1170 lines
22 KiB
Plaintext
1170 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b4db2677-a229-40ea-bb5b-d5064390c906",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e6e64f1-5820-4907-90f1-d314cebd8b21",
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"metadata": {},
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"source": [
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"### Creating Arrays"
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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": 2,
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"id": "7b612c05-8510-4c75-98b9-aa1dd82638ce",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 2 3 4 5]\n",
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"<class 'numpy.ndarray'>\n"
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]
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}
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],
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"source": [
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"arr = np.array([1, 2, 3, 4, 5])\n",
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"\n",
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"print(arr)\n",
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"\n",
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"print(type(arr))\n",
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"print"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee4dfe3a-d0bc-44e8-8c13-c81b669fe029",
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"metadata": {},
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"source": [
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"### Access Array Elements\n"
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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": 3,
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"id": "46ed2f18-325e-433f-b8e8-81a7fcb55a68",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1\n"
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]
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}
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],
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"source": [
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"arr = np.array([1, 2, 3, 4])\n",
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"\n",
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"print(arr[0])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "577d8ad4-1ea1-4766-95d0-eca9eaa2e139",
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"metadata": {},
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"source": [
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"### Access 2-D Arrays"
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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": 4,
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"id": "f611eb2f-1a51-4922-98cc-e0ca9c23d70c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2nd element on 1st row: 2\n"
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]
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}
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],
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"source": [
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"arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
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"\n",
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"print('2nd element on 1st row: ', arr[0, 1])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "039dd714-035a-47ff-9189-4738d0439a25",
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"metadata": {},
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"source": [
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"### Slicing arrays"
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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": 5,
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"id": "d4b7ab6b-337b-43cc-94a3-80286023cb64",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2 3 4 5]\n"
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]
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}
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],
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"source": [
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"arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
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"\n",
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"print(arr[1:5])"
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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": 6,
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"id": "9f4155d9-85be-412b-a60a-d3d501b3f37e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[5 6 7]\n"
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]
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}
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],
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"source": [
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"print(arr[4:])"
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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": 7,
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"id": "c2a24299-e29a-4ac4-a972-b6c29825974e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 2 3 4]\n"
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]
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}
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],
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"source": [
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"print(arr[:4])"
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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": 13,
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"id": "1fb6581f-012f-4e76-92f4-e8334e9d72e1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2 4]\n"
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]
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}
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],
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"source": [
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"# step -> index 1 to index 5, max 2 elements\n",
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"arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
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"\n",
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"print(arr[1:5:2])"
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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": 14,
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"id": "68dc5552-2f13-40dd-9bd6-3db60f025063",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[7 8 9]\n"
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]
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}
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],
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"source": [
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"# 2 dim array\n",
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"arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\n",
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"\n",
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"print(arr[1, 1:4])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dbeb9676-d6cf-44f2-83a5-18fc2cfc1999",
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"metadata": {},
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"source": [
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"### Data-Types "
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]
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},
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{
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"cell_type": "markdown",
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"id": "79240bea-6521-4bcb-b630-7bf61e62ce69",
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"metadata": {},
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"source": [
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"#### Python\n",
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"- strings - used to represent text data, the text is given under quote marks. e.g. \"ABCD\"\n",
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"- integer - used to represent integer numbers. e.g. -1, -2, -3\n",
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"- float - used to represent real numbers. e.g. 1.2, 42.42\n",
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"- boolean - used to represent True or False.\n",
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"- complex - used to represent complex numbers. e.g. 1.0 + 2.0j, 1.5 + 2.5j\n",
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"\n",
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"#### Numpy\n",
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"- i - integer\n",
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"- b - boolean\n",
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"- u - unsigned integer\n",
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"- f - float\n",
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"- c - complex float\n",
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"- m - timedelta\n",
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"- M - datetime\n",
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"- O - object\n",
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"- S - string\n",
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"- U - unicode string\n",
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"- V - fixed chunk of memory for other type ( void )\n"
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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": 16,
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"id": "0be7eb7c-1f64-400a-88f4-6104bb778646",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"int64\n"
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]
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}
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],
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"source": [
|
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"arr = np.array([1, 2, 3, 4])\n",
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"\n",
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"print(arr.dtype)"
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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": 17,
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"id": "ccf3a0c3-7132-4800-9dc3-2c7966ddf0e8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<U6\n"
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]
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}
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],
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"source": [
|
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"arr = np.array(['apple', 'banana', 'cherry'])\n",
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"\n",
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"print(arr.dtype)"
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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": 18,
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"id": "7875ec79-b7ed-4b3a-a2b4-139e958994dc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"[b'1' b'2' b'3' b'4']\n",
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"|S1\n"
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]
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}
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],
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"source": [
|
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"arr = np.array([1, 2, 3, 4], dtype='S')\n",
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"\n",
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"print(arr)\n",
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"print(arr.dtype)"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
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|
"id": "e8854d24-35df-44e3-bdb5-383e2baa92b2",
|
|
"metadata": {},
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|
"outputs": [
|
|
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"[1 2 3]\n",
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"int32\n"
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|
]
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}
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|
],
|
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"source": [
|
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"arr = np.array([1.1, 2.1, 3.1])\n",
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"\n",
|
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"newarr = arr.astype('i')\n",
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"\n",
|
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"print(newarr)\n",
|
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"print(newarr.dtype)"
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]
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},
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{
|
|
"cell_type": "markdown",
|
|
"id": "0c61e36d-f3fd-41a1-8bc0-bd8a7ed114c7",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Copy vs. View\n",
|
|
"The main difference between a copy and a view of an array is that the copy is a new array, and the view is just a view of the original array.\n",
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"\n",
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"The copy owns the data and any changes made to the copy will not affect original array, and any changes made to the original array will not affect the copy.\n",
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"\n",
|
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"The view does not own the data and any changes made to the view will affect the original array, and any changes made to the original array will affect the view."
