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tap1012
Commit b837c115 authored by geobumac's avatar geobumac
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Merge branch 'master' into gaspar 

# Conflicts:
#	05-NumPy&Pandas.ipynb
parents 7ef96275 5d319062
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05-NumPy&Pandas.ipynb
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...@@ -15,7 +15,7 @@ ...@@ -15,7 +15,7 @@
"### 5.1.1 numpy.array \n", "### 5.1.1 numpy.array \n",
"El tipo de dato mas importante de numpy es **numpy.array** sus atibutos mas importantes son:\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.**ndim**: -numero de dimensiones del arreglo.\n",
"* numpy.array.**shape**: Un tumpla indicando el tamaño del arreglo en cada dimension.\n", "* numpy.array.**shape**: Un tupla indicando el tamaño del arreglo en cada dimension.\n",
"* numpy.array.**size**: El numero total elementos en el arreglo.\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.**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.**itemsize**: el tamaño en bytes de cada elemento del arrglo.\n",
...@@ -53,7 +53,7 @@ ...@@ -53,7 +53,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -63,7 +63,7 @@ ...@@ -63,7 +63,7 @@
" [3.+0.j, 4.+0.j]])" " [3.+0.j, 4.+0.j]])"
] ]
}, },
"execution_count": 6, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -75,7 +75,7 @@ ...@@ -75,7 +75,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -86,7 +86,7 @@ ...@@ -86,7 +86,7 @@
" [0., 0., 0., 0.]])" " [0., 0., 0., 0.]])"
] ]
}, },
"execution_count": 7, "execution_count": 3,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -98,7 +98,7 @@ ...@@ -98,7 +98,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -109,7 +109,7 @@ ...@@ -109,7 +109,7 @@
" [1., 1., 1., 1.]])" " [1., 1., 1., 1.]])"
] ]
}, },
"execution_count": 8, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -120,7 +120,7 @@ ...@@ -120,7 +120,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -131,7 +131,7 @@ ...@@ -131,7 +131,7 @@
" [1., 1., 1., 1.]])" " [1., 1., 1., 1.]])"
] ]
}, },
"execution_count": 9, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -142,7 +142,7 @@ ...@@ -142,7 +142,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -155,7 +155,7 @@ ...@@ -155,7 +155,7 @@
" [0., 0., 0., 0., 1.]])" " [0., 0., 0., 0., 1.]])"
] ]
}, },
"execution_count": 39, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -194,7 +194,7 @@ ...@@ -194,7 +194,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -203,7 +203,7 @@ ...@@ -203,7 +203,7 @@
"array([20, 29, 38, 47])" "array([20, 29, 38, 47])"
] ]
}, },
"execution_count": 11, "execution_count": 8,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -215,7 +215,7 @@ ...@@ -215,7 +215,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -224,19 +224,19 @@ ...@@ -224,19 +224,19 @@
"array([ 40, 60, 80, 100])" "array([ 40, 60, 80, 100])"
] ]
}, },
"execution_count": 12, "execution_count": 9,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# Multiplicacion Por escalar\n", "# Multiplicacion por Escalar\n",
"a*2" "a*2"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -245,7 +245,7 @@ ...@@ -245,7 +245,7 @@
"array([0, 1, 4, 9])" "array([0, 1, 4, 9])"
] ]
}, },
"execution_count": 13, "execution_count": 10,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -257,7 +257,7 @@ ...@@ -257,7 +257,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -266,7 +266,7 @@ ...@@ -266,7 +266,7 @@
"array([ True, True, True, False])" "array([ True, True, True, False])"
] ]
}, },
"execution_count": 14, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -278,7 +278,7 @@ ...@@ -278,7 +278,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -287,7 +287,7 @@ ...@@ -287,7 +287,7 @@
"array([ 0, 30, 80, 150])" "array([ 0, 30, 80, 150])"
] ]
}, },
"execution_count": 15, "execution_count": 12,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -297,135 +297,135 @@ ...@@ -297,135 +297,135 @@
"a*b" "a*b"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[**Producto punto**](https://en.wikipedia.org/wiki/Dot_product) y [**Multiplicacion Matricial**](https://en.wikipedia.org/wiki/Matrix_multiplication)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"True" "66"
