.flake8

@@ -0,0 +1,2 @@
+[flake8]
+max-line-length = 120

.gitattributes

@@ -0,0 +1 @@
+* text=crlf

.gitignore

@@ -1,18 +1,103 @@
-# ---> VisualStudioCode
-.vscode/*
-!.vscode/settings.json
-!.vscode/tasks.json
-!.vscode/launch.json
-!.vscode/extensions.json
-!.vscode/*.code-snippets
 
-# Local History for Visual Studio Code
-.history/
+# Created by https://www.toptal.com/developers/gitignore/api/python,pycharm+all
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,pycharm+all
 
-# Built Visual Studio Code Extensions
-*.vsix
+### PyCharm+all ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
 
-# ---> Python
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### PyCharm+all Patch ###
+# Ignores the whole .idea folder and all .iml files
+# See https://github.com/joeblau/gitignore.io/issues/186 and https://github.com/joeblau/gitignore.io/issues/360
+
+.idea/*
+
+# Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-249601023
+
+*.iml
+modules.xml
+.idea/misc.xml
+*.ipr
+
+# Sonarlint plugin
+.idea/sonarlint
+
+### Python ###
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -116,15 +201,7 @@ ipython_config.py
 #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
 #poetry.lock
 
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
 __pypackages__/
 
 # Celery stuff
@@ -168,9 +245,34 @@ dmypy.json
 cython_debug/
 
 # PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/python,pycharm+all
+
+# JS
+node_modules/
+
+test.csv

.vscode/extensions.json

@@ -0,0 +1,13 @@
+{
+    "recommendations": [
+        "ms-python.black-formatter",
+        "ms-python.flake8",
+        "ms-python.isort",
+        "ms-toolsai.jupyter",
+        "ms-toolsai.datawrangler",
+        "ms-python.python",
+        "donjayamanne.python-environment-manager",
+        // optional
+        "usernamehw.errorlens"
+    ]
+}

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "mai-service",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "justMyCode": true
+        }
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,38 @@
+{
+    "files.autoSave": "onFocusChange",
+    "files.exclude": {
+        "**/__pycache__": true
+    },
+    "editor.detectIndentation": false,
+    "editor.formatOnType": false,
+    "editor.formatOnPaste": true,
+    "editor.formatOnSave": true,
+    "editor.tabSize": 4,
+    "editor.insertSpaces": true,
+    "editor.codeActionsOnSave": {
+        "source.organizeImports": "explicit",
+        "source.sortImports": "explicit"
+    },
+    "editor.stickyScroll.enabled": false,
+    "diffEditor.ignoreTrimWhitespace": false,
+    "debug.showVariableTypes": true,
+    "workbench.editor.highlightModifiedTabs": true,
+    "git.suggestSmartCommit": false,
+    "git.autofetch": true,
+    "git.openRepositoryInParentFolders": "always",
+    "git.confirmSync": false,
+    "errorLens.gutterIconsEnabled": true,
+    "errorLens.messageEnabled": false,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+    },
+    "python.languageServer": "Pylance",
+    "python.analysis.typeCheckingMode": "basic",
+    "python.analysis.autoImportCompletions": true,
+    "isort.args": [
+        "--profile",
+        "black"
+    ],
+    "notebook.lineNumbers": "on",
+    "notebook.output.minimalErrorRendering": true,
+}