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "15121d39-bed1-4508-a88e-90e931b4c39d",
|
|
"metadata": {},
|
|
"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"[42 2 3 4 5]\n",
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"[1 2 3 4 5]\n"
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]
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}
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],
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"source": [
|
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"# copy\n",
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"arr = np.array([1, 2, 3, 4, 5])\n",
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"x = arr.copy()\n",
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"arr[0] = 42\n",
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"\n",
|
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"print(arr)\n",
|
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"print(x)"
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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": 22,
|
|
"id": "3f7ee1d4-ab6c-4511-9684-858bf7507250",
|
|
"metadata": {},
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|
"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"[42 2 3 4 5]\n",
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"[42 2 3 4 5]\n"
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]
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}
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],
|
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"source": [
|
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"arr = np.array([1, 2, 3, 4, 5])\n",
|
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"x = arr.view()\n",
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"arr[0] = 42\n",
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"\n",
|
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"print(arr)\n",
|
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"print(x)"
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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": 24,
|
|
"id": "cbc20cde-62cd-4114-8c7a-22fad1d98963",
|
|
"metadata": {},
|
|
"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"[31 2 3 4 5]\n",
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"[31 2 3 4 5]\n"
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]
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}
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],
|
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"source": [
|
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"arr = np.array([1, 2, 3, 4, 5])\n",
|
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"x = arr.view()\n",
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"x[0] = 31\n",
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"\n",
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"print(arr)\n",
|
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"print(x)"
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]
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "126d79dd-6a6c-4944-91e8-99bca5d71ee4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
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"None\n",
|
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"[1 2 3 4 5]\n"
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]
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}
|
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],
|
|
"source": [
|
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"arr = np.array([1, 2, 3, 4, 5])\n",
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"\n",
|
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"x = arr.copy()\n",
|
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"y = arr.view()\n",
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"\n",
|
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"print(x.base)\n",
|
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"print(y.base)"
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]
|
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7f07f77a-4344-4e91-89bb-7b328918eeb3",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Shape of an Array"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "10a14265-ab85-4055-b18b-6e922cb5b249",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(2, 4)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
|
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"\n",
|
|
"print(arr.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "462f1f64-dda3-4fb9-b24f-5b7bc6f66c00",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[[[[1 2 3 4]]]]]\n",
|
|
"shape of array : (1, 1, 1, 1, 4)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([1, 2, 3, 4], ndmin=5)\n",
|
|
"\n",
|
|
"print(arr)\n",
|
|
"print('shape of array :', arr.shape)\n",
|
|
"# at index-4 we have value 4, so we can say that 5th ( 4 + 1 th) dimension has 4 elements."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e1c88c47-7707-49a0-a844-211419979186",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Reshape From 1-D to 2-D"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "c3d02cc8-9b48-478c-9a50-84a68a42c8a8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[ 1 2 3]\n",
