Merge b24dee6 into e8ff5e2

Author: himahuja

core/numpy/numpy-basics.ipynb

@@ -27,31 +27,17 @@
     "1. Inspecting an array with slicing and indexing"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "\n",
-    "| Concepts | Importance | Notes |\n",
-    "| --- | --- | --- |\n",
-    "| [Python Quickstart](../../foundations/quickstart) | Necessary | Lists, indexing, slicing, math |\n",
-    "\n",
-    "* **Time to learn**: 35 minutes\n",
-    "---"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Imports\n",
-    "A common convention you might encounter is to rename `numpy` to `np` on import to shorten it for the many times we will be calling on `numpy` for functionality."
+    "You need to import packages into your notebook to be able to use them. A common convention you might encounter is to rename `numpy` to `np` on import to shorten it for the many times we will be calling on `numpy` for functionality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,14 +50,25 @@
    "source": [
     "## Create an array of 'data'\n",
     "\n",
-    "The NumPy array represents a *contiguous* block of memory, holding entries of a given type (and hence fixed size). The entries are laid out in memory according to the shape, or list of dimension sizes. Let's start by creating an array from a list of integers and taking a look at it,"
+    "The NumPy array represents a *contiguous* block of memory, holding entries of a given type (and hence fixed size). Let's start by creating an array from a list of integers and taking a look at it,"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a = np.array([1, 2, 3])\n",
     "a"
@@ -81,23 +78,45 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can inspect the number of dimensions our array is organized along with `ndim`, and how long each of these dimensions are with `shape`"
+    "The entries of the array are laid out in memory according to the shape, or list of dimension sizes. We can inspect the number of dimensions of our array with `ndim`, and how long each of these dimensions are with `shape`. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a.ndim"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a.shape"
    ]
@@ -106,14 +125,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So our 1-dimensional array has a shape of `3` along that dimension! Finally we can check out the underlying type of our underlying data,"
+    "So our 1-dimensional array has a shape of `3` along that dimension! The dot `(.)` notation helps you access various `properties` of the NumPy array. Finally, we can check out the underlying type of our underlying data,"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int64')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a.dtype"
    ]
@@ -127,37 +157,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 2., 3.],\n",
+       "       [4., 5., 6.]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])\n",
     "a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a.ndim"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a.dtype"
    ]