1402 lines
40 KiB
Plaintext
Executable File
1402 lines
40 KiB
Plaintext
Executable File
{
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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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"# Рабочая тетрадь № 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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd \n",
|
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler"
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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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"### 1.3.1 Задание \n",
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"\n",
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"Создать 8x8 матрицу и заполнить её в шахматном порядке нулями и \n",
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"единицами."
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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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"array([[0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
|
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" [1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
|
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" [0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
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" [1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
|
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" [0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
|
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" [1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
|
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" [0, 1, 0, 1, 0, 1, 0, 1, 0],\n",
|
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" [1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
|
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" [0, 1, 0, 1, 0, 1, 0, 1, 0]])"
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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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"np.array([[(x + y) % 2 for x in range(9)] for y in range(9)])"
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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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"### 1.3.2 Задание\n",
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"\n",
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"Создать 5x5 матрицу со значениями в строках от 0 до 4. Для создания \n",
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"необходимо использовать функцию arrange. "
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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": 8,
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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, 1, 2, 3, 4],\n",
|
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" [0, 1, 2, 3, 4],\n",
|
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" [0, 1, 2, 3, 4],\n",
|
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" [0, 1, 2, 3, 4],\n",
|
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" [0, 1, 2, 3, 4]])"
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]
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},
|
||
"execution_count": 8,
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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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"np.array([np.arange(0, 5) for _ in range(5)])"
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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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"### 1.3.3 Задание\n",
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"\n",
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"Создать массив 3x3x3 со случайными значениями. "
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]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
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||
"metadata": {},
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||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[[0.20345315, 0.43131518, 0.76966255],\n",
|
||
" [0.86176214, 0.92498056, 0.61472383],\n",
|
||
" [0.93945099, 0.1950391 , 0.23309391]],\n",
|
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"\n",
|
||
" [[0.32571424, 0.91336522, 0.95073139],\n",
|
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" [0.48109355, 0.32416483, 0.32925225],\n",
|
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" [0.56116627, 0.8999968 , 0.58189073]],\n",
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"\n",
|
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" [[0.98682298, 0.58254628, 0.25540734],\n",
|
||
" [0.96791093, 0.96124583, 0.53073238],\n",
|
||
" [0.25062485, 0.88066294, 0.99449625]]])"
|
||
]
|
||
},
|
||
"execution_count": 9,
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||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
|
||
],
|
||
"source": [
|
||
"np.random.random((3, 3, 3))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
