540 lines
9.4 KiB
Plaintext
540 lines
9.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 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": 36,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 2, 3],\n",
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" [4, 5, 6]], dtype=int32)"
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]
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},
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"x = np.array([[1, 2, 3], [4, 5, 6]], np.int32)\n",
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"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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(2, 3)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x.shape"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dtype('int32')"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x.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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x[0, 1]"
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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": 37,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 4], dtype=int32)"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x[:, 0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# inserting"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"x = np.append(x, [7, 8, 9])\n",
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"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": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1, 2, 3],\n",
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" [4, 5, 6],\n",
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" [7, 8, 9]])"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"x = np.append(arr=x, values=[[7, 8, 9]], axis=0)\n",
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"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": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 2, 4, 6],\n",
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" [ 8, 10, 12]], dtype=int32)"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"x = np.add(x, x)\n",
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"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": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
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"x.tolist()"
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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": 30,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1],\n",
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" [2],\n",
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" [3],\n",
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" [4],\n",
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" [5],\n",
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" [6]])"
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]
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},
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"np.reshape(x, (-1, 1))"
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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": 32,
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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]\n",
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" [4 5 6]]\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([[1, 4],\n",
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" [2, 5],\n",
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" [3, 6]])"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"print(x)\n",
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"x.transpose()"
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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": 34,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 2, 3, 4, 5, 6])"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"x.flatten()"
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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": 39,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[4, 3],\n",
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" [5, 7]])"
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]
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},
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"execution_count": 39,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a = [4, 3, 5, 7, 6, 8]\n",
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"np.take(a, [[0, 1], [2, 3]])"
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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": 41,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([-44, 1, -55, 3, 4])"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a = np.arange(5)\n",
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"np.put(a, [0, 2], [-44, -55])\n",
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"a"
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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": 44,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([10, 1, 10, 3, 4])"
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]
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},
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a = np.arange(5)\n",
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"np.put(a, [0, 2], 10)\n",
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"a"
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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": 50,
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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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"6\n",
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"[4 5 6]\n",
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"[3 6]\n"
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]
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"print(x.max())\n",
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"print(x.max(axis=0))\n",
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"print(x.max(axis=1))"
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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": 51,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0.125, 0.754, 0.345],\n",
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" [0.453, 0.896, 0.384]])"
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]
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},
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"execution_count": 51,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[0.12541, 0.753682, 0.3453453], [0.45261364, 0.8957225, 0.3842736]])\n",
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"np.round(x, 3)"
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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": 53,
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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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"21\n",
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"[ 6 15]\n"
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]
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"print(np.sum(x))\n",
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"print(np.sum(x, axis=1))"
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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": 55,
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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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"3.5\n",
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"[2.5 3.5 4.5]\n"
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]
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"print(np.mean(x))\n",
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"print(np.mean(x, axis=0))"
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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": 56,
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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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"1.707825127659933\n",
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"[1.5 1.5 1.5]\n"
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]
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"print(np.std(x))\n",
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"print(np.std(x, axis=0))"
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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": 66,
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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]\n",
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" [5 6 7]]\n",
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"[[ 2 4 6]\n",
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" [ 8 10 12]]\n",
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"[[0.5 1. 1.5]\n",
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" [2. 2.5 3. ]]\n",
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"floor [[0 1 1]\n",
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" [2 2 3]]\n",
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"mod [[1 0 1]\n",
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" [0 1 0]]\n",
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"power [[ 1 4 9]\n",
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" [16 25 36]]\n"
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]
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"print(x + 1)\n",
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"print(x * 2)\n",
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"print(x / 2)\n",
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"print('floor', x // 2)\n",
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"print('mod', x % 2)\n",
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"print('power', pow(x, 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": 68,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 4, 16, 36],\n",
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" [ 64, 100, 144]])"
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]
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},
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"execution_count": 68,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = np.array([[1, 2, 3], [4, 5, 6]])\n",
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"x *= 2\n",
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"x **= 2\n",
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"x"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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} |