{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Загрузка данных в DataFrame \"Список форбс\"\n", "\n", "О рейтинге\n", "The World's Billionaires (\"Миллиардеры мира\") - ежегодный рейтинг самых богатых миллиардеров мира, составляемый и публикуемый в марте американским деловым журналом Forbes. Общее состояние каждого человека, включенного в список, оценивается в долларах США на основе его документально подтвержденных активов, а также с учетом долгов и других факторов. Этот рейтинг представляет собой список самых богатых людей, зарегистрированных по документам, за исключением тех, чье благосостояние не может быть полностью установлено.\n", "\n", "Методология Forbes\n", "Каждый год Forbes нанимает команду из более чем 50 репортеров из разных стран, чтобы отслеживать деятельность самых богатых людей мира, а иногда и групп или семей, которые делятся богатством. Предварительные опросы рассылаются тем, кто может попасть в список. \n", "\n", "По данным Forbes, они получили ответы трех типов: одни люди пытаются преувеличить свое богатство, другие сотрудничают, но не раскрывают деталей, а третьи отказываются отвечать на любые вопросы. \n", "Затем тщательно изучаются деловые сделки и оценивается стоимость ценных активов – земли, домов, транспортных средств, произведений искусства и т.д. – сделаны.\n", "\n", "Для проверки данных и уточнения оценки активов отдельных лиц проводятся собеседования. И, наконец, котировки акций, обращающихся на бирже, оцениваются по рыночным ценам примерно за месяц до публикации. \n", "Частные компании оцениваются в соответствии с преобладающим соотношением цены к продажам или цены к прибыли. \n", "Известные долги вычитаются из активов, чтобы получить окончательную оценку предполагаемого состояния человека в долларах США. \n", "Поскольку цены на акции быстро колеблются, истинное состояние человека и его рейтинг на момент публикации могут отличаться от того, в котором он находился на момент составления списка." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 2600 entries, 1 to 2578\n", "Data columns (total 6 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Name 2600 non-null object \n", " 1 Networth 2600 non-null float64\n", " 2 Age 2600 non-null int64 \n", " 3 Country 2600 non-null object \n", " 4 Source 2600 non-null object \n", " 5 Industry 2600 non-null object \n", "dtypes: float64(1), int64(1), object(4)\n", "memory usage: 142.2+ KB\n", "(2600, 6)\n" ] }, { "data": { "text/html": [ "
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NameNetworthAgeCountrySourceIndustry
Rank
1Elon Musk219.050United StatesTesla, SpaceXAutomotive
2Jeff Bezos171.058United StatesAmazonTechnology
3Bernard Arnault & family158.073FranceLVMHFashion & Retail
4Bill Gates129.066United StatesMicrosoftTechnology
5Warren Buffett118.091United StatesBerkshire HathawayFinance & Investments
\n", "
" ], "text/plain": [ " Name Networth Age Country \\\n", "Rank \n", "1 Elon Musk 219.0 50 United States \n", "2 Jeff Bezos 171.0 58 United States \n", "3 Bernard Arnault & family 158.0 73 France \n", "4 Bill Gates 129.0 66 United States \n", "5 Warren Buffett 118.0 91 United States \n", "\n", " Source Industry \n", "Rank \n", "1 Tesla, SpaceX Automotive \n", "2 Amazon Technology \n", "3 LVMH Fashion & Retail \n", "4 Microsoft Technology \n", "5 Berkshire Hathaway Finance & Investments " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv(\"..//..//static//csv//Forbes Billionaires.csv\", index_col=\"Rank\")\n", "\n", "df.info()\n", "\n", "print(df.shape)\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Проблемная область\n", "

\n", "Анализ данных из списка миллиардеров Forbes позволяет не только понять текущее состояние богатства в мире, но и выявить более глубокие тенденции и паттерны, которые могут помочь в принятии бизнес-решений, понимании экономических процессов и определении направлений для дальнейших исследований. Эти данные могут быть основой для многочисленных статей, отчетов и аналитических исследований, что делает их ценными для широкого круга специалистов в различных областях." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Анализ содержимого\n", "

