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naviarh/pandas.ipynb

Created July 25, 2018 11:46
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "pandas.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"[View in Colaboratory](https://colab.research.google.com/gist/naviarh/c9aa9b9de36fe9ec553403a50c5b6b81/pandas.ipynb)"
]
},
{
"metadata": {
"id": "9XRMYCuLD8E5",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"#!/usr/bin/env python3\n",
"# -*- coding: utf-8 -*-"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "8Lr1I6CzECCb",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "312d1731-67e0-43e1-dfe8-22a9da5eed0e"
},
"cell_type": "code",
"source": [
"# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"!pwd"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"/content\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "t4og7bFaEM61",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 59
},
"outputId": "47072aa4-cca6-445c-d1e3-885afd307336"
},
"cell_type": "code",
"source": [
"import subprocess, os\n",
"os.uname()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"posix.uname_result(sysname='Linux', nodename='8911bc6be2c6', release='4.14.33+', version='#1 SMP Wed Jun 20 01:15:52 PDT 2018', machine='x86_64')"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"metadata": {
"id": "C6JFy_KHEXhT",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Pandas\n",
"\n",
"\n",
"---\n",
"\n",
"**Series** - ряды\n",
"\n",
"**DataFrame** - таблицы\n",
"\n",
"\n",
"---\n",
"\n"
]
},
{
"metadata": {
"id": "amyzmBPJRIA9",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"!pip install --upgrade pip\n",
"!pip3 install pytest moto\n",
"!pip3 show pytest moto | grep Name -A 1"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "f8ndhOHMENct",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"pd.test() # Тестировать будет долго"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "o4e7X6P4U49j",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Series - создание"
]
},
{
"metadata": {
"id": "tyuKOkbMWsbM",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Конструктор класса Series выглядит следующим образом:\n",
"\n",
"**pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)**\n",
"\n",
"**data** – массив, словарь или скалярное значение, на базе которого будет построен Series;\n",
"\n",
"**index** – список меток, который будет использоваться для доступа к элементам Series. Длина списка должна быть равна длине data;\n",
"\n",
"**dtype** – объект numpy.dtype, определяющий тип данных;\n",
"\n",
"**copy** – создает копию массива данных, если параметр равен True в ином случае ничего не делает."
]
},
{
"metadata": {
"id": "F63vqveTVr4k",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"outputId": "69da264e-68de-4f40-b0e2-30e2dc1372ee"
},
"cell_type": "code",
"source": [
"# Создание Series из списка Python\n",
"print(pd.Series([1, 2, 3, 4, 5]), '\\n')\n",
"print(pd.Series([1, 2, 3, 4, 5], ['a', 'b', 'c', 'd', 'e'], dtype=float))"
],
"execution_count": 91,
"outputs": [
{
"output_type": "stream",
"text": [
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
"dtype: int64 \n",
"\n",
"a 1.0\n",
"b 2.0\n",
"c 3.0\n",
"d 4.0\n",
"e 5.0\n",
"dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "KfKSA1MZaJBE",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "0b040458-d81c-46c6-ca1c-ab5478ca4317"
},
"cell_type": "code",
"source": [
"# Создание Series из ndarray массива numpy\n",
"ndarr = np.array([1, 2, 3, 4, 5])\n",
"type(ndarr)\n",
"print(pd.Series(ndarr, [.1, .2, .3, .4, .5]))"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"0.1 1\n",
"0.2 2\n",
"0.3 3\n",
"0.4 4\n",
"0.5 5\n",
"dtype: int64\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Lx7KhhY5auUJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "277d2664-51b5-4f71-da22-4ee6845f1b46"
},
"cell_type": "code",
"source": [
"# Создание Series из словаря (dict)\n",
"d = {'a0':1, 'a1':2, 'a3':3}\n",
"print(pd.Series(d))"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"a0 1\n",
"a1 2\n",
"a3 3\n",
"dtype: int64\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "eDWUbKpjbWhO",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "febe5172-9b86-4391-9b6e-bdb12e0b2cb2"
},
"cell_type": "code",
"source": [
"# Создание Series с использованием константы\n",
"print(pd.Series('word', [0,1,2,3,4]))"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"0 word\n",
"1 word\n",
"2 word\n",
"3 word\n",
"4 word\n",
"dtype: object\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "zQkYgUUHdv14",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## DataFrame - создание"
]
},
{
"metadata": {
"id": "HnMbE1VneCNM",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Конструктор класса DataFrame выглядит так:\n",
"\n",
"**class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)**\n",
"\n",
"**data** – массив ndarray, словарь (dict) или другой DataFrame;\n",
"\n",
"**index** – список меток для записей (имена строк таблицы);\n",
"\n",
"**columns** – список меток для полей (имена столбцов таблицы);\n",
"\n",
"**dtype** – объект numpy.dtype, определяющий тип данных;\n",
"\n",
"**copy** – создает копию массива данных, если параметр равен True в ином случае ничего не делает."
