Coursework

Author: KishalayP

.vscode/settings.json

@@ -1,4 +1,5 @@
 {
     "github.gitCommand": "C:\\Users\\Name\\AppData\\Local\\Programs\\Microsoft VS Code\\Code.exe",
-    "python.pythonPath": "C:\\Users\\Name\\AppData\\Local\\Programs\\Python\\Python39\\python.exe"
+    "python.pythonPath": "C:\\Users\\Name\\AppData\\Local\\Programs\\Python\\Python39\\python.exe",
+    "python.languageServer": "Pylance"
 }

6. Python 101 - Files - Skeleton before filming.ipynb

@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 3,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -81,18 +81,16 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "I am learning python now!\n",
-      "It is super fun to learn Python programming \n",
-      "I am learning how to handle files in python!THis is a new line that I added to the file!THis is a new line that I added to the file!THis is a new line that I added to the file!THis is a new line that I added to the file!THis is a new line that I added to the file!THis is a new line that I added to the file!\n"
+      "First Name,Last Name,Email,Postal Code,Net Worth\nJoseph,Patton,[email protected],M6U 5U7,\"$2,629.13 \"\nNoah,Moran,[email protected],K2D 4M9,\"$8,626.96 \"\nNina,Keller,[email protected],S1T 4E6,\"$9,072.02 \"\n\n"
      ]
     }
    ],
    "source": [
     "# Open and read text file\n",
-    "f=open('D:\\Python+for+Financial+Analysis+-+Course+Package\\Part 1. Python Programming Fundamentals\\sample_text_file.txt','r')\n",
+    "f=open('G:\\Python+for+Financial+Analysis+-+Course+Package\\Part 1. Python Programming Fundamentals\\sample_text_file.txt','r')\n",
     "print(f.read())"
    ]
   },
@@ -886,9 +884,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.9.0-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

7. Python 101 - Python Libraries for Data Analysis - Numpy Skeleton.ipynb

@@ -1440,30 +1440,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {
     "id": "UujCvuhMCt4K"
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([[6, 4, 3, 9, 5],\n",
+       "       [4, 1, 5, 3, 4],\n",
+       "       [8, 8, 4, 3, 9],\n",
+       "       [6, 7, 4, 5, 3],\n",
+       "       [1, 4, 9, 2, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 54
+    }
+   ],
+   "source": [
+    "y=np.random.randint(1,10,(5,5))\n",
+    "y"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {
     "id": "JBgJ8ugyCvht"
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([6, 4, 9, 5, 4, 5, 4, 8, 8, 4, 9, 6, 7, 4, 5, 4, 9, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    }
+   ],
+   "source": [
+    "n_y=y[y>3]\n",
+    "n_y"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {
     "id": "TIQ-A7IBCvgl"
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([3, 9, 5, 1, 5, 3, 3, 9, 7, 5, 3, 1, 9, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "n_y=y[y%2==1]\n",
+    "n_y"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1486,12 +1532,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {
     "id": "AbN-vySf9gen"
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([[ 2, 30, 20,  0,  0],\n",
+       "       [25,  4, 40,  0,  0],\n",
+       "       [ 0,  4,  0, 90, 10],\n",
+       "       [25, 25, 34, 22, 12],\n",
+       "       [25, 24, 22, 32, 25]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 71
+    }
+   ],
+   "source": [
+    "x=np.array([[2, 30, 20, -2, -4],\n",
+    "    [3, 4,  40, -3, -2],\n",
+    "    [-3, 4, -6, 90, 10],\n",
+    "    [25, 45 ,34 ,22 ,12],\n",
+    "    [13, 24, 22, 32, 37]])\n",
+    "x[x<0]=0\n",
+    "x[x%2==1]=25\n",
+    "x"
+   ]
   },
   {
    "cell_type": "markdown",

8. Python 101 - Python Libraries for Data Analysis - Pandas Skeleton.ipynb

@@ -0,0 +1,27 @@
+import numpy as np
+import os
+import time
+
+os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
+
+import keras
+import keras.applications as kapp
+from keras.datasets import cifar10
+
+(x_train, y_train_cats), (x_test, y_test_cats) = cifar10.load_data()
+batch_size = 8
+x_train = x_train[:batch_size]
+x_train = np.repeat(np.repeat(x_train, 7, axis=1), 7, axis=2)
+model = kapp.VGG19()
+model.compile(optimizer='sgd', loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+print("Running initial batch (compiling tile program)")
+y = model.predict(x=x_train, batch_size=batch_size)
+
+# Now start the clock and run 10 batches
+print("Timing inference...")
+start = time.time()
+for i in range(1):
+    y = model.predict(x=x_train, batch_size=batch_size)
+print("Ran in {} seconds".format(time.time() - start))

9. Python 101 - Data Visualization with matplotlib - Skeleton b4 filming.ipynb

@@ -0,0 +1,4 @@
+,First Name,Last Name,Email,Postal Code,Net Worth
+0,Joseph,Patton,[email protected],M6U 5U7,"$2,629.13 "
+1,Noah,Moran,[email protected],K2D 4M9,"$8,626.96 "
+2,Nina,Keller,[email protected],S1T 4E6,"$9,072.02 "

Untitled-1.py

@@ -0,0 +1,4 @@
+First Name,Last Name,Email,Postal Code,Net Worth
+Joseph,Patton,[email protected],M6U 5U7,"$2,629.13 "
+Noah,Moran,[email protected],K2D 4M9,"$8,626.96 "
+Nina,Keller,[email protected],S1T 4E6,"$9,072.02 "