README.md

@@ -1,2 +0,0 @@
-# MII
-

backend/__init__.py

@@ -0,0 +1,52 @@
+import importlib
+import os
+import traceback
+
+import matplotlib
+from apiflask import APIBlueprint, APIFlask
+from flask_cors import CORS
+
+matplotlib.use("agg")
+
+cors = CORS()
+api_bp = APIBlueprint("api", __name__, url_prefix="/api/v1")
+dataset_path: str | None = None
+
+
+class Config:
+    SECRET_KEY = "secret!"
+    SEND_FILE_MAX_AGE_DEFAULT = -1
+
+
+def create_app():
+    global dataset_path
+
+    # Create and configure app
+    app = APIFlask(
+        "MAI Service",
+        title="MAI Service API",
+        docs_path="/",
+        version="1.0",
+        static_folder="",
+        template_folder="",
+    )
+    app.config.from_object(Config)
+
+    dataset_path = os.path.join(app.instance_path, "dataset")
+    os.makedirs(dataset_path, exist_ok=True)
+
+    @app.errorhandler(Exception)
+    def my_error_processor(error):
+        traceback.print_exception(error)
+        return {"message": str(error), "detail": "No details"}, 500
+
+    # Import custom REST methods
+    importlib.import_module("backend.api")
+
+    # Enable REST API
+    app.register_blueprint(api_bp)
+
+    # Enable app extensions
+    cors.init_app(app)
+
+    return app

backend/api.py

@@ -0,0 +1,57 @@
+from apiflask import FileSchema, Schema, fields
+from flask import send_file
+
+from backend import api_bp, dataset_path
+from backend.service import Service
+
+
+class FileUpload(Schema):
+    file = fields.File(required=True)
+
+
+class ColumnInfoDto(Schema):
+    datatype = fields.String()
+    items = fields.List(fields.String())
+
+
+class TableColumnDto(Schema):
+    name = fields.String()
+    datatype = fields.String()
+    items = fields.List(fields.String())
+
+
+service = Service(dataset_path)
+
+
+@api_bp.post("/dataset")
+@api_bp.input(FileUpload, location="files")
+def upload_dataset(files_data):
+    uploaded_file = files_data["file"]
+    return service.upload_dataset(uploaded_file)
+
+
+@api_bp.get("/dataset")
+def get_all_datasets():
+    return service.get_all_datasets()
+
+
+@api_bp.get("/dataset/<string:name>")
+@api_bp.output(TableColumnDto(many=True))
+def get_dataset_info(name: str):
+    return service.get_dataset_info(name)
+
+
+@api_bp.get("/dataset/<string:name>/<string:column>")
+@api_bp.output(ColumnInfoDto)
+def get_column_info(name: str, column: str):
+    return service.get_column_info(name, column)
+
+
+@api_bp.get("/dataset/draw/hist/<string:name>/<string:column>")
+@api_bp.output(
+    FileSchema(type="string", format="binary"), content_type="image/png", example=""
+)
+def get_dataset_hist(name: str, column: str):
+    data = service.get_hist(name, column)
+    data.seek(0)
+    return send_file(data, download_name=f"{name}.hist.png", mimetype="image/png")

backend/service.py

@@ -0,0 +1,59 @@
+import io
+import os
+import pathlib
+from typing import BinaryIO, Dict, List
+
+import pandas as pd
+from matplotlib.figure import Figure
+from werkzeug.datastructures import FileStorage
+from werkzeug.utils import secure_filename
+
+
+class Service:
+    def __init__(self, dataset_path: str | None) -> None:
+        if dataset_path is None:
+            raise Exception("Dataset path is not defined")
+        self.__path: str = dataset_path
+
+    def __get_dataset(self, filename: str) -> pd.DataFrame:
+        full_file_name = os.path.join(self.__path, secure_filename(filename))
+        return pd.read_csv(full_file_name)
+
+    def upload_dataset(self, file: FileStorage) -> str:
+        if file.filename is None:
+            raise Exception("Dataset upload error")
+        file_name: str = file.filename
+        full_file_name = os.path.join(self.__path, secure_filename(file_name))
+        file.save(full_file_name)
+        return file_name
+
+    def get_all_datasets(self) -> List[str]:
+        return [file.name for file in pathlib.Path(self.__path).glob("*.csv")]
+
+    def get_dataset_info(self, filename) -> List[Dict]:
+        dataset = self.__get_dataset(filename)
+        dataset_info = []
+        for column in dataset.columns:
+            items = dataset[column].astype(str)
+            column_info = {
+                "name": column,
+                "datatype": dataset.dtypes[column],
+                "items": items,
+            }
+            dataset_info.append(column_info)
+        return dataset_info
+
+    def get_column_info(self, filename, column) -> Dict:
+        dataset = self.__get_dataset(filename)
+        datatype = dataset.dtypes[column]
+        items = sorted(dataset[column].astype(str).unique())
+        return {"datatype": datatype, "items": items}
+
+    def get_hist(self, filename, column) -> BinaryIO:
+        dataset = self.__get_dataset(filename)
+        bytes = io.BytesIO()
+        plot: Figure | None = dataset.plot.hist(column=[column], bins=80).get_figure()
+        if plot is None:
+            raise Exception("Can't create hist plot")
+        plot.savefig(bytes, dpi=300, format="png")
+        return bytes