\n", "\n", "1. Объектами наблюдения являются миллиардеры.\n", "2. В качестве атбирутов вредставлены: имя, величина богатства, возраст, страна, источник, индустрия\n", "3. Связей между объектами нет" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Бизнес-цели\n", "

\n", "1. Сравнив свой бизнес с другими успешными компаниями, основанными миллиардерами, можно извлечь ценные уроки о сильных сторонами и недостатках своей компании.\n", "2. Анализируя, в каких секторах работают миллиардеры и какие компании они развивают, можно выявить растущие рынки и индустрии, в которые стоит инвестировать.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Привести примеры целей технического проекта для каждой выделенной ранее\n", "бизнес-цели. Что поступает на вход, что является целевым признаком?\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Получение сведений о пропущенных данных" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Типы пропущенных данных:\n", "- None - представление пустых данных в Python\n", "- NaN - представление пустых данных в Pandas\n", "- '' - пустая строка" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name 0\n", "Networth 0\n", "Age 0\n", "Country 0\n", "Source 0\n", "Industry 0\n", "dtype: int64\n", "\n", "Name False\n", "Networth False\n", "Age False\n", "Country False\n", "Source False\n", "Industry False\n", "dtype: bool\n", "\n" ] } ], "source": [ "# Количество пустых значений признаков\n", "print(df.isnull().sum())\n", "\n", "print()\n", "\n", "# Есть ли пустые значения признаков\n", "print(df.isnull().any())\n", "\n", "print()\n", "\n", "# Процент пустых значений признаков\n", "for i in df.columns:\n", " null_rate = df[i].isnull().sum() / len(df) * 100\n", " if null_rate > 0:\n", " print(f\"{i} процент пустых значений: %{null_rate:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Заполнение пропущенных данных\n", "\n", "https://pythonmldaily.com/posts/pandas-dataframes-search-drop-empty-values\n", "\n", "https://scales.arabpsychology.com/stats/how-to-fill-nan-values-with-median-in-pandas/" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(891, 11)\n", "Survived False\n", "Pclass False\n", "Name False\n", "Sex False\n", "Age False\n", "SibSp False\n", "Parch False\n", "Ticket False\n", "Fare False\n", "Cabin False\n", "Embarked False\n", "dtype: bool\n" ] }, { "data": { "text/html": [ "
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SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeFillNAAgeFillMedian
PassengerId
88702Montvila, Rev. Juozasmale27.00021153613.00NaNS27.027.0
88811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S19.019.0
88903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.45NaNS0.028.0
89011Behr, Mr. Karl Howellmale26.00011136930.00C148C26.026.0
89103Dooley, Mr. Patrickmale32.0003703767.75NaNQ32.032.0
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" ], "text/plain": [ " Survived Pclass Name \\\n", "PassengerId \n", "887 0 2 Montvila, Rev. Juozas \n", "888 1 1 Graham, Miss. Margaret Edith \n", "889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "890 1 1 Behr, Mr. Karl Howell \n", "891 0 3 Dooley, Mr. Patrick \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n", "PassengerId \n", "887 male 27.0 0 0 211536 13.00 NaN S \n", "888 female 19.0 0 0 112053 30.00 B42 S \n", "889 female NaN 1 2 W./C. 6607 23.45 NaN S \n", "890 male 26.0 0 0 111369 30.00 C148 C \n", "891 male 32.0 0 0 370376 7.75 NaN Q \n", "\n", " AgeFillNA AgeFillMedian \n", "PassengerId \n", "887 27.0 27.0 \n", "888 19.0 19.0 \n", "889 0.0 28.0 \n", "890 26.0 26.0 \n", "891 32.0 32.0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fillna_df = df.fillna(0)\n", "\n", "print(fillna_df.shape)\n", "\n", "print(fillna_df.isnull().any())\n", "\n", "# Замена пустых данных на 0\n", "df[\"AgeFillNA\"] = df[\"Age\"].fillna(0)\n", "\n", "# Замена пустых данных на медиану\n", "df[\"AgeFillMedian\"] = df[\"Age\"].fillna(df[\"Age\"].median())\n", "\n", "df.tail()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedAgeFillNAAgeFillMedianAgeCopy