] ]
}, },
"execution_count": 16, "execution_count": 13,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"a@b == a.dot(b)" "c= np.arange(12).reshape(3,4)\n",
] "c.sum()"
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"a = np.array([[1, 0], [0, 1]])\n",
"b = np.array([[4, 1], [2, 2]]) "
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[ True, True],\n", "array([12, 15, 18, 21])"
" [ True, True]])"
] ]
}, },
"execution_count": 18, "execution_count": 14,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"np.matmul(a, b) == a.dot(b)\n" "c.sum(axis=0) # Suma por Columna"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"66" "array([ 6, 22, 38])"
] ]
}, },
"execution_count": 19, "execution_count": 15,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"c= np.arange(12).reshape(3,4)\n", "c.sum(axis=1) #Suma por Fila"
"c.sum()" ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[**Producto punto**](https://en.wikipedia.org/wiki/Dot_product) y [**Multiplicacion Matricial**](https://en.wikipedia.org/wiki/Matrix_multiplication)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([12, 15, 18, 21])" "True"
] ]
}, },
"execution_count": 20, "execution_count": 16,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"c.sum(axis=0) # Suma por Columna" "a@b == a.dot(b)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"A = np.array([[1, 0], [0, 1]])\n",
"B = np.array([[4, 1], [2, 2]]) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([ 6, 22, 38])" "array([[ True, True],\n",
" [ True, True]])"
] ]
}, },
"execution_count": 21, "execution_count": 18,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"c.sum(axis=1) #Suma por Fila" "np.matmul(A, B) == A.dot(B)\n"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Elementos, filas, columnas y subarreglos." "### Elementos, filas, columnas y submatrices."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -438,7 +438,7 @@ ...@@ -438,7 +438,7 @@
" [40, 41, 42, 43]])" " [40, 41, 42, 43]])"
] ]
}, },
"execution_count": 22, "execution_count": 19,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -446,13 +446,13 @@ ...@@ -446,13 +446,13 @@
"source": [ "source": [
"def f(x,y):\n", "def f(x,y):\n",
" return 10*x+y\n", " return 10*x+y\n",
"b = np.fromfunction(f,(5,4),dtype=int)\n", "B = np.fromfunction(f,(5,4),dtype=int)\n",
"b" "B"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -461,18 +461,18 @@ ...@@ -461,18 +461,18 @@
"3" "3"
] ]
}, },
"execution_count": 23, "execution_count": 20,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"b[0,3]" "B[0,3]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -481,18 +481,18 @@ ...@@ -481,18 +481,18 @@
"array([10, 11, 12, 13])" "array([10, 11, 12, 13])"
] ]
}, },
"execution_count": 24, "execution_count": 21,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"b[1,:]" "B[1,:]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -501,18 +501,18 @@ ...@@ -501,18 +501,18 @@
"array([ 1, 11, 21, 31, 41])" "array([ 1, 11, 21, 31, 41])"
] ]
}, },
"execution_count": 25, "execution_count": 22,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"b[:,1]" "B[:,1]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -522,13 +522,13 @@ ...@@ -522,13 +522,13 @@
" [11, 12]])" " [11, 12]])"
] ]
}, },
"execution_count": 26, "execution_count": 23,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"b[:2,1:3]" "B[:2,1:3]"
] ]
}, },
{ {
...@@ -540,7 +540,7 @@ ...@@ -540,7 +540,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 24,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -556,13 +556,13 @@ ...@@ -556,13 +556,13 @@
} }
], ],
"source": [ "source": [
"for row in b:\n", "for row in B:\n",
" print(row)" " print(row)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -593,7 +593,7 @@ ...@@ -593,7 +593,7 @@
} }
], ],
"source": [ "source": [
"for element in b.flat:\n", "for element in B.flat:\n",
" print(element)" " print(element)"
] ]
}, },
...@@ -606,18 +606,18 @@ ...@@ -606,18 +606,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 26,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[8., 0., 8., 9.],\n", "array([[2., 2., 9., 4.],\n",
" [6., 1., 5., 9.],\n", " [6., 6., 3., 6.],\n",
" [9., 3., 3., 2.]])" " [7., 9., 9., 8.]])"