data/country.csv

@@ -0,0 +1,235 @@
+Country/Territory,Capital,Continent
+Afghanistan,Kabul,Asia
+Albania,Tirana,Europe
+Algeria,Algiers,Africa
+American Samoa,Pago Pago,Oceania
+Andorra,Andorra la Vella,Europe
+Angola,Luanda,Africa
+Anguilla,The Valley,North America
+Antigua and Barbuda,Saint Johns,North America
+Argentina,Buenos Aires,South America
+Armenia,Yerevan,Asia
+Aruba,Oranjestad,North America
+Australia,Canberra,Oceania
+Austria,Vienna,Europe
+Azerbaijan,Baku,Asia
+Bahamas,Nassau,North America
+Bahrain,Manama,Asia
+Bangladesh,Dhaka,Asia
+Barbados,Bridgetown,North America
+Belarus,Minsk,Europe
+Belgium,Brussels,Europe
+Belize,Belmopan,North America
+Benin,Porto-Novo,Africa
+Bermuda,Hamilton,North America
+Bhutan,Thimphu,Asia
+Bolivia,Sucre,South America
+Bosnia and Herzegovina,Sarajevo,Europe
+Botswana,Gaborone,Africa
+Brazil,Brasilia,South America
+British Virgin Islands,Road Town,North America
+Brunei,Bandar Seri Begawan,Asia
+Bulgaria,Sofia,Europe
+Burkina Faso,Ouagadougou,Africa
+Burundi,Bujumbura,Africa
+Cambodia,Phnom Penh,Asia
+Cameroon,Yaounde,Africa
+Canada,Ottawa,North America
+Cape Verde,Praia,Africa
+Cayman Islands,George Town,North America
+Central African Republic,Bangui,Africa
+Chad,N'Djamena,Africa
+Chile,Santiago,South America
+China,Beijing,Asia
+Colombia,Bogota,South America
+Comoros,Moroni,Africa
+Cook Islands,Avarua,Oceania
+Costa Rica,San Jos??,North America
+Croatia,Zagreb,Europe
+Cuba,Havana,North America
+Curacao,Willemstad,North America
+Cyprus,Nicosia,Europe
+Czech Republic,Prague,Europe
+Denmark,Copenhagen,Europe
+Djibouti,Djibouti,Africa
+Dominica,Roseau,North America
+Dominican Republic,Santo Domingo,North America
+DR Congo,Kinshasa,Africa
+Ecuador,Quito,South America
+Egypt,Cairo,Africa
+El Salvador,San Salvador,North America
+Equatorial Guinea,Malabo,Africa
+Eritrea,Asmara,Africa
+Estonia,Tallinn,Europe
+Eswatini,Mbabane,Africa
+Ethiopia,Addis Ababa,Africa
+Falkland Islands,Stanley,South America
+Faroe Islands,Trshavn,Europe
+Fiji,Suva,Oceania
+Finland,Helsinki,Europe
+France,Paris,Europe
+French Guiana,Cayenne,South America
+French Polynesia,Papeete,Oceania
+Gabon,Libreville,Africa
+Gambia,Banjul,Africa
+Georgia,Tbilisi,Asia
+Germany,Berlin,Europe
+Ghana,Accra,Africa
+Gibraltar,Gibraltar,Europe
+Greece,Athens,Europe
+Greenland,Nuuk,North America
+Grenada,Saint George's,North America
+Guadeloupe,Basse-Terre,North America
+Guam,Hagta,Oceania
+Guatemala,Guatemala City,North America
+Guernsey,Saint Peter Port,Europe
+Guinea,Conakry,Africa
+Guinea-Bissau,Bissau,Africa
+Guyana,Georgetown,South America
+Haiti,Port-au-Prince,North America
+Honduras,Tegucigalpa,North America
+Hong Kong,Hong Kong,Asia
+Hungary,Budapest,Europe
+Iceland,Reykjavk,Europe
+India,New Delhi,Asia
+Indonesia,Jakarta,Asia
+Iran,Tehran,Asia
+Iraq,Baghdad,Asia
+Ireland,Dublin,Europe
+Isle of Man,Douglas,Europe
+Israel,Jerusalem,Asia
+Italy,Rome,Europe
+Ivory Coast,Yamoussoukro,Africa
+Jamaica,Kingston,North America
+Japan,Tokyo,Asia
+Jersey,Saint Helier,Europe
+Jordan,Amman,Asia
+Kazakhstan,Nursultan,Asia
+Kenya,Nairobi,Africa
+Kiribati,Tarawa,Oceania
+Kuwait,Kuwait City,Asia
+Kyrgyzstan,Bishkek,Asia
+Laos,Vientiane,Asia
+Latvia,Riga,Europe
+Lebanon,Beirut,Asia
+Lesotho,Maseru,Africa
+Liberia,Monrovia,Africa
+Libya,Tripoli,Africa
+Liechtenstein,Vaduz,Europe