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||
"source": [
|
||
"### 1.3.4 Задание\n",
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||
"\n",
|
||
"Создать матрицу с 0 внутри, и 1 на границах."
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]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
|
||
" [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.array([[int((x in [0, 14]) or (y in [0, 14])) for x in range(15)] for y in range(15)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.3.5 Задание\n",
|
||
"\n",
|
||
"Создайте массив и отсортируйте его по убыванию. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"arr = np.arange(0, 10)\n",
|
||
"np.sort(arr)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.3.6 Задание\n",
|
||
"\n",
|
||
"Создайте матрицу, выведите ее форму, размер и размерность."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Размерность массива по каждой оси: (2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)\n",
|
||
"Число измерений: 12\n",
|
||
"Колличество элементов: 4096\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"arr = np.random.random(np.full(12, 2))\n",
|
||
"\n",
|
||
"print(\"Размерность массива по каждой оси:\", arr.shape)\n",
|
||
"print(\"Число измерений:\", arr.ndim)\n",
|
||
"print(\"Колличество элементов:\", arr.size)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 2.3.1 Задание\n",
|
||
"\n",
|
||
"Найди евклидово расстояние между двумя Series (точками) a и b, не \n",
|
||
"используя встроенную формулу."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"1.4142135623730951\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from math import sqrt\n",
|
||
"\n",
|
||
"first_dot = pd.Series([1, 0])\n",
|
||
"second_dot = pd.Series([0, 1])\n",
|
||
"\n",
|
||
"s = 0\n",
|
||
"for dim in range(first_dot.size):\n",
|
||
" s += (first_dot.array[dim] - second_dot.array[dim]) ** 2\n",
|
||
"\n",
|
||
"print(sqrt(s))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 2.3.2 Задание \n",
|
||
"\n",
|
||
"Найдите в Интернете ссылку на любой csv файл и сформируйте из него \n",
|
||
"фрейм данных (например, коллекцию фреймов данных можно найти \n",
|
||
"здесь: https://github.com/akmand/datasets)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" 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>Airline</th>\n",
|
||
" <th>Flight</th>\n",
|
||
" <th>AirportFrom</th>\n",
|
||
" <th>AirportTo</th>\n",
|
||
" <th>DayOfWeek</th>\n",
|
||
" <th>Time</th>\n",
|
||
" <th>Length</th>\n",
|
||
" <th>Delay</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>CO</td>\n",
|
||
" <td>269</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>IAH</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>205</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>US</td>\n",
|
||
" <td>1558</td>\n",
|
||
" <td>PHX</td>\n",
|
||
" <td>CLT</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>222</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>AA</td>\n",
|
||
" <td>2400</td>\n",
|
||
" <td>LAX</td>\n",
|
||
" <td>DFW</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>165</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>AA</td>\n",
|
||
" <td>2466</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>DFW</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>195</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>AS</td>\n",
|
||
" <td>108</td>\n",
|
||
" <td>ANC</td>\n",
|
||
" <td>SEA</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>202</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>539378</th>\n",
|
||
" <td>CO</td>\n",
|
||
" <td>178</td>\n",
|
||
" <td>OGG</td>\n",
|
||
" <td>SNA</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>326</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>539379</th>\n",
|
||
" <td>FL</td>\n",
|
||
" <td>398</td>\n",
|
||
" <td>SEA</td>\n",
|
||
" <td>ATL</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>305</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>539380</th>\n",
|
||
" <td>FL</td>\n",
|
||
" <td>609</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>MKE</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>255</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>539381</th>\n",
|
||
" <td>UA</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>HNL</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>313</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>539382</th>\n",
|
||
" <td>US</td>\n",
|
||
" <td>1442</td>\n",
|
||
" <td>LAX</td>\n",
|
||
" <td>PHL</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>301</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>539383 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Airline Flight AirportFrom AirportTo DayOfWeek Time Length Delay\n",
|
||
"0 CO 269 SFO IAH 3 15 205 1\n",
|
||
"1 US 1558 PHX CLT 3 15 222 1\n",
|
||
"2 AA 2400 LAX DFW 3 20 165 1\n",
|
||
"3 AA 2466 SFO DFW 3 20 195 1\n",
|
||
"4 AS 108 ANC SEA 3 30 202 0\n",
|
||
"... ... ... ... ... ... ... ... ...\n",
|
||
"539378 CO 178 OGG SNA 5 1439 326 0\n",
|
||
