Examples for Pandas, Numpy, Statsmodels and Plotting added

Python Tutorial - Basics.ipynb

@@ -79,17 +79,6 @@
     "**Mind the difference between \\/ and \\/\\/**"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Attention!\n",
-    "print(\"That's a: \", type(6/3))\n",
-    "print(\"And that's a \",type(6//3))\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -155,6 +144,56 @@
     "# ...\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nalso \\none\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# multiline string\n",
+    "\"\"\"\n",
+    "this is one\n",
+    "...\n",
+    "\"\"\"\n",
+    "# and this\n",
+    "'''\n",
+    "also \n",
+    "one\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# keep in mind\n",
+    "''==\"\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -162,6 +201,17 @@
     "## Basic Math Hints"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Attention!\n",
+    "print(\"That's a: \", type(6/3))\n",
+    "print(\"And that's a \",type(6//3))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -666,16 +716,6 @@
     "matrix"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# list comprehension\n",
-    "[i**2 for i in range(10)]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

Tasks - Pandas, Numpy, Statsmodels, Plotting.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pandas, (Numpy), Statsmodels and Plotting\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#####################################################\n",
+    "## YOUR DATA\n",
+    "from sklearn import datasets\n",
+    "iris=datasets.load_iris()\n",
+    "\n",
+    "iris_data=iris.data\n",
+    "iris_header=iris.feature_names\n",
+    "iris_group=iris.target\n",
+    "iris_target_names=iris.target_names\n",
+    "####################################################"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**1. Convert data to a Pandas dataframe!**\n",
+    "\n",
+    "These columns must be in the dataframe:\n",
+    "\n",
+    "sepal_length | sepal_width | petal_length | petal_width | target_name\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**2. Plot the data to get a better feel for it. (Scattermatrix would be a good idea)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**3. Now plot the data _grouped_ by target_name.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**4. Create a multidimensional linear model that tries to guess the petal width depending on petal_length, sepal_width, sepal_length and check how well it fits!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**5. Create Numpy array from the setosa sepal and petal values only!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "@webio": {
+   "lastCommId": null,
+   "lastKernelId": null
+  },
+  "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}