+Lithuania,Vilnius,Europe
+Luxembourg,Luxembourg,Europe
+Macau,Concelho de Macau,Asia
+Madagascar,Antananarivo,Africa
+Malawi,,Africa
+Malaysia,Kuala Lumpur,Asia
+Maldives,Mal??,Asia
+Mali,Bamako,Africa
+Malta,Valletta,Europe
+Marshall Islands,Majuro,Oceania
+Martinique,,North America
+Mauritania,Nouakchott,Africa
+Mauritius,Port Louis,Africa
+Mayotte,Mamoudzou,Africa
+Mexico,Mexico City,North America
+Micronesia,Palikir,Oceania
+Moldova,Chisinau,Europe
+Monaco,Monaco,Europe
+Mongolia,Ulaanbaatar,Asia
+Montenegro,Podgorica,Europe
+Montserrat,Brades,North America
+Morocco,Rabat,Africa
+Mozambique,Maputo,Africa
+Myanmar,Nay Pyi Taw,Asia
+Namibia,Windhoek,Africa
+Nauru,Yaren,Oceania
+Nepal,Kathmandu,Asia
+Netherlands,Amsterdam,Europe
+New Caledonia,Noum??a,Oceania
+New Zealand,Wellington,Oceania
+Nicaragua,Managua,North America
+Niger,Niamey,Africa
+Nigeria,Abuja,Africa
+Niue,Alofi,Oceania
+North Korea,Pyongyang,Asia
+North Macedonia,Skopje,Europe
+Northern Mariana Islands,Saipan,Oceania
+Norway,Oslo,Europe
+Oman,Muscat,Asia
+Pakistan,Islamabad,Asia
+Palau,Ngerulmud,Oceania
+Palestine,Ramallah,Asia
+Panama,Panama City,North America
+Papua New Guinea,Port Moresby,Oceania
+Paraguay,Asunci??n,South America
+Peru,Lima,South America
+Philippines,Manila,Asia
+Poland,Warsaw,Europe
+Portugal,Lisbon,Europe
+Puerto Rico,San Juan,North America
+Qatar,Doha,Asia
+Republic of the Congo,Brazzaville,Africa
+Reunion,Saint-Denis,Africa
+Romania,Bucharest,Europe
+Russia,Moscow,Europe
+Rwanda,Kigali,Africa
+Saint Barthelemy,Gustavia,North America
+Saint Kitts and Nevis,Basseterre,North America
+Saint Lucia,Castries,North America
+Saint Martin,Marigot,North America
+Saint Pierre and Miquelon,Saint-Pierre,North America
+Saint Vincent and the Grenadines,Kingstown,North America
+Samoa,Apia,Oceania
+San Marino,San Marino,Europe
+Sao Tome and Principe,So Tom,Africa
+Saudi Arabia,Riyadh,Asia
+Senegal,Dakar,Africa
+Serbia,Belgrade,Europe
+Seychelles,Victoria,Africa
+Sierra Leone,Freetown,Africa
+Singapore,Singapore,Asia
+Sint Maarten,Philipsburg,North America
+Slovakia,Bratislava,Europe
+Slovenia,Ljubljana,Europe
+Solomon Islands,Honiara,Oceania
+Somalia,Mogadishu,Africa
+South Africa,Pretoria,Africa
+South Korea,Seoul,Asia
+South Sudan,Juba,Africa
+Spain,Madrid,Europe
+Sri Lanka,Colombo,Asia
+Sudan,Khartoum,Africa
+Suriname,Paramaribo,South America
+Sweden,Stockholm,Europe
+Switzerland,Bern,Europe
+Syria,Damascus,Asia
+Taiwan,Taipei,Asia
+Tajikistan,Dushanbe,Asia
+Tanzania,Dodoma,Africa
+Thailand,Bangkok,Asia
+Timor-Leste,Dili,Asia
+Togo,Lom,Africa
+Tokelau,Nukunonu,Oceania
+Tonga,Nukualofa,Oceania
+Trinidad and Tobago,Port-of-Spain,North America
+Tunisia,Tunis,Africa
+Turkey,Ankara,Asia
+Turkmenistan,Ashgabat,Asia
+Turks and Caicos Islands,Cockburn Town,North America
+Tuvalu,Funafuti,Oceania
+Uganda,Kampala,Africa
+Ukraine,Kiev,Europe
+United Arab Emirates,Abu Dhabi,Asia
+United Kingdom,London,Europe
+United States,"Washington, D.C.",North America
+United States Virgin Islands,Charlotte Amalie,North America
+Uruguay,Montevideo,South America
+Uzbekistan,Tashkent,Asia
+Vanuatu,Port-Vila,Oceania
+Vatican City,Vatican City,Europe
+Venezuela,Caracas,South America
+Vietnam,Hanoi,Asia
+Wallis and Futuna,Mata-Utu,Oceania
+Western Sahara,El Aain,Africa
+Yemen,Sanaa,Asia
+Zambia,Lusaka,Africa
+Zimbabwe,Harare,Africa