]
},
{
"metadata": {
"id": "KEW_aaLDguoB",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"outputId": "2ca07a9c-f80f-4bc6-86f2-29f2002e6c10"
},
"cell_type": "code",
"source": [
"# Создание DataFrame из списка словарей\n",
"d = [{\"price\": 3, \"count\":8}, {\"price\": 4, \"count\": 11}]\n",
"dd = pd.DataFrame(d, ['one','two'])\n",
"dd"
],
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" 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>count</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>one</th>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>two</th>\n",
" <td>11</td>\n",
" <td>4</td>\n",
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"</div>"
],
"text/plain": [
" count price\n",
"one 8 3\n",
"two 11 4"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
}
]
},
{
"metadata": {
"id": "mM5q7nD5iyrX",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "b04f75bc-1f58-44e7-b85f-38acea1a95a3"
},
"cell_type": "code",
"source": [
"# Создание DataFrame из словаря списков\n",
"print(pd.DataFrame({0:['ax','ay'], 1:['bx','by'], 2:['cx','cy']}), '\\n')\n",
"print(pd.DataFrame({0:np.array(['ax','ay']), 1:np.array(['bx','by']), 2:np.array(['cx','cy'])}), '\\n')\n",
"print(pd.DataFrame({0:pd.Series(['ax','ay']), 1:pd.Series(['bx','by']), 2:pd.Series(['cx','cy'])}))"
],
"execution_count": 92,
"outputs": [
{
"output_type": "stream",
"text": [
" 0 1 2\n",
"0 ax bx cx\n",
"1 ay by cy \n",
"\n",
" 0 1 2\n",
"0 ax bx cx\n",
"1 ay by cy \n",
"\n",
" 0 1 2\n",
"0 ax bx cx\n",
"1 ay by cy\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "P0wkRUmAkyC2",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "f222fab2-d34c-4cf8-cea0-7d05a1c9e93c"
},
"cell_type": "code",
"source": [
"# Создание DataFrame из двумерного массива numpy\n",
"print(pd.DataFrame(np.zeros((3,5))))"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
" 0 1 2 3 4\n",
"0 0.0 0.0 0.0 0.0 0.0\n",
"1 0.0 0.0 0.0 0.0 0.0\n",
"2 0.0 0.0 0.0 0.0 0.0\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "u7ALENH0lY87",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 179
},
"outputId": "5a6a6ef9-1489-4cdd-bc5d-df5bdd9d1bc3"
},
"cell_type": "code",
"source": [
"# Создание DataFrame из другого DataFrame\n",
"df1 = pd.DataFrame(np.zeros((2,3)))\n",
"df2 = pd.DataFrame(df1, dtype=int)\n",
"print(df1, '\\n')\n",
"df2"
],
"execution_count": 93,
"outputs": [
{
"output_type": "stream",
"text": [
" 0 1 2\n",
"0 0.0 0.0 0.0\n",
"1 0.0 0.0 0.0 \n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" 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",
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" <tbody>\n",
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"text/plain": [
" 0 1 2\n",
"0 0 0 0\n",
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"metadata": {
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},
{
"metadata": {
"id": "WsXRWxfpoHQH",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"outputId": "f9bea09b-7d24-4cd8-a6f2-401c8f248fc5"
},
"cell_type": "code",
"source": [
"# Создание Series с использованием константы\n",
"pd.DataFrame('word', [0,1], [0,1,2,3,4])"
],
"execution_count": 94,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <tr>\n",
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" <td>word</td>\n",
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"text/plain": [
" 0 1 2 3 4\n",
"0 word word word word word\n",
"1 word word word word word"
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"metadata": {
"tags": []
},
"execution_count": 94
}
]
},
{
"metadata": {
"id": "c781a4HsnUsW",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Доступ к данным в pandas"
