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craigsdennis
committed
Adds rounds to the study log
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‎Introduction to NumPy.ipynb

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@@ -621,32 +621,277 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"study_minutes[2:6] = [80, 60, 30, 90]"
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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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"## Creation\n",
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"* Random state\n",
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"* Appending rows\n",
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"\n",
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"## Indexing\n",
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"* Shortcut (tuple)\n",
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"* Fancy Indexing"
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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": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"study_minutes = np.array([\n",
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" study_minutes,\n",
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" np.zeros(100, np.uint16)\n",
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"])"
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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": 34,
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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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"(2, 100)"
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]
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},
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"execution_count": 34,
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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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"study_minutes.shape"
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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": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Set round 2 day 1 to 60\n",
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"study_minutes[1][0] = 60"
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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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"60"
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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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"study_minutes[1, 0]"
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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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"data": {
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"text/plain": [
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"(1, 0)"
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]
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},
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"execution_count": 38,
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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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"1, 0"
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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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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([150, 60, 80, 60, 30, 90, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint16)"
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"array([132, 122, 128, 44, 136, 129, 101, 95, 50, 132, 151, 64, 104,\n",
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" 175, 117, 146, 139, 129, 133, 176, 98, 160, 179, 99, 82, 142,\n",
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" 31, 106, 117, 56, 98, 67, 121, 159, 81, 170, 31, 50, 49,\n",
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" 87, 179, 51, 116, 177, 118, 78, 171, 117, 88, 123, 102, 44,\n",
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" 79, 31, 108, 80, 59, 137, 84, 93, 155, 160, 67, 80, 166,\n",
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" 164, 70, 50, 102, 113, 47, 131, 161, 118, 82, 89, 81, 43,\n",
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" 81, 38, 119, 52, 82, 31, 159, 57, 113, 71, 121, 140, 91,\n",
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" 70, 37, 106, 64, 127, 110, 58, 93, 79], dtype=uint16)"
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]
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},
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"execution_count": 31,
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"execution_count": 39,
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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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"rand = np.random.RandomState(42)\n",
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"fake_log = rand.randint(30, 180, size=100, dtype=np.uint16)\n",
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"fake_log"
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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": 40,
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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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"[44, 50]"
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]
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},
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"execution_count": 40,
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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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"[fake_log[3], fake_log[8]]"
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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": 41,
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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([44, 50], dtype=uint16)"
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]
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},
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"execution_count": 41,
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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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"fake_log[[3, 8]]"
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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": 42,
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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([[ 44, 50],\n",
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" [132, 122]], dtype=uint16)"
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]
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},
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"execution_count": 42,
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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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"index = np.array([\n",
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" [3, 8],\n",
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" [0, 1]\n",
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"])\n",
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"fake_log[index]"
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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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"source": [
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"study_minutes = np.append(study_minutes, [fake_log], axis=0)"
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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": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"study_minutes[1, 1] = 360"
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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": 47,
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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([[150, 60, 80, 60, 30, 90, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 60, 360, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [132, 122, 128, 44, 136, 129, 101, 95, 50, 132, 151, 64, 104,\n",
863+
" 175, 117, 146, 139, 129, 133, 176, 98, 160, 179, 99, 82, 142,\n",
864+
" 31, 106, 117, 56, 98, 67, 121, 159, 81, 170, 31, 50, 49,\n",
865+
" 87, 179, 51, 116, 177, 118, 78, 171, 117, 88, 123, 102, 44,\n",
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" 79, 31, 108, 80, 59, 137, 84, 93, 155, 160, 67, 80, 166,\n",
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" 164, 70, 50, 102, 113, 47, 131, 161, 118, 82, 89, 81, 43,\n",
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" 81, 38, 119, 52, 82, 31, 159, 57, 113, 71, 121, 140, 91,\n",
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" 70, 37, 106, 64, 127, 110, 58, 93, 79]], dtype=uint16)"
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]
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},
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"execution_count": 47,
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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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"study_minutes[2:6] = [80, 60, 30, 90]\n",
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"study_minutes"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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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"metadata": {},
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"outputs": [],
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"source": []
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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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