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Commit f8b3b016 authored by Mario Chirinos Colunga's avatar Mario Chirinos Colunga 💬
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numpy

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05-NumPy&Pandas.ipynb
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...@@ -4,16 +4,557 @@ ...@@ -4,16 +4,557 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# 2.SciPy\n", "![scipy](https://cdn-images-1.medium.com/max/1600/1*Y2v3PrF1rUQRUHwOcXJznA.png)\n",
"## 2.1 NumPy" "\n",
"# 5.SciPy\n",
"SciPy es un ecosistema para computo cientifico en python, esta constriuido sobre los arreglos de NumPy. Scipy incluye herramientas como Matplotlib, pandas , SymPy y scikit-learn. \n",
"\n",
"## 5.1 NumPy\n",
"NumPy es la base para todos los paquetes de computo científico en python, provee soporte para arreglos multidimensionales y matrices, junto con una amplia coleccion de funciones matematicas de alto nivel para operar con estos arreglos.\n",
"\n",
"### 5.1.1 numpy.array \n",
"El tipo de dato mas importante de numpy es **numpy.array** sus atibutos mas importantes son:\n",
"* numpy.array.**ndim**: -numero de dimensiones del arreglo.\n",
"* numpy.array.**shape**: Un tumpla indicando el tamaño del arreglo en cada dimension.\n",
"* numpy.array.**size**: El numero total elementos en el arreglo.\n",
"* numpy.array.**dtype**: El tipo de elemenos en el arreglo e.g. numpy.int32, numpy.int16, and numpy.float64.\n",
"* numpy.array.**itemsize**: el tamaño en bytes de cada elemento del arrglo.\n",
"* numpy.array.**data**: El bloque de memoria que contiene los datos del arreglo.\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3 4]\n",
" [ 5 6 7 8 9]\n",
" [10 11 12 13 14]]\n",
"(3, 5)\n",
"2\n",
"int64\n"
]
}
],
"source": [
"import numpy as np\n",
"a = np.array([[ 0, 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8, 9],\n",
" [10, 11, 12, 13, 14]])\n",
"print(a)\n",
"print(a.shape)\n",
"print(a.ndim)\n",
"print(a.dtype)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.+0.j, 2.+0.j],\n",
" [3.+0.j, 4.+0.j]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = np.array( [ [1,2], [3,4] ], dtype=complex )\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0., 0.],\n",
" [0., 0., 0., 0.],\n",
" [0., 0., 0., 0.]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" np.zeros( (3,4) )\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 1., 1., 1.],\n",
" [1., 1., 1., 1.],\n",
" [1., 1., 1., 1.]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ones( (3,4)) "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 1., 1., 1.],\n",
" [1., 1., 1., 1.],\n",
" [1., 1., 1., 1.]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.empty( (3,4) ) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Operaciones Basicas"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": [
"a = np.array( [20,30,40,50] )\n",
"b = np.arange( 4 )"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([20, 29, 38, 47])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Suma\n",
"a-b"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 40, 60, 80, 100])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Multiplicacion Por escalar\n",
"a*2"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 4, 9])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Potencia\n",
"b**2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True, False])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Operadores Boleanos\n",
"a<50"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 30, 80, 150])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Multiplicacion por elemento\n",
"a*b"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Multiplicacion Matricial / Producto Punto\n",
"a@b == a.dot(b)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([[1, 0], [0, 1]])\n",
"b = np.array([[4, 1], [2, 2]]) "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, True],\n",
" [ True, True]])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.matmul(a, b) == a.dot(b)\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"66"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c= np.arange(12).reshape(3,4)\n",
"c.sum()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([12, 15, 18, 21])"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.sum(axis=0) # Suma por Columna"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 22, 38])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.sum(axis=1) #Suma por Fila"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Elementos, filas, columnas y subarreglos."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2, 3],\n",
" [10, 11, 12, 13],\n",
" [20, 21, 22, 23],\n",
" [30, 31, 32, 33],\n",
" [40, 41, 42, 43]])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def f(x,y):\n",
" return 10*x+y\n",
"b = np.fromfunction(f,(5,4),dtype=int)\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0,3]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 11, 12, 13])"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[1,:]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 11, 21, 31, 41])"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2],\n",
" [11, 12]])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[:2,1:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Iterando elementos"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3]\n",
"[10 11 12 13]\n",
"[20 21 22 23]\n",
"[30 31 32 33]\n",
"[40 41 42 43]\n"
]
}
],
"source": [
"for row in b:\n",
" print(row)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"10\n",
"11\n",
"12\n",
"13\n",
"20\n",
"21\n",
"22\n",
"23\n",
"30\n",
"31\n",
"32\n",
"33\n",
"40\n",
"41\n",
"42\n",
"43\n"
]
}
],
"source": [
"for element in b.flat:\n",
" print(element)"
]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
...@@ -22,6 +563,13 @@ ...@@ -22,6 +563,13 @@
"## 2.2 Pandas" "## 2.2 Pandas"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
...@@ -46,7 +594,7 @@ ...@@ -46,7 +594,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.8rc1" "version": "3.5.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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