]
},
{
"metadata": {
"id": "F1urdwLV3a0b",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"При работе со структурами Series и DataFrame из библиотеки pandas, как правило, используют два основных способа получения значений элементов.\n",
"\n",
"- по индексам (.loc[ ])\n",
"- по меткам (.iloc[ ])\n",
"\n",
".loc[ ] и .iloc[ ] - можно не писать полностью, достаточно квадратных скобок [ ]"
]
},
{
"metadata": {
"id": "_o6PbAp539Nz",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**доступ по меткам ( .loc[ ] )**\n",
"- метки в виде отдельных символов [‘a’] или чисел [5], числа используются в качестве меток, если при создании структуры не был указан список с метками;\n",
"- список меток [‘a’, ‘b’, ‘c’];\n",
"- слайс меток [‘a’:’c’];\n",
"- массив логических переменных;\n",
"- callable функция с одним аргументом."
]
},
{
"metadata": {
"id": "NSECHKZU4WGC",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"**доступ по индексам ( .iloc[ ] )**\n",
"- отдельные целые числа для доступа к элементам структуры;\n",
"- массивы целых чисел [0, 1, 2];\n",
"- слайсы целых чисел [1:4];\n",
"- массивы логических переменных;\n",
"- callable функцию с одним аргументом."
]
},
{
"metadata": {
"id": "_psdV55k-AAt",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"\n",
"\n",
"---\n",
"\n",
"\n",
"С помощью синтаксиса через точку:"
]
},
{
"metadata": {
"id": "IxYJ--L925pE",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "01042e72-3381-4eef-8db8-153c1e2d5c49"
},
"cell_type": "code",
"source": [
"print(dd, '\\n\\n')\n",
"print(dd.price, '\\n\\n')\n",
"print(dd.price.one)"
],
"execution_count": 64,
"outputs": [
{
"output_type": "stream",
"text": [
" count price\n",
"one 8 3\n",
"two 11 4 \n",
"\n",
"\n",
"one 3\n",
"two 4\n",
"Name: price, dtype: int64 \n",
"\n",
"\n",
"3\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "o3r3yexLBZXA",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Получение случайного набора из структур pandas\n",
"метод **sample()**"
]
},
{
"metadata": {
"id": "E9MWDAD1-tXl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "747ac519-a73a-4ca3-cf6a-aabcbfc7a428"
},
"cell_type": "code",
"source": [
"s = pd.Series([10, 20, 30, 40, 50], ['a', 'b', 'c', 'd', 'e'])\n",
"s"
],
"execution_count": 66,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"a 10\n",
"b 20\n",
"c 30\n",
"d 40\n",
"e 50\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
}
]
},
{
"metadata": {
"id": "YkekVhosBr3w",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "7b55c838-2c5b-4bb4-fc24-3e4283e0e34f"
},
"cell_type": "code",
"source": [
"# Выборка случайного элемента\n",
"s.sample()"
],
"execution_count": 76,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"d 40\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 76
}
]
},
{
"metadata": {
"id": "r2lYASaFBv29",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "696b5abf-1717-492d-e88b-701755fd6ee2"
},
"cell_type": "code",
"source": [
"# Выборка нескольких элементов\n",
"s.sample(n=3)"
],
"execution_count": 77,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"e 50\n",
"b 20\n",
"a 10\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 77
}
]
},
{
"metadata": {
"id": "FkcZ5DSSB5OK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"outputId": "ed91d3b6-5d3f-41fc-90c6-d202a2db457e"
},
"cell_type": "code",
"source": [
"# Доля от общего числа объектов в структуре, используя параметр\n",
"s.sample(frac=0.3)"
],
"execution_count": 78,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"c 30\n",
"a 10\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 78
}
]
},
{
"metadata": {