] ]
}, },
"execution_count": 29, "execution_count": 26,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -629,7 +629,7 @@ ...@@ -629,7 +629,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 27,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -638,7 +638,7 @@ ...@@ -638,7 +638,7 @@
"(3, 4)" "(3, 4)"
] ]
}, },
"execution_count": 30, "execution_count": 27,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -649,21 +649,21 @@ ...@@ -649,21 +649,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[8., 0.],\n", "array([[2., 2.],\n",
" [8., 9.],\n", " [9., 4.],\n",
" [6., 1.],\n", " [6., 6.],\n",
" [5., 9.],\n", " [3., 6.],\n",
" [9., 3.],\n", " [7., 9.],\n",
" [3., 2.]])" " [9., 8.]])"
] ]
}, },
"execution_count": 31, "execution_count": 28,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -674,19 +674,19 @@ ...@@ -674,19 +674,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[8., 6., 9.],\n", "array([[2., 6., 7.],\n",
" [0., 1., 3.],\n", " [2., 6., 9.],\n",
" [8., 5., 3.],\n", " [9., 3., 9.],\n",
" [9., 9., 2.]])" " [4., 6., 8.]])"
] ]
}, },
"execution_count": 32, "execution_count": 29,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -697,7 +697,7 @@ ...@@ -697,7 +697,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 30,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -709,7 +709,7 @@ ...@@ -709,7 +709,7 @@
" [ True, True, True]])" " [ True, True, True]])"
] ]
}, },
"execution_count": 33, "execution_count": 30,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -720,7 +720,7 @@ ...@@ -720,7 +720,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 31,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -729,7 +729,7 @@ ...@@ -729,7 +729,7 @@
"(4, 3)" "(4, 3)"
] ]
}, },
"execution_count": 34, "execution_count": 31,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -740,24 +740,24 @@ ...@@ -740,24 +740,24 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[8., 0., 8., 9.],\n", "array([[2., 2., 9., 4.],\n",
" [6., 1., 5., 9.],\n", " [6., 6., 3., 6.],\n",
" [9., 3., 3., 2.]])" " [7., 9., 9., 8.]])"
] ]
}, },
"execution_count": 35, "execution_count": 32,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# La dimencion con -1 se calcula automaticamente\n", "# La dimensión con -1 se calcula automaticamente\n",
"a.reshape(3,-1)" "a.reshape(3,-1)"
] ]
}, },
...@@ -772,7 +772,7 @@ ...@@ -772,7 +772,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 33,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -802,7 +802,7 @@ ...@@ -802,7 +802,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 34,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -924,7 +924,7 @@ ...@@ -924,7 +924,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -934,21 +934,21 @@ ...@@ -934,21 +934,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 51, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"a 0.994272\n", "a -0.192610\n",
"b 0.530519\n", "b 0.957230\n",
"c 1.162452\n", "c -0.917233\n",
"d -0.981436\n", "d 1.156523\n",
"e -1.283798\n", "e -0.539532\n",
"dtype: float64" "dtype: float64"
] ]
}, },
"execution_count": 51, "execution_count": 36,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -960,21 +960,21 @@ ...@@ -960,21 +960,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 46, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"0 2.042498\n", "0 -0.856313\n",
"1 -0.964070\n", "1 -0.342923\n",
"2 -0.687132\n", "2 -1.398583\n",
"3 0.623300\n", "3 0.170869\n",
"4 1.366322\n", "4 1.049196\n",
"dtype: float64" "dtype: float64"
] ]
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"execution_count": 46, "execution_count": 37,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -985,7 +985,7 @@ ...@@ -985,7 +985,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 47, "execution_count": 38,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -997,7 +997,7 @@ ...@@ -997,7 +997,7 @@
"dtype: int64" "dtype: int64"
] ]
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"execution_count": 47, "execution_count": 38,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1009,7 +1009,7 @@ ...@@ -1009,7 +1009,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 48, "execution_count": 39,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1022,7 +1022,7 @@ ...@@ -1022,7 +1022,7 @@
"dtype: float64" "dtype: float64"
] ]
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"execution_count": 48, "execution_count": 39,
"metadata": {}, "metadata": {},
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} }
...@@ -1041,16 +1041,16 @@ ...@@ -1041,16 +1041,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 53, "execution_count": 40,
"metadata": {}, "metadata": {},
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{ {
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"0.9942721192063438" "-0.19261011238315226"
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} }
...@@ -1061,19 +1061,19 @@ ...@@ -1061,19 +1061,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
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{ {
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"a 0.994272\n", "a -0.192610\n",
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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 55, "execution_count": 42,