"539379 FL 398 SEA ATL 5 1439 305 0\n",
|
||
"539380 FL 609 SFO MKE 5 1439 255 0\n",
|
||
"539381 UA 78 HNL SFO 5 1439 313 1\n",
|
||
"539382 US 1442 LAX PHL 5 1439 301 1\n",
|
||
"\n",
|
||
"[539383 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"url = 'https://raw.githubusercontent.com/akmand/datasets/refs/heads/main/airlines.csv'\n",
|
||
"\n",
|
||
"df = pd.read_csv(url)\n",
|
||
"\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 2.3.3 Задание\n",
|
||
"\n",
|
||
"Проделайте с получившемся из предыдущего задания фреймом данных \n",
|
||
"те же действия, что и в примерах 2.2.5-2.2.7. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### 2.2.5\n",
|
||
"\n",
|
||
"Пронализировать характеристики фрейма данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" 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>Airline</th>\n",
|
||
" <th>Flight</th>\n",
|
||
" <th>AirportFrom</th>\n",
|
||
" <th>AirportTo</th>\n",
|
||
" <th>DayOfWeek</th>\n",
|
||
" <th>Time</th>\n",
|
||
" <th>Length</th>\n",
|
||
" <th>Delay</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>CO</td>\n",
|
||
" <td>269</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>IAH</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>205</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>US</td>\n",
|
||
" <td>1558</td>\n",
|
||
" <td>PHX</td>\n",
|
||
" <td>CLT</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>222</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Airline Flight AirportFrom AirportTo DayOfWeek Time Length Delay\n",
|
||
"0 CO 269 SFO IAH 3 15 205 1\n",
|
||
"1 US 1558 PHX CLT 3 15 222 1"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head(2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" 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>Airline</th>\n",
|
||
" <th>Flight</th>\n",
|
||
" <th>AirportFrom</th>\n",
|
||
" <th>AirportTo</th>\n",
|
||
" <th>DayOfWeek</th>\n",
|
||
" <th>Time</th>\n",
|
||
" <th>Length</th>\n",
|
||
" <th>Delay</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>539380</th>\n",
|
||
" <td>FL</td>\n",
|
||
" <td>609</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>MKE</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>255</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>539381</th>\n",
|
||
" <td>UA</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>HNL</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>313</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>539382</th>\n",
|
||
" <td>US</td>\n",
|
||
" <td>1442</td>\n",
|
||
" <td>LAX</td>\n",
|
||
" <td>PHL</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1439</td>\n",
|
||
" <td>301</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Airline Flight AirportFrom AirportTo DayOfWeek Time Length Delay\n",
|
||
"539380 FL 609 SFO MKE 5 1439 255 0\n",
|
||
"539381 UA 78 HNL SFO 5 1439 313 1\n",
|
||
"539382 US 1442 LAX PHL 5 1439 301 1"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.tail(3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(539383, 8)"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" 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>Flight</th>\n",
|
||
" <th>DayOfWeek</th>\n",
|
||
" <th>Time</th>\n",
|
||
" <th>Length</th>\n",
|
||
" <th>Delay</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>539383.000000</td>\n",
|
||
" <td>539383.000000</td>\n",
|
||
" <td>539383.000000</td>\n",
|
||
" <td>539383.000000</td>\n",
|
||
" <td>539383.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>2427.928630</td>\n",
|
||
" <td>3.929668</td>\n",
|
||
" <td>802.728963</td>\n",
|
||
" <td>132.202007</td>\n",
|
||
" <td>0.445442</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>2067.429837</td>\n",
|
||
" <td>1.914664</td>\n",
|
||
" <td>278.045911</td>\n",
|
||
" <td>70.117016</td>\n",
|
||
" <td>0.497015</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>10.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>712.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>565.000000</td>\n",
|
||
" <td>81.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>1809.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>795.000000</td>\n",
|
||
" <td>115.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>3745.000000</td>\n",
|
||
" <td>5.000000</td>\n",
|
||
" <td>1035.000000</td>\n",
|
||
" <td>162.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>7814.000000</td>\n",
|
||
" <td>7.000000</td>\n",
|
||
" <td>1439.000000</td>\n",
|
||
" <td>655.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Flight DayOfWeek Time Length \\\n",
|
||
"count 539383.000000 539383.000000 539383.000000 539383.000000 \n",
|
||
"mean 2427.928630 3.929668 802.728963 132.202007 \n",
|
||
"std 2067.429837 1.914664 278.045911 70.117016 \n",
|
||
"min 1.000000 1.000000 10.000000 0.000000 \n",
|
||
"25% 712.000000 2.000000 565.000000 81.000000 \n",
|
||
"50% 1809.000000 4.000000 795.000000 115.000000 \n",
|
||
"75% 3745.000000 5.000000 1035.000000 162.000000 \n",
|
||
"max 7814.000000 7.000000 1439.000000 655.000000 \n",
|
||
"\n",
|
||
" Delay \n",
|
||
"count 539383.000000 \n",