|
|
" [ 4 5 6]\n",
|
|
" [ 7 8 9]\n",
|
|
" [10 11 12]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
|
|
"\n",
|
|
"newarr = arr.reshape(4, 3)\n",
|
|
"\n",
|
|
"print(newarr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e7a700dd-bc22-44d4-85c3-5931418a473a",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Flattening the arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "0189914c-8a7b-4bba-a7be-73db0f6b3b38",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[1 2 3 4 5 6]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
|
|
"\n",
|
|
"newarr = arr.reshape(-1)\n",
|
|
"\n",
|
|
"print(newarr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bc649e80-767d-4570-932a-6e074b17fdd3",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Iterating Arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "662af486-9b6b-4bd7-8ade-2e6a3ebb437d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1\n",
|
|
"2\n",
|
|
"3\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([1, 2, 3])\n",
|
|
"\n",
|
|
"for x in arr:\n",
|
|
" print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"id": "7463237c-c2e5-43a1-97ea-74c970df938a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[1 2 3]\n",
|
|
"[4 5 6]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
|
|
"\n",
|
|
"for x in arr:\n",
|
|
" print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "113a726f-9f1f-49b3-83c4-942fbd6bd981",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1\n",
|
|
"2\n",
|
|
"3\n",
|
|
"4\n",
|
|
"5\n",
|
|
"6\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 2 dim array\n",
|
|
"arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
|
|
"\n",
|
|
"for x in arr:\n",
|
|
" for y in x:\n",
|
|
" print(y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"id": "95202463-8c24-479b-98e3-1104778bbbf0",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1\n",
|
|
"2\n",
|
|
"3\n",
|
|
"4\n",
|
|
"5\n",
|
|
"6\n",
|
|
"7\n",
|
|
"8\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#nditer --> n-for loops\n",
|
|
"arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
|
|
"\n",
|
|
"for x in np.nditer(arr):\n",
|
|
" print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "3b2ca7d7-28e7-429d-9c1b-a35ae789f50f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1\n",
|
|
"3\n",
|
|
"5\n",
|
|
"7\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# different step size\n",
|
|
"arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
|
|
"# Iterate through every scalar element of the 2D array skipping 1 element:\n",
|
|
"for x in np.nditer(arr[:, ::2]):\n",
|
|
" print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "b3257129-9727-4647-84e6-ac985580fe73",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(0, 0) 1\n",
|
|
"(0, 1) 2\n",
|
|
"(0, 2) 3\n",
|
|
"(0, 3) 4\n",
|
|
"(1, 0) 5\n",
|
|
"(1, 1) 6\n",
|
|
"(1, 2) 7\n",
|
|
"(1, 3) 8\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#ndenumerate --> returns index too\n",
|
|
"arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
|
|
"\n",
|
|
"for idx, x in np.ndenumerate(arr):\n",
|
|
" print(idx, x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "299e02df-ed56-4c1c-a4c8-779a0a71f97a",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Joining Arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "72b19370-5292-4987-b79e-bd8772683bea",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[1 2 3 4 5 6]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr1 = np.array([1, 2, 3])\n",
|
|
"\n",
|
|
"arr2 = np.array([4, 5, 6])\n",
|
|
"\n",
|
|
"arr = np.concatenate((arr1, arr2))\n",
|
|
"\n",
|
|
"print(arr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "bb39d7aa-a1ca-41a4-899e-193610ea4bd8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[1 2 5 6]\n",
|
|
" [3 4 7 8]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#2 dim array\n",
|
|
"arr1 = np.array([[1, 2], [3, 4]])\n",
|
|
"\n",
|
|
"arr2 = np.array([[5, 6], [7, 8]])\n",
|
|
"\n",
|
|
"arr = np.concatenate((arr1, arr2), axis=1)\n",
|
|
"\n",
|
|
"print(arr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "177a8c9a-2cf8-40a9-bed0-39436910c45b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[1 4]\n",
|
|
" [2 5]\n",
|
|
" [3 6]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# stack\n",
|
|
"arr1 = np.array([1, 2, 3])\n",
|
|
"\n",
|
|
"arr2 = np.array([4, 5, 6])\n",
|
|
"\n",
|
|
"arr = np.stack((arr1, arr2), axis=1)\n",
|
|
"\n",
|
|
"print(arr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"id": "d3519009-19ac-4175-913e-8c5d47b1896f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[1 2 3 4 5 6]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Stacking Along Rows\n",
|
|
"arr1 = np.array([1, 2, 3])\n",
|
|
"\n",
|
|
"arr2 = np.array([4, 5, 6])\n",
|
|
"\n",
|
|
"arr = np.hstack((arr1, arr2))\n",
|
|
"\n",
|
|
"print(arr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "d3a2b831-6e4d-4caa-b270-372958b70d49",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[1 2 3]\n",
|
|
