ds

.ipynb_checkpoints/Google_DS_Simulations-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

.ipynb_checkpoints/Numpy_Examples-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## matmul v.s dot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the documentation:\n",
+    "\n",
+    "1matmul differs from dot in two important ways.\n",
+    "\n",
+    "Multiplication by scalars is not allowed.\n",
+    "Stacks of matrices are broadcast together as if the matrices were elements.\n",
+    "The last point makes it clear that dot and matmul methods behave differently when passed 3D (or higher dimensional) arrays. Quoting from the documentation some more:\n",
+    "\n",
+    "2For matmul:\n",
+    "\n",
+    "If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the **last two indexes** and broadcast accordingly.\n",
+    "\n",
+    "3For np.dot:\n",
+    "\n",
+    "For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors (without complex conjugation). For N dimensions it is a sum product over the **last axis of a and the second-to-last of b**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c shape:(8, 15, 16) d shape: (8, 15, 8, 16)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "a = np.random.rand(8,15,13)\n",
+    "b = np.random.rand(8,13,16)\n",
+    "c = a @ b  # Python 3.5+\n",
+    "d = np.dot(a, b)\n",
+    "print('c shape:{} d shape: {}'.format(c.shape,d.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "a.ravel():\n",
+    "(i) Return only reference/view of original array\n",
+    "(ii) If you modify the array you would notice that the value of original array also changes.\n",
+    "(iii) Ravel is faster than flatten() as it does not occupy any memory.\n",
+    "(iv) Ravel is a library-level function.\n",
+    "\n",
+    "a.flatten() :\n",
+    "(i) Return copy of original array\n",
+    "(ii) If you modify any value of this array value of original array is not affected.\n",
+    "(iii) Flatten() is comparatively slower than ravel() as it occupies memory.\n",
+    "(iv) Flatten is a method of an ndarray object.\n",
+    "\n",
+    "Let us check out the difference by this cod"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.arange(12).reshape((3,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  4,  8],\n",
+       "       [ 1,  5,  9],\n",
+       "       [ 2,  6, 10],\n",
+       "       [ 3,  7, 11]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 10, 11]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1.simulation coding，9个normal rv， mean和sigma都不同，问这9个数的 max expectation是多少，期望的uncertainty是多少"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "n = 9\n",
+    "sample_size =1000\n",
+    "mean = 10*np.random.random(size=n)\n",
+    "var = 100*np.random.random(size=n)\n",
+    "sample_mean = []\n",
+    "sample_var = []\n",
+    "samples = []\n",
+    "for m, v in zip(mean, var):\n",
+    "    sample = np.random.normal(loc=m, scale=v,size=sample_size)\n",
+    "    if len(samples) == 0:\n",
+    "        samples = sample\n",
+    "    else:\n",
+    "        samples = np.vstack((sample,samples))\n",
+    "sample_max = np.max(samples,axis=0)\n",
+    "sample_mean = np.mean(sample_max)\n",
+    "sample_var = np.var(sample_max)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "list comprehension\n",
+    "Python's conditional expression \n",
+    "\n",
+    "1.[a if C else b]\n",
+    "2.[ expression for item in list if conditional ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[27, 18, 46, 51, 99, 70, 48, 49, 6]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "l = [22, 13, 45, 50, 98, 69, 43, 44, 1]\n",
+    "[x+1 if x >= 45 else x+5 for x in l]"
+   ]
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

.ipynb_checkpoints/Pandas_Examples-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}