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Eduardo Cueto Mendoza 2020-04-14 11:37:47 -06:00
parent d1c1f000c0
commit c3a9906f30
7 changed files with 3481 additions and 0 deletions
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158
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{
"cells": [
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"class SimpleClass():\n",
" \n",
" def __init__(self,name):\n",
" print(\"Hello\"+ ' ' +name)\n",
" \n",
" def yell(self):\n",
" print(\"YELLING!!!\")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"s = \"world\""
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(s)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello Ed\n"
]
}
],
"source": [
"x = SimpleClass('Ed')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"YELLING!!!\n"
]
}
],
"source": [
"x.yell()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"class ExtendedClass(SimpleClass):\n",
" \n",
" def __init__(self):\n",
" super().__init__('Eddie')\n",
" print(\"EXTEND\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello Eddie\n",
"EXTEND\n"
]
}
],
"source": [
"y = ExtendedClass()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"YELLING!!!\n"
]
}
],
"source": [
"y.yell()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

528
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"my_list = [1,2,3]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
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},
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"output_type": "execute_result"
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"source": [
"type(np.array(my_list))"
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},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"arr = np.array(my_list)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(0,10)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 2, 4, 6, 8, 10])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(0,11,2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0.])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros(5)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros((3,5))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 11. ])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linspace(0,11,11)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[7, 3, 7],\n",
" [8, 3, 2],\n",
" [1, 9, 0]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(0,10,(3,3))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([95, 11, 81, 70, 63, 87, 75, 9, 77, 40])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(101)\n",
"\n",
"np.random.randint(0,100,10)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 4, 63, 40, 60, 92, 64, 5, 12, 93, 40])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(0,100,10)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(101)\n",
"\n",
"arr = np.random.randint(0,100,10)\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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"array([95, 11, 81, 70, 63, 87, 75, 9, 77, 40])"
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"metadata": {},
"output_type": "execute_result"
}
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"arr"
]
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{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"95"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.max()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.min()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.argmax()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[95, 11, 81, 70, 63],\n",
" [87, 75, 9, 77, 40]])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.reshape(2,5)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"mat = np.arange(0,100).reshape(10,10)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
" [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
" [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
" [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
" [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
" [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
" [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
" [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
" [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mat"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"43"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mat[4,3]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
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},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mat[:,0]"
]
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{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mat[5,:]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2],\n",
" [10, 11, 12],\n",
" [20, 21, 22]])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mat[0:3,0:3]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"my_filter = mat > 50"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
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},
"execution_count": 39,
"metadata": {},
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"source": [
"mat[my_filter]"
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{
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"source": []
}
],
"metadata": {
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{
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{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/eddie/.pyenv/versions/3.7.3/envs/tensorflow-1.14/lib/python3.7/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
" warnings.warn(msg)\n"
]
}
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{
"data": {
"text/plain": [
"'/Users/eddie/Documents/Programming/Python/Neural_Networks_Stuff/Course'"
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],
"source": [
"pwd"
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"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('./Tensorflow-Bootcamp-master/00-Crash-Course-Basics/salaries.csv')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
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"df"
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{
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"0 50000\n",
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"2 80000\n",
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"source": [
"df['Salary']"
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" Salary Name\n",
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"df[['Salary','Name']]"
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" <td>83333.333333</td>\n",
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" <th>std</th>\n",
" <td>35118.845843</td>\n",
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" <tr>\n",
" <th>min</th>\n",
" <td>50000.000000</td>\n",
" <td>27.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>65000.000000</td>\n",
" <td>30.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
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" <th>75%</th>\n",
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" Salary Age\n",
"count 3.000000 3.000000\n",
"mean 83333.333333 35.333333\n",
"std 35118.845843 9.073772\n",
"min 50000.000000 27.000000\n",
"25% 65000.000000 30.500000\n",
"50% 80000.000000 34.000000\n",
"75% 100000.000000 39.500000\n",
"max 120000.000000 45.000000"
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},
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"metadata": {},
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"</table>\n",
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"text/plain": [
" Name Salary Age\n",
"1 Sally 120000 45\n",
"2 Alyssa 80000 27"
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},
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"metadata": {},
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{
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{
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{
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"source": [
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{
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"sklearn.preprocessing._data.MinMaxScaler"
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{
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},
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{
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"text": [
"/Users/eddie/.pyenv/versions/3.7.3/envs/tensorflow-1.14/lib/python3.7/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
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"source": [
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{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"y = df['label']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(33, 3)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(17, 3)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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