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Commit e153603

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‎python3_helpful_coding_patterns_&_features.ipynb

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@@ -681,13 +681,139 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Looping Over Arrays"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can directly loop over rows of an array easily, without using indices:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 2, 3],\n",
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" [4, 5, 6],\n",
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" [7, 8, 9]])"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_array_2d = np.array([1,2,3,4,5,6,7,8,9]).reshape(3, 3)\n",
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"my_array_2d"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 2 3]\n",
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"[4 5 6]\n",
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"[7 8 9]\n"
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]
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}
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],
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"source": [
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"for row in my_array_2d:\n",
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" print(row)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To loop over columns, just take the transpose using `.T` and loop over the rows of the transpose."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 4 7]\n",
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"[2 5 8]\n",
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"[3 6 9]\n"
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]
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}
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],
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"source": [
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"for col in my_array_2d.T:\n",
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" print(col)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### np.ndenumerate"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"`np.ndenumerate` allows looping over the array and enumerating indices in a concise, clean manner."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index: (0, 0), Value: 1\n",
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"Index: (0, 1), Value: 2\n",
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"Index: (0, 2), Value: 3\n",
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"Index: (1, 0), Value: 4\n",
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"Index: (1, 1), Value: 5\n",
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"Index: (1, 2), Value: 6\n",
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"Index: (2, 0), Value: 7\n",
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"Index: (2, 1), Value: 8\n",
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"Index: (2, 2), Value: 9\n"
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]
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}
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],
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"source": [
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"for index, value in np.ndenumerate(my_array_2d):\n",
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" print('Index: {}, Value: {}'.format(index, value))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
@@ -842,7 +968,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Array Rotation"
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"## Array Rolling with np.roll"
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]
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},
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{
@@ -862,7 +988,9 @@
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"\n",
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"E.g.:\n",
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"\n",
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"Rotate the array `right` by `2` positions: Expected Result = ` 4 | 5 | 1 | 2 | 3 `."
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"Rotate the array `right` by `2` positions: Expected Result = ` 4 | 5 | 1 | 2 | 3 `.\n",
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"\n",
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"For a 1-d list, it can be done manually in a one-liner easily, but in an n-dim array, rolling along the mth dim can be easily done by `np.roll`"
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]
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},
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{
@@ -914,17 +1042,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# multi-dimenisional cases\n",
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"my_array_nd = np.array([1,2,3,4,5,6,7,8,9]).reshape(3, 3)"
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"my_array_2d = np.array([1,2,3,4,5,6,7,8,9]).reshape(3, 3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
@@ -939,12 +1067,12 @@
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}
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],
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"source": [
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"print('original 2-d array: \\n', my_array_nd)"
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"print('original 2-d array: \\n', my_array_2d)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
@@ -959,16 +1087,60 @@
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}
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],
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"source": [
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"my_array_nd_rolled = np.roll(my_array_nd, 1, axis=0)\n",
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"print('rolled 2-d array: \\n', my_array_nd_rolled)"
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"my_array_2d_rolled = np.roll(my_array_2d, 1, axis=0)\n",
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"print('rolled 2-d array: \\n', my_array_2d_rolled)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Array Rotation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 2, 3],\n",
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" [4, 5, 6],\n",
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" [7, 8, 9]])"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_array_2d"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": []
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[7, 4, 1],\n",
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" [8, 5, 2],\n",
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" [9, 6, 3]])"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.rot90(my_array_2d, k=-1)"
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]
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},
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{
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"cell_type": "code",
@@ -994,7 +1166,46 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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"version": "3.6.4"
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},
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"toc": {
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": "block",
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,

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