7: 13,\n",
+       " 9: 34,\n",
+       " 10: 55,\n",
+       " 12: 144,\n",
+       " 13: 233,\n",
+       " 15: 610,\n",
+       " 16: 987,\n",
+       " 18: 2584,\n",
+       " 19: 4181,\n",
+       " 21: 10946,\n",
+       " 22: 17711,\n",
+       " 24: 46368,\n",
+       " 25: 75025,\n",
+       " 27: 196418,\n",
+       " 28: 317811,\n",
+       " 30: 832040,\n",
+       " 31: 1346269,\n",
+       " 33: 3524578,\n",
+       " 34: 5702887}"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cache"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "loving-jungle",
+   "metadata": {},
+   "source": [
+    "### Classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "parallel-sharp",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Car:\n",
+    "    \"\"\"\n",
+    "    Class for cars that people drive\n",
+    "    ---\n",
+    "    params \n",
+    "        make (str): make of the car\n",
+    "        model (str): model of the car\n",
+    "        year (int): year car was made\n",
+    "    \"\"\"\n",
+    "    def __init__(self, make: str, model: str, year: int=2021):\n",
+    "        self.make = make\n",
+    "        self.model = model\n",
+    "        self.year = year\n",
+    "        self.__is_driving = False\n",
+    "        \n",
+    "    def __repr__(self):\n",
+    "        return f'{self.make} - {self.model}'\n",
+    "    \n",
+    "    def drive(self) -> str:\n",
+    "        if not self.__is_driving:\n",
+    "            print(f'{self.make} - {self.model} is now driving')\n",
+    "            self.__is_driving = True\n",
+    "        else:\n",
+    "            print(f'{self.make} - {self.model} is ALREADY driving!')\n",
+    "    \n",
+    "    def stop(self) -> str:\n",
+    "        if self.__is_driving:\n",
+    "            print(f'{self.make} - {self.model} has stopped')\n",
+    "            self.__is_driving = False\n",
+    "        else:\n",
+    "            print(f'{self.make} - {self.model} is ALREADY stopped!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "protective-lying",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mCar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myear\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2021\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m     \n",
+       "Class for cars that people drive\n",
+       "---\n",
+       "params \n",
+       "    make (str): make of the car\n",
+       "    model (str): model of the car\n",
+       "    year (int): year car was made\n",
+       "\u001b[0;31mType:\u001b[0m           type\n",
+       "\u001b[0;31mSubclasses:\u001b[0m     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "?Car"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "dimensional-shanghai",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shivan = Car('Toyota', 'Corolla', '2008')\n",
+    "bruno = Car('Tesla', 'Model3')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "raised-brook",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tesla - Model3"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bruno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "earlier-diploma",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toyota - Corolla is now driving\n"
+     ]
+    }
+   ],
+   "source": [
+    "shivan.drive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "varied-setting",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toyota - Corolla is ALREADY driving!\n"
+     ]
+    }
+   ],
+   "source": [
+    "shivan.drive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "recognized-forwarding",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tesla - Model3 is ALREADY stopped!\n"
+     ]
+    }
+   ],
+   "source": [
+    "bruno.stop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "executed-cleanup",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}