data/dollar.csv

@@ -0,0 +1,244 @@
+"my_date","my_value","bullet","bulletClass","label"
+"28.03.2023","76.5662","","",""
+"31.03.2023","77.0863","","",""
+"01.04.2023","77.3233","","",""
+"04.04.2023","77.9510","","",""
+"05.04.2023","79.3563","","",""
+"06.04.2023","79.4961","","",""
+"07.04.2023","80.6713","","",""
+"08.04.2023","82.3988","","",""
+"11.04.2023","81.7441","","",""
+"12.04.2023","82.1799","","",""
+"13.04.2023","82.0934","","",""
+"14.04.2023","81.6758","","",""
+"15.04.2023","81.5045","","",""
+"18.04.2023","81.6279","","",""
+"19.04.2023","81.6028","","",""
+"20.04.2023","81.6549","","",""
+"21.04.2023","81.6188","","",""
+"22.04.2023","81.4863","","",""
+"25.04.2023","81.2745","","",""
+"26.04.2023","81.5499","","",""
+"27.04.2023","81.6274","","",""
+"28.04.2023","81.5601","","",""
+"29.04.2023","80.5093","","",""
+"03.05.2023","79.9609","","",""
+"04.05.2023","79.3071","","",""
+"05.05.2023","78.6139","","",""
+"06.05.2023","76.8207","","",""
+"11.05.2023","76.6929","","",""
+"12.05.2023","75.8846","round","min-pulsating-bullet","мин"
+"13.05.2023","77.2041","","",""
+"16.05.2023","79.1004","","",""
+"17.05.2023","79.9798","","",""
+"18.05.2023","80.7642","","",""
+"19.05.2023","80.0366","","",""
+"20.05.2023","79.9093","","",""
+"23.05.2023","79.9379","","",""
+"24.05.2023","80.1665","","",""
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data/forcast.csv

