ml-lab/Notes/Numpy_Base.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "1de8b57d",
"metadata": {},
"source": [
"# Основы Numpy"
]
},
{
"cell_type": "markdown",
"id": "e442423a",
"metadata": {},
"source": [
"## Основная информация"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "54ad0b92",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"id": "6f127250",
"metadata": {},
"source": [
"ndarray, основные метка"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5df38ce7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3, 3, 3)\n",
"3\n",
"int64\n"
]
}
],
"source": [
"data = np.array(\n",
" [\n",
" [\n",
" [1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9],\n",
" ],\n",
" [\n",
" [1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9],\n",
" ],\n",
" [\n",
" [1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9],\n",
" ],\n",
" ],\n",
")\n",
"\n",
"\n",
"print(data.shape)\n",
"print(data.ndim)\n",
"print(data.dtype)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}