PassengerId
88702Montvila, Rev. Juozasmale27.00021153613.00NaNS27.027.027.0
88811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S19.019.019.0
88903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.45NaNS0.028.00.0
89011Behr, Mr. Karl Howellmale26.00011136930.00C148C26.026.026.0
89103Dooley, Mr. Patrickmale32.0003703767.75NaNQ32.032.032.0
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" ], "text/plain": [ " Survived Pclass Name \\\n", "PassengerId \n", "887 0 2 Montvila, Rev. Juozas \n", "888 1 1 Graham, Miss. Margaret Edith \n", "889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "890 1 1 Behr, Mr. Karl Howell \n", "891 0 3 Dooley, Mr. Patrick \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n", "PassengerId \n", "887 male 27.0 0 0 211536 13.00 NaN S \n", "888 female 19.0 0 0 112053 30.00 B42 S \n", "889 female NaN 1 2 W./C. 6607 23.45 NaN S \n", "890 male 26.0 0 0 111369 30.00 C148 C \n", "891 male 32.0 0 0 370376 7.75 NaN Q \n", "\n", " AgeFillNA AgeFillMedian AgeCopy \n", "PassengerId \n", "887 27.0 27.0 27.0 \n", "888 19.0 19.0 19.0 \n", "889 0.0 28.0 0.0 \n", "890 26.0 26.0 26.0 \n", "891 32.0 32.0 32.0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"AgeCopy\"] = df[\"Age\"]\n", "\n", "# Замена данных сразу в DataFrame без копирования\n", "df.fillna({\"AgeCopy\": 0}, inplace=True)\n", "\n", "df.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Удаление наблюдений с пропусками" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(183, 14)\n", "Survived False\n", "Pclass False\n", "Name False\n", "Sex False\n", "Age False\n", "SibSp False\n", "Parch False\n", "Ticket False\n", "Fare False\n", "Cabin False\n", "Embarked False\n", "dtype: bool\n" ] } ], "source": [ "dropna_df = df.dropna()\n", "\n", "print(dropna_df.shape)\n", "\n", "print(fillna_df.isnull().any())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создание выборок данных\n", "\n", "Библиотека scikit-learn\n", "\n", "https://scikit-learn.org/stable/index.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Функция для создания выборок\n", "from sklearn.model_selection import train_test_split\n", "\n", "\n", "def split_stratified_into_train_val_test(\n", " df_input,\n", " stratify_colname=\"y\",\n", " frac_train=0.6,\n", " frac_val=0.15,\n", " frac_test=0.25,\n", " random_state=None,\n", "):\n", " \"\"\"\n", " Splits a Pandas dataframe into three subsets (train, val, and test)\n", " following fractional ratios provided by the user, where each subset is\n", " stratified by the values in a specific column (that is, each subset has\n", " the same relative frequency of the values in the column). It performs this\n", " splitting by running train_test_split() twice.\n", "\n", " Parameters\n", " ----------\n", " df_input : Pandas dataframe\n", " Input dataframe to be split.\n", " stratify_colname : str\n", " The name of the column that will be used for stratification. Usually\n", " this column would be for the label.\n", " frac_train : float\n", " frac_val : float\n", " frac_test : float\n", " The ratios with which the dataframe will be split into train, val, and\n", " test data. The values should be expressed as float fractions and should\n", " sum to 1.0.\n", " random_state : int, None, or RandomStateInstance\n", " Value to be passed to train_test_split().\n", "\n", " Returns\n", " -------\n", " df_train, df_val, df_test :\n", " Dataframes containing the three splits.