"id": "wJgHHG8RCR5Y",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "b2a4c588-e14d-424d-fd23-b94d1adf2de3"
},
"cell_type": "code",
"source": [
"# Мы можем передать вектор весов, длина которого должна быть равна количеству \n",
"# элементов в структуре. Сумма весов должна быть равна единице, вес, \n",
"# в данном случае, это вероятность появления элемента в выборке\n",
"w = [0.1, 0.2, 0.5, 0.1, 0.1]\n",
"s.sample(n = 3, weights=w)"
],
"execution_count": 79,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"c 30\n",
"a 10\n",
"d 40\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 79
}
]
},
{
"metadata": {
"id": "gWQOurfXCozi",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 216
},
"outputId": "b58112b1-4f05-4881-87dc-5c4a338d11a9"
},
"cell_type": "code",
"source": [
"# Для структуры DataFrame\n",
"df = pd.DataFrame({\"price\":[1, 2, 3, 5, 6], \n",
" \"count\": [10, 20, 30, 40, 50], \n",
" \"percent\": [24, 51, 71, 25, 42]})\n",
"print(d, '\\n\\n')\n",
"df.sample()"
],
"execution_count": 86,
"outputs": [
{
"output_type": "stream",
"text": [
" count percent price\n",
"0 10 24 1\n",
"1 20 51 2\n",
"2 30 71 3\n",
"3 40 25 5\n",
"4 50 42 6 \n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"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>count</th>\n",
" <th>percent</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count percent price\n",
"0 10 24 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 86
}
]
},
{
"metadata": {
"id": "sTiDZ09VDbLj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "5256ab16-4499-4c97-9fef-7a6870e06b5a"
},
"cell_type": "code",
"source": [
"# При работе с DataFrame можно указать ось\n",
"df.sample(axis=1)"
],
"execution_count": 87,
"outputs": [
{
"output_type": "execute_result",
"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>percent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>42</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" percent\n",
"0 24\n",
"1 51\n",
"2 71\n",
"3 25\n",
"4 42"
]
},
"metadata": {
"tags": []
},
"execution_count": 87
}
]
},
{
"metadata": {
"id": "z1-9qQj8Di3z",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"outputId": "9694244d-dade-409f-f79c-40d95664428d"
},
"cell_type": "code",
"source": [
"df.sample(n=2, axis=0)"
],
"execution_count": 89,
"outputs": [
{
"output_type": "execute_result",
"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>count</th>\n",
" <th>percent</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30</td>\n",
" <td>71</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count percent price\n",
"2 30 71 3\n",
"0 10 24 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 89
}
]
},
{
"metadata": {
"id": "4_ZrYND1Ekrj",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Добавление и изменение данных в pandas"
]
},
{
"metadata": {
"id": "Yw5VcWqjFqLq",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"\n",
"\n",
"---\n",
"\n",
"\n",
"**Для структуры Series**"
]
},
{
"metadata": {
"id": "YZCeCsESElEk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "4da875b6-db7f-46fd-d306-fe02016b40b7"
},
"cell_type": "code",
"source": [
"s = pd.Series([10, 20, 30, 40, 50], ['a', 'b', 'c', 'd', 'e'])\n",
"s"
],
"execution_count": 95,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"a 10\n",
"b 20\n",
"c 30\n",
"d 40\n",
"e 50\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 95
}
]
},
{
"metadata": {
"id": "vBVSJwnKEqlz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
},
"outputId": "4ba934b7-c558-40a6-c3bf-fac5a46169c3"
},
"cell_type": "code",
"source": [
"# Добавление нового элемента\n",
"s['f'] = 60\n",
"s"
],
"execution_count": 96,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"a 10\n",
