add array-joining

Author: rsokl

Python/Module3_IntroducingNumpy/AccessingDataAlongMultipleDimensions.ipynb

@@ -42,7 +42,7 @@
     "                  [[4, 5],\n",
     "                   [6, 7]]])\n",
     "\n",
-    "# sheet-0, both rows, flip order of columns\n",
+    "# get: sheet-0, both rows, flip order of columns\n",
     ">>> x[0, :, ::-1]\n",
     "array([[1, 0],\n",
     "       [3, 2]])\n",

Python/Module3_IntroducingNumpy/FunctionsForCreatingNumpyArrays.ipynb

@@ -5,7 +5,35 @@
    "metadata": {},
    "source": [
     "# Functions for Creating NumPy Arrays \n",
-    "This section presents standard methods for creating NumPy arrays of varying shapes and contents. NumPy provides a laundry list of functions for creating arrays\n",
+    "This section presents standard methods for creating NumPy arrays of varying shapes and contents. NumPy provides a laundry list of functions for creating arrays:\n",
+    "\n",
+    "```python\n",
+    ">>> import numpy as np\n",
+    "\n",
+    "# creating an array from a Python-sequence\n",
+    ">>> np.array([i**2 for i in range(5)])\n",
+    "array([ 0,  1,  4,  9, 16])\n",
+    "\n",
+    "# creating an array filled with ones\n",
+    ">>> np.ones((2, 4))\n",
+    "array([[ 1.,  1.,  1.,  1.],\n",
+    "       [ 1.,  1.,  1.,  1.]])\n",
+    "\n",
+    "# creating an array of evenly-spaced points\n",
+    ">>> np.linspace(0, 10, 5)\n",
+    "array([  0. ,   2.5,   5. ,   7.5,  10. ])\n",
+    "\n",
+    "# creating an array by sampling 10 numbers \n",
+    "# randomly from a mean-1, std-dev-5 normal\n",
+    "# distribution\n",
+    ">>> np.random.normal(1, 5, 10)\n",
+    "array([ 2.549537  ,  2.75144951,  0.60031823,  3.75185732,  4.65543858,\n",
+    "        0.55779525,  1.15574987, -1.98461337,  5.39771083, -7.81395192])\n",
+    "\n",
+    "# creating an array of a specified datatype\n",
+    ">>> np.array([1.5, 3.20, 5.78], dtype=int)\n",
+    "array([1, 3, 5])\n",
+    "```\n",
     "\n",
     "## Creating Arrays from Python Sequences\n",
     "You can create an array from a Python `list` or `tuple` by using NumPy's `array` function. NumPy will interpret the structure of the data it receives to determine the dimensionality and shape of the array. For example, a single list of numbers will be used to create a 1-dimensional array: \n",
@@ -20,7 +48,7 @@
     "Nested lists/tuples will be used to constructing multidimensional arrays. For example, a \"list of equal-length lists of numbers\" will lead to a 2-dimensional array; each of the inner-lists comprises a row of the array. Thus a list of two, length-three lists will produce a (2,3)-shaped array:\n",
     " \n",
     "```python\n",
-    "# lists of lists of numbers will produce a 2D-array\n",
+    "# a list of lists of numbers will produce a 2D-array\n",
     ">>> np.array([[1., 2., 3.], [4., 5., 6.]])  # shape: (2, 3)\n",
     "array([[ 1.,  2.,  3.],\n",
     "       [ 4.,  5.,  6.]])\n",
@@ -46,7 +74,7 @@
     "\n",
     "**Warning!** \n",
     "\n",
-    "You actually *can* create an array from lists of *unequal* lengths. The resulting array is **not** an ND-array as it has no well-defined dimensionality. Instead, something called an *object-array* is produced, which does not benefit from the majoriyy of NumPy's features. This is a relatively obscure feature of the NumPy library, and should be avoided unless you really know what you're doing!\n",
+    "You actually *can* create an array from lists of *unequal* lengths. The resulting array is **not** an ND-array as it has no well-defined dimensionality. Instead, something called an *object-array* is produced, which does not benefit from the majority of NumPy's features. This is a relatively obscure feature of the NumPy library, and should be avoided unless you really know what you're doing!\n",
     "\n",
     "</div>"
    ]
@@ -163,6 +191,30 @@
     "\n",
     "Refer to [the official NumPy documentation](https://docs.scipy.org/doc/numpy/user/basics.types.html#array-types-and-conversions-between-types) for the complete list of available array datatypes."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Joining Arrays Together\n",
+    "Arrays can be concatenated together similar to Python lists and tuples, however because NumPy's arrays can be multi-dimensional, we can choose the dimension along which arrays are joined. \n",
+    "```python\n",
+    "# demonstrating methods for joining arrays \n",
+    ">>> x = np.array([1, 2, 3])\n",
+    ">>> y = np.array([-1, -2, -3])\n",
+    "\n",
+    "# stack `x` and `y` \"vertically\"\n",
+    ">>> np.vstack([x, y])\n",
+    "array([[ 1,  2,  3],\n",
+    "       [-1, -2, -3]])\n",
+    "\n",
+    "# stack `x` and `y` \"horizontally\"\n",
+    ">>> np.vstack([x, y])\n",
+    "array([ 1,  2,  3, -1, -2, -3])\n",
+    "```\n",
+    "\n",
+    "A complete listing of functions for joining arrays can be [found in the official NumPy documentation](https://docs.scipy.org/doc/numpy-1.14.0/reference/routines.array-manipulation.html#joining-arrays). There are corresponding functions for splitting an array into independent arrays."
+   ]
   }
  ],
  "metadata": {