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"a 0.994272\n", "b 0.957230\n",
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"execution_count": 55, "execution_count": 42,
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{ {
"cell_type": "code", "cell_type": "code",
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"a 1.988544\n", "a -0.385220\n",
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{ {
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{ {
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{ {
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...@@ -1194,7 +1193,7 @@ ...@@ -1194,7 +1193,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
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"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1203,7 +1202,7 @@ ...@@ -1203,7 +1202,7 @@
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
] ]
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"execution_count": 60, "execution_count": 47,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
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}, },
{ {
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{ {
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{ {
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"metadata": {}, "metadata": {},
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{ {
...@@ -1253,7 +1252,7 @@ ...@@ -1253,7 +1252,7 @@
"array([ 4, 6, 8, 10, 12, 14])" "array([ 4, 6, 8, 10, 12, 14])"
] ]
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"execution_count": 75, "execution_count": 49,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
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{ {
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{ {
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"output_type": "execute_result" "output_type": "execute_result"
} }
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"(s[:6]+s[4:])" "(s[:6]+s[4:])"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.3.2 DataFrame\n",
"DataFrame es un estructura bidimensiona etiquetada con columnas que pueden ser de diferentes tipos, es el objeto mas usado en pandas, se puede pensar en ella como un diccionario de **Series**. **DataFrame** acepta diferentes tipos de entradas como:\n",
"* Diccionarios de arreglos unidimensionales, listas dicionarios o series. \n",
"* 2-D numpy.ndarray\n",
"* Series\n",
"* Otro **DataFrame**"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"d = {'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
" 'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
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" <td>3.0</td>\n",
" <td>3.0</td>\n",
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" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
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" one two\n",
"a 1.0 1.0\n",
"b 2.0 2.0\n",
"c 3.0 3.0\n",
"d NaN 4.0"
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"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Seleccionar, Añadir y Borrar Columnas"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1.0\n",
"b 2.0\n",
"c 3.0\n",
"d NaN\n",
"Name: one, dtype: float64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"one\"]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
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" <th>one</th>\n",
" <th>two</th>\n",
" <th>three</th>\n",
" <th>flag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
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" <td>4.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>9.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
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" one two three flag\n",
"a 1.0 1.0 1.0 False\n",
"b 2.0 2.0 4.0 False\n",
"c 3.0 3.0 9.0 True\n",
"d NaN 4.0 NaN False"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['three'] = df['one'] * df['two']\n",
"df['flag'] = df['one'] > 2\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"del df['two']"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"three = df.pop('three')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
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" one flag\n",
"a 1.0 False\n",
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"c 3.0 True\n",
"d NaN False"
]
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"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
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"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>one</th>\n",
" <th>three</th>\n",
" <th>flag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <th>b</th>\n",
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" <td>4.0</td>\n",
" <td>False</td>\n",
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" <tr>\n",
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"text/plain": [
" one three flag\n",
"a 1.0 1.0 False\n",
"b 2.0 4.0 False\n",
"c 3.0 9.0 True\n",
"d NaN NaN False"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.insert(1, \"three\", three)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Indexado y Selección\n",
"\n",
"| Operación | Sintaxis | Resultado |\n",