|
||
"mean 0.445442 \n",
|
||
"std 0.497015 \n",
|
||
"min 0.000000 \n",
|
||
"25% 0.000000 \n",
|
||
"50% 0.000000 \n",
|
||
"75% 1.000000 \n",
|
||
"max 1.000000 "
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### 2.2.6\n",
|
||
"\n",
|
||
"Выберите индивидуальные данные или срезы фрейма данных."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" 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>Airline</th>\n",
|
||
" <th>Flight</th>\n",
|
||
" <th>AirportFrom</th>\n",
|
||
" <th>AirportTo</th>\n",
|
||
" <th>DayOfWeek</th>\n",
|
||
" <th>Time</th>\n",
|
||
" <th>Length</th>\n",
|
||
" <th>Delay</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>US</td>\n",
|
||
" <td>1558</td>\n",
|
||
" <td>PHX</td>\n",
|
||
" <td>CLT</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>222</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>AA</td>\n",
|
||
" <td>2400</td>\n",
|
||
" <td>LAX</td>\n",
|
||
" <td>DFW</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>165</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>AA</td>\n",
|
||
" <td>2466</td>\n",
|
||
" <td>SFO</td>\n",
|
||
" <td>DFW</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>195</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Airline Flight AirportFrom AirportTo DayOfWeek Time Length Delay\n",
|
||
"1 US 1558 PHX CLT 3 15 222 1\n",
|
||
"2 AA 2400 LAX DFW 3 20 165 1\n",
|
||
"3 AA 2466 SFO DFW 3 20 195 1"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.iloc[1:4]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### 2.2.7\n",
|
||
"\n",
|
||
"Требуется отобрать строки фрейма данных на основе некоторого \n",
|
||
"условия."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" 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>Airline</th>\n",
|
||
" <th>Flight</th>\n",
|
||
" <th>AirportFrom</th>\n",
|
||
" <th>AirportTo</th>\n",
|
||
" <th>DayOfWeek</th>\n",
|
||
" <th>Time</th>\n",
|
||
" <th>Length</th>\n",
|
||
" <th>Delay</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>US</td>\n",
|
||
" <td>1558</td>\n",
|
||
" <td>PHX</td>\n",
|
||
" <td>CLT</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>222</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>US</td>\n",
|
||
" <td>498</td>\n",
|
||
" <td>DEN</td>\n",
|
||
" <td>CLT</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>55</td>\n",
|
||
" <td>179</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Airline Flight AirportFrom AirportTo DayOfWeek Time Length Delay\n",
|
||
"1 US 1558 PHX CLT 3 15 222 1\n",
|
||
"15 US 498 DEN CLT 3 55 179 0"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[df['Airline'] == 'US'].head(2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 3.3.2 Задание\n",
|
||
"\n",
|
||
"Загрузить фрейм данных по ссылке: \n",
|
||
"https://raw.githubusercontent.com/akmand/datasets/master/iris.csv. \n",
|
||
"Необходимо выполнить нормализацию первого числового признака \n",
|
||
"(sepal_length_cm) с использованием минимаксного преобразования, а \n",
|
||
"второго (sepal_width_cm) с задействованием z-масштабирования. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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|
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|
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|
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" .dataframe thead th {\n",
|
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|
||
" }\n",
|
||
"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>sepal_length_cm</th>\n",
|
||
" <th>sepal_width_cm</th>\n",
|
||
" <th>petal_length_cm</th>\n",
|
||
" <th>petal_width_cm</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",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>145</th>\n",
|
||
" <td>6.7</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>5.2</td>\n",
|
||
" <td>2.3</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>146</th>\n",
|
||
" <td>6.3</td>\n",
|
||
" <td>2.5</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>1.9</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>147</th>\n",
|
||
" <td>6.5</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>5.2</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>148</th>\n",
|
||
" <td>6.2</td>\n",
|
||
" <td>3.4</td>\n",
|
||
" <td>5.4</td>\n",
|
||
" <td>2.3</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>149</th>\n",
|
||
" <td>5.9</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>5.1</td>\n",
|
||
" <td>1.8</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>150 rows × 5 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" sepal_length_cm sepal_width_cm petal_length_cm petal_width_cm \\\n",
|
||
"0 5.1 3.5 1.4 0.2 \n",
|
||
"1 4.9 3.0 1.4 0.2 \n",
|
||
"2 4.7 3.2 1.3 0.2 \n",
|
||
"3 4.6 3.1 1.5 0.2 \n",
|
||
"4 5.0 3.6 1.4 0.2 \n",
|
||
".. ... ... ... ... \n",
|
||
"145 6.7 3.0 5.2 2.3 \n",
|
||
"146 6.3 2.5 5.0 1.9 \n",
|
||
"147 6.5 3.0 5.2 2.0 \n",
|
||
"148 6.2 3.4 5.4 2.3 \n",
|
||
"149 5.9 3.0 5.1 1.8 \n",
|
||
"\n",
|
||
" species \n",
|
||
"0 setosa \n",
|
||
"1 setosa \n",
|
||
"2 setosa \n",
|
||