" [4 5 6]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Stacking Along Columns\n",
|
|
"arr1 = np.array([1, 2, 3])\n",
|
|
"\n",
|
|
"arr2 = np.array([4, 5, 6])\n",
|
|
"\n",
|
|
"arr = np.vstack((arr1, arr2))\n",
|
|
"\n",
|
|
"print(arr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "64a57420-2507-4abd-b573-d9976d2bb385",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[[1 4]\n",
|
|
" [2 5]\n",
|
|
" [3 6]]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Stacking Along Height (depth)\n",
|
|
"arr1 = np.array([1, 2, 3])\n",
|
|
"\n",
|
|
"arr2 = np.array([4, 5, 6])\n",
|
|
"\n",
|
|
"arr = np.dstack((arr1, arr2))\n",
|
|
"\n",
|
|
"print(arr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7b226ad5-addb-4ee1-93d4-748df1de2e85",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Splitting Arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"id": "f357a0b4-7dbf-4cd0-b586-23e06c882f6d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[array([1, 2]), array([3, 4]), array([5, 6])]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# The return value is a list containing three arrays.\n",
|
|
"arr = np.array([1, 2, 3, 4, 5, 6])\n",
|
|
"\n",
|
|
"newarr = np.array_split(arr, 3)\n",
|
|
"\n",
|
|
"print(newarr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"id": "437b8f01-1309-48c5-a4ef-13dd26f73025",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[array([[1, 2],\n",
|
|
" [3, 4]]), array([[5, 6],\n",
|
|
" [7, 8]]), array([[ 9, 10],\n",
|
|
" [11, 12]])]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# splitting 2dim arrays\n",
|
|
"arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])\n",
|
|
"\n",
|
|
"newarr = np.array_split(arr, 3)\n",
|
|
"\n",
|
|
"print(newarr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "54416dbe-202c-461c-9417-79043e44207f",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Searching Arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"id": "c3dc56a2-9928-40b1-9e89-0f61e22efc11",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(array([3, 5, 6]),)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([1, 2, 3, 4, 5, 4, 4])\n",
|
|
"\n",
|
|
"x = np.where(arr == 4)\n",
|
|
"\n",
|
|
"print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"id": "b07a60bd-ddb1-45b8-bae8-452b4140133b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(array([1, 3, 5, 7]),)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Find the indexes where the values are even (modulo)\n",
|
|
"arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
|
|
"\n",
|
|
"x = np.where(arr%2 == 0)\n",
|
|
"\n",
|
|
"print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0400ef9f-09f4-4e8d-b183-56e401fa8a5a",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Sorting Arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"id": "ad6ff4df-0260-46c6-ace8-f716baaa81ff",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[0 1 2 3]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([3, 2, 0, 1])\n",
|
|
"sorted_array = np.sort(arr)\n",
|
|
"print(sorted_array)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"id": "2f51f21d-4ddc-4677-907f-07038c4e3a80",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[3 2 1 0]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# reverse\n",
|
|
"reverse_array = sorted_array[::-1]\n",
|
|
"print(reverse_array)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"id": "7264d51f-6d8b-4571-91ef-d8550b539d86",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['apple' 'banana' 'cherry']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array(['banana', 'cherry', 'apple'])\n",
|
|
"\n",
|
|
"print(np.sort(arr))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 60,
|
|
"id": "056524c9-a534-4b82-bb7f-4772efb94296",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[2 3 4]\n",
|
|
" [0 1 5]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 2-dim\n",
|
|
"arr = np.array([[3, 2, 4], [5, 0, 1]])\n",
|
|
"\n",
|
|
"print(np.sort(arr))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f5a6b9f1-6a39-4f3b-8a98-a45bb6a803fb",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Filter Arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 64,
|
|
"id": "101256a3-98a7-4ee8-bb6d-5386a42043f8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[False False True True]\n",
|
|
"[43 44]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([41, 42, 43, 44])\n",
|
|
"\n",
|
|
"filter_arr = arr > 42\n",
|
|
"\n",
|
|
"newarr = arr[filter_arr]\n",
|
|
"\n",
|
|
"print(filter_arr)\n",
|
|
"print(newarr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 65,
|
|
"id": "79b7bfaf-1056-42f8-948e-bb6c2811a25a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[False True False True False True False]\n",
|
|
"[2 4 6]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
|
|
"\n",
|
|
"filter_arr = arr % 2 == 0\n",
|
|
"\n",
|
|
"newarr = arr[filter_arr]\n",
|
|
"\n",
|
|
"print(filter_arr)\n",
|
|
"print(newarr)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d8fcf398-28f3-4380-8aab-7692578ab277",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.11.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|