"|:----------|:--------:|:----------|\n",
"| Selección de Columna | df[col] | Series |\n",
"| Selección de fila por etiqueta | df.loc[label] | Series |\n",
"| Selección de fila por posición | df.iloc[loc] | Series |\n",
"| Rango de filas | df[5:10] | DataFrame |\n",
"| Selección de filas por vector booleano | df[bool_vec] | DataFrame |\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"one 1\n",
"three 1\n",
"flag False\n",
"Name: a, dtype: object"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc['a']"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"one 3\n",
"three 9\n",
"flag True\n",
"Name: c, dtype: object"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[2]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>one</th>\n",
" <th>three</th>\n",
" <th>flag</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>a</th>\n",
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" <td>1.0</td>\n",
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" <tr>\n",
" <th>b</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" one three flag\n",
"a 1.0 1.0 False\n",
"b 2.0 4.0 False"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5.4 Ejercicios\n",
"Los siguentes ejercicios de realizann con los dato de *iris.csv *"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"iris = pd.read_csv('data/iris.csv')\n",
"iris.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.1 ¿Cual es el numero de observaciones en el conjunto de datos?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.2 ¿Cual es el numero de columnas en el conjunto de datos?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.3 Imprime el nombre de todas las columnas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.4 ¿Cual es el nombre de la columna 4?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.5 Selecciona las columnas, \"sepal_length\" y \"petal_length\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.6 Selecciona las filas en donde \"sepal_length\" sea mayor a 4.8"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.7 Agrega una nueva columna que sea la muliplicacion de \"petal_length\" por \"petal_width\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.4.8 Encuentra el promedio por especie de cada columna."
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
data/iris.csv 0 → 100644
View file @ b837c115
sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5.0,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3.0,1.4,0.1,setosa
4.3,3.0,1.1,0.1,setosa
5.8,4.0,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1.0,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5.0,3.0,1.6,0.2,setosa
5.0,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.0,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.1,1.5,0.1,setosa
4.4,3.0,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5.0,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5.0,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3.0,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5.0,3.3,1.4,0.2,setosa
7.0,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4.0,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1.0,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5.0,2.0,3.5,1.0,versicolor
5.9,3.0,4.2,1.5,versicolor
6.0,2.2,4.0,1.0,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3.0,4.5,1.5,versicolor
5.8,2.7,4.1,1.0,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4.0,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3.0,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3.0,5.0,1.7,versicolor
6.0,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1.0,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1.0,versicolor
5.8,2.7,3.9,1.2,versicolor
6.0,2.7,5.1,1.6,versicolor
5.4,3.0,4.5,1.5,versicolor
6.0,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3.0,4.1,1.3,versicolor
5.5,2.5,4.0,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3.0,4.6,1.4,versicolor
5.8,2.6,4.0,1.2,versicolor
5.0,2.3,3.3,1.0,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3.0,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3.0,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6.0,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3.0,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3.0,5.8,2.2,virginica
7.6,3.0,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2.0,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3.0,5.5,2.1,virginica
5.7,2.5,5.0,2.0,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3.0,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6.0,2.2,5.0,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2.0,virginica
7.7,2.8,6.7,2.0,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6.0,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3.0,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3.0,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2.0,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3.0,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6.0,3.0,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3.0,5.2,2.3,virginica
6.3,2.5,5.0,1.9,virginica
6.5,3.0,5.2,2.0,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3.0,5.1,1.8,virginica
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