"3 setosa \n",
|
||
"4 setosa \n",
|
||
".. ... \n",
|
||
"145 virginica \n",
|
||
"146 virginica \n",
|
||
"147 virginica \n",
|
||
"148 virginica \n",
|
||
"149 virginica \n",
|
||
"\n",
|
||
"[150 rows x 5 columns]"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"url = 'https://raw.githubusercontent.com/akmand/datasets/master/iris.csv'\n",
|
||
"\n",
|
||
"iris_df = pd.read_csv(url)\n",
|
||
"\n",
|
||
"iris_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"length_feature = np.array(iris_df['sepal_length_cm']).reshape(-1,1)\n",
|
||
"\n",
|
||
"minmax_scale = MinMaxScaler(feature_range = (0, 1))\n",
|
||
"scaled_sepal_length = minmax_scale.fit_transform(length_feature)\n",
|
||
"iris_df['sepal_length_cm'] = scaled_sepal_length;"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"width_feature = np.array(iris_df['sepal_width_cm']).reshape(-1,1)\n",
|
||
"\n",
|
||
"z_scale = StandardScaler()\n",
|
||
"scaled_sepal_width = z_scale.fit_transform(width_feature)\n",
|
||
"iris_df['sepal_width_cm'] = scaled_sepal_width"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
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"<style scoped>\n",
|
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|
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|
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|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
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|
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|
||
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|
||
" .dataframe thead th {\n",
|
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|
||
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|
||
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|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>sepal_length_cm</th>\n",
|
||
" <th>sepal_width_cm</th>\n",
|
||
" <th>petal_length_cm</th>\n",
|
||
" <th>petal_width_cm</th>\n",
|
||
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|
||
" </tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>0.222222</td>\n",
|
||
" <td>1.032057</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>0.166667</td>\n",
|
||
" <td>-0.124958</td>\n",
|
||
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|
||
" <td>0.2</td>\n",
|
||
" <td>setosa</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>0.111111</td>\n",
|
||
" <td>0.337848</td>\n",
|
||
" <td>1.3</td>\n",
|
||
" <td>0.2</td>\n",
|
||
" <td>setosa</td>\n",
|
||
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|
||
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|
||
" <th>3</th>\n",
|
||
" <td>0.083333</td>\n",
|
||
" <td>0.106445</td>\n",
|
||
" <td>1.5</td>\n",
|
||
" <td>0.2</td>\n",
|
||
" <td>setosa</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>0.194444</td>\n",
|
||
" <td>1.263460</td>\n",
|
||
" <td>1.4</td>\n",
|
||
" <td>0.2</td>\n",
|
||
" <td>setosa</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>0.666667</td>\n",
|
||
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|
||
" <td>5.2</td>\n",
|
||
" <td>2.3</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>146</th>\n",
|
||
" <td>0.555556</td>\n",
|
||
" <td>-1.281972</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>1.9</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>147</th>\n",
|
||
" <td>0.611111</td>\n",
|
||
" <td>-0.124958</td>\n",
|
||
" <td>5.2</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>virginica</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
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|
||
" <td>0.527778</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>1.8</td>\n",
|
||
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|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>150 rows × 5 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" sepal_length_cm sepal_width_cm petal_length_cm petal_width_cm \\\n",
|
||
"0 0.222222 1.032057 1.4 0.2 \n",
|
||
"1 0.166667 -0.124958 1.4 0.2 \n",
|
||
"2 0.111111 0.337848 1.3 0.2 \n",
|
||
"3 0.083333 0.106445 1.5 0.2 \n",
|
||
"4 0.194444 1.263460 1.4 0.2 \n",
|
||
".. ... ... ... ... \n",
|
||
"145 0.666667 -0.124958 5.2 2.3 \n",
|
||
"146 0.555556 -1.281972 5.0 1.9 \n",
|
||
"147 0.611111 -0.124958 5.2 2.0 \n",
|
||
"148 0.527778 0.800654 5.4 2.3 \n",
|
||
"149 0.444444 -0.124958 5.1 1.8 \n",
|
||
"\n",
|
||
" species \n",
|
||
"0 setosa \n",
|
||
"1 setosa \n",
|
||
"2 setosa \n",
|
||
"3 setosa \n",
|
||
"4 setosa \n",
|
||
".. ... \n",
|
||
"145 virginica \n",
|
||
"146 virginica \n",
|
||
"147 virginica \n",
|
||
"148 virginica \n",
|
||
"149 virginica \n",
|
||
"\n",
|
||
"[150 rows x 5 columns]"
|
||
]
|
||
},
|
||
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|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
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|
||
"source": [
|
||
"iris_df"
|
||
]
|
||
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|
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|
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|
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