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ananya868/Numpy axis.ipynb

Created July 27, 2021 08:18
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Numpy asix and sorting
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Numpy axis.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "4f87dff3",
"metadata": {},
"source": [
"# #This Is the notebook for Axis, Sorting and other useful functions in numpy "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3287d4ad",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "27bb820c",
"metadata": {},
"outputs": [],
"source": [
"a1 = np.array([[1,2,3],\n",
" [4,5,6]])"
]
},
{
"cell_type": "markdown",
"id": "09d4c6db",
"metadata": {},
"source": [
"for axis=1 calculates the value from the rows and for asix=0 calculates the value from the columns "
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8211cfb2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([5, 7, 9])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a1.sum(axis=0)"
]
},
{
"cell_type": "markdown",
"id": "c3e7952e",
"metadata": {},
"source": [
"To calculate the dot product do two arrays:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a2b8dde8",
"metadata": {},
"outputs": [],
"source": [
"b1 = np.array([[7,8,9],\n",
" [2,8,0]])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e4ed1cfe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 50, 18],\n",
" [122, 48]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.dot(a1, b1.transpose())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "273607cf",
"metadata": {},
"outputs": [
{
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},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(np)"
]
},
{
"cell_type": "markdown",
"id": "d7d2c98b",
"metadata": {},
"source": [
"Lets do the cross product of the two arrays:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7da2a69c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ -6, 12, -6],\n",
" [-48, 12, 22]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.cross(a1, b1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "54c7119a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[7, 8, 9],\n",
" [0, 2, 8]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sort(b1)"
]
},
{
"cell_type": "markdown",
"id": "b1333667",
"metadata": {},
"source": [
"we can also sort by column "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5750246a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2, 8, 0],\n",
" [7, 8, 9]])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sort(b1, axis=0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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