\n", " \"\"\"\n", "\n", " if frac_train + frac_val + frac_test != 1.0:\n", " raise ValueError(\n", " \"fractions %f, %f, %f do not add up to 1.0\"\n", " % (frac_train, frac_val, frac_test)\n", " )\n", "\n", " if stratify_colname not in df_input.columns:\n", " raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n", "\n", " X = df_input # Contains all columns.\n", " y = df_input[\n", " [stratify_colname]\n", " ] # Dataframe of just the column on which to stratify.\n", "\n", " # Split original dataframe into train and temp dataframes.\n", " df_train, df_temp, y_train, y_temp = train_test_split(\n", " X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n", " )\n", "\n", " # Split the temp dataframe into val and test dataframes.\n", " relative_frac_test = frac_test / (frac_val + frac_test)\n", " df_val, df_test, y_val, y_test = train_test_split(\n", " df_temp,\n", " y_temp,\n", " stratify=y_temp,\n", " test_size=relative_frac_test,\n", " random_state=random_state,\n", " )\n", "\n", " assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n", "\n", " return df_train, df_val, df_test" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pclass\n", "3 491\n", "1 216\n", "2 184\n", "Name: count, dtype: int64\n", "Обучающая выборка: (534, 3)\n", "Pclass\n", "3 294\n", "1 130\n", "2 110\n", "Name: count, dtype: int64\n", "Контрольная выборка: (178, 3)\n", "Pclass\n", "3 98\n", "1 43\n", "2 37\n", "Name: count, dtype: int64\n", "Тестовая выборка: (179, 3)\n", "Pclass\n", "3 99\n", "1 43\n", "2 37\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# Вывод распределения количества наблюдений по меткам (классам)\n", "print(df.Pclass.value_counts())\n", "\n", "data = df[[\"Pclass\", \"Survived\", \"AgeFillMedian\"]].copy()\n", "\n", "df_train, df_val, df_test = split_stratified_into_train_val_test(\n", " data, stratify_colname=\"Pclass\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n", ")\n", "\n", "print(\"Обучающая выборка: \", df_train.shape)\n", "print(df_train.Pclass.value_counts())\n", "\n", "print(\"Контрольная выборка: \", df_val.shape)\n", "print(df_val.Pclass.value_counts())\n", "\n", "print(\"Тестовая выборка: \", df_test.shape)\n", "print(df_test.Pclass.value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Выборка с избытком (oversampling)\n", "\n", "https://www.blog.trainindata.com/oversampling-techniques-for-imbalanced-data/\n", "\n", "https://datacrayon.com/machine-learning/class-imbalance-and-oversampling/\n", "\n", "Выборка с недостатком (undersampling)\n", "\n", "https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/\n", "\n", "Библиотека imbalanced-learn\n", "\n", "https://imbalanced-learn.org/stable/" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Обучающая выборка: (534, 3)\n", "Pclass\n", "3 294\n", "1 130\n", "2 110\n", "Name: count, dtype: int64\n", "Обучающая выборка после oversampling: (864, 3)\n", "Pclass\n", "3 294\n", "2 290\n", "1 280\n", "Name: count, dtype: int64\n" ] }, { "data": { "text/html": [ "
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864 rows × 3 columns

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" ], "text/plain": [ " Pclass Survived AgeFillMedian\n", "0 3 0 28.000000\n", "1 3 0 32.000000\n", "2 3 1 28.000000\n", "3 1 0 45.000000\n", "4 3 0 7.000000\n", ".. ... ... ...\n", "859 2 0 26.887761\n", "860 2 1 0.890459\n", "861 2 0 17.481437\n", "862 2 0 17.078473\n", "863 2 1 17.220445\n", "\n", "[864 rows x 3 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from imblearn.over_sampling import ADASYN\n", "\n", "ada = ADASYN()\n", "\n", "print(\"Обучающая выборка: \", df_train.shape)\n", "print(df_train.Pclass.value_counts())\n", "\n", "X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"Pclass\"])\n", "df_train_adasyn = pd.DataFrame(X_resampled)\n", "\n", "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n", "print(df_train_adasyn.Pclass.value_counts())\n", "\n", "df_train_adasyn" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 2 }