"b 20\n",
"c 30\n",
"d 40\n",
"e 50\n",
"f 60\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 96
}
]
},
{
"metadata": {
"id": "VKDUEg3vEu5u",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
},
"outputId": "58f27a6e-5f05-4116-d52d-4c030948af6b"
},
"cell_type": "code",
"source": [
"# Изменение существующего элемента\n",
"s['a'] = 100\n",
"s"
],
"execution_count": 97,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"a 100\n",
"b 20\n",
"c 30\n",
"d 40\n",
"e 50\n",
"f 60\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 97
}
]
},
{
"metadata": {
"id": "x3vyYn1oFjjI",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"\n",
"\n",
"---\n",
"\n",
"\n",
"**Для структуры DataFrame**"
]
},
{
"metadata": {
"id": "PXXxsBsmFRW6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "6ccbdc66-5e9f-4612-ae3e-ea602635a098"
},
"cell_type": "code",
"source": [
"# Для структуры DataFrame\n",
"df = pd.DataFrame({\"price\":[1, 2, 3, 5, 6], \n",
" \"count\": [10, 20, 30, 40, 50], \n",
" \"percent\": [24, 51, 71, 25, 42]})\n",
"df"
],
"execution_count": 155,
"outputs": [
{
"output_type": "execute_result",
"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>count</th>\n",
" <th>percent</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20</td>\n",
" <td>51</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30</td>\n",
" <td>71</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>50</td>\n",
" <td>42</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count percent price\n",
"0 10 24 1\n",
"1 20 51 2\n",
"2 30 71 3\n",
"3 40 25 5\n",
"4 50 42 6"
]
},
"metadata": {
"tags": []
},
"execution_count": 155
}
]
},
{
"metadata": {
"id": "y_5L5lNAF6WR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "effbd99d-2424-4128-b8c4-d4ffc19e8113"
},
"cell_type": "code",
"source": [
"# Добавление (столбец)\n",
"df['value'] = [3, 14, 7, 91, 5]\n",
"df"
],
"execution_count": 156,
"outputs": [
{
"output_type": "execute_result",
"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>count</th>\n",
" <th>percent</th>\n",
" <th>price</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20</td>\n",
" <td>51</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30</td>\n",
" <td>71</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>50</td>\n",
" <td>42</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count percent price value\n",
"0 10 24 1 3\n",
"1 20 51 2 14\n",
"2 30 71 3 7\n",
"3 40 25 5 91\n",
"4 50 42 6 5"
]
},
"metadata": {
"tags": []
},
"execution_count": 156
}
]
},
{
"metadata": {
"id": "5n1YaMpoGDAi",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "0089f050-5fc3-4a7d-be9b-96effd44d5d8"
},
"cell_type": "code",
"source": [
"# Изменение существующего столбца\n",
"df['count'] = [0, 0, 0, 0, 0]\n",
"df"
],
"execution_count": 157,
"outputs": [
{
"output_type": "execute_result",
"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>count</th>\n",
" <th>percent</th>\n",
" <th>price</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>51</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>71</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count percent price value\n",
"0 0 24 1 3\n",
"1 0 51 2 14\n",
"2 0 71 3 7\n",
"3 0 25 5 91\n",
"4 0 42 6 5"
]
},
"metadata": {
"tags": []
},
"execution_count": 157
}
]
},
{
"metadata": {
"id": "VXzvzaPyGP2F",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Добавление и изменение строк\n",
"# Полуение размерностей и индексов"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "6GBnl8YXQmO1",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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