@@ -0,0 +1,8 @@
+Year,Population,YearlyPer,Yearly,Median,Fertility,Density
+2020,"7,794,798,739",1.10%,"83,000,320",31,2.47,52
+2025,"8,184,437,460",0.98%,"77,927,744",32,2.54,55
+2030,"8,548,487,400",0.87%,"72,809,988",33,2.62,57
+2035,"8,887,524,213",0.78%,"67,807,363",34,2.7,60
+2040,"9,198,847,240",0.69%,"62,264,605",35,2.77,62
+2045,"9,481,803,274",0.61%,"56,591,207",35,2.85,64
+2050,"9,735,033,990",0.53%,"50,646,143",36,2.95,65

data/population.csv

@@ -0,0 +1,236 @@
+no,Country (or dependency),Population 2020,Yearly Change,Net Change,Density(P/Km²),Land Area (Km²),Migrants (net),Fert. Rate,MedAge,Urban Pop %,World Share
+1,China,"1,439,323,776",0.39%,"5,540,090",153,"9,388,211","-348,399",1.7,38,61%,18.47%
+2,India,"1,380,004,385",0.99%,"13,586,631",464,"2,973,190","-532,687",2.2,28,35%,17.70%
+3,United States,"331,002,651",0.59%,"1,937,734",36,"9,147,420","954,806",1.8,38,83%,4.25%
+4,Indonesia,"273,523,615",1.07%,"2,898,047",151,"1,811,570","-98,955",2.3,30,56%,3.51%
+5,Pakistan,"220,892,340",2.00%,"4,327,022",287,"770,880","-233,379",3.6,23,35%,2.83%
+6,Brazil,"212,559,417",0.72%,"1,509,890",25,"8,358,140","21,200",1.7,33,88%,2.73%
+7,Nigeria,"206,139,589",2.58%,"5,175,990",226,"910,770","-60,000",5.4,18,52%,2.64%
+8,Bangladesh,"164,689,383",1.01%,"1,643,222","1,265","130,170","-369,501",2.1,28,39%,2.11%
+9,Russia,"145,934,462",0.04%,"62,206",9,"16,376,870","182,456",1.8,40,74%,1.87%
+10,Mexico,"128,932,753",1.06%,"1,357,224",66,"1,943,950","-60,000",2.1,29,84%,1.65%
+11,Japan,"126,476,461",-0.30%,"-383,840",347,"364,555","71,560",1.4,48,92%,1.62%
+12,Ethiopia,"114,963,588",2.57%,"2,884,858",115,"1,000,000","30,000",4.3,19,21%,1.47%
+13,Philippines,"109,581,078",1.35%,"1,464,463",368,"298,170","-67,152",2.6,26,47%,1.41%
+14,Egypt,"102,334,404",1.94%,"1,946,331",103,"995,450","-38,033",3.3,25,43%,1.31%
+15,Vietnam,"97,338,579",0.91%,"876,473",314,"310,070","-80,000",2.1,32,38%,1.25%
+16,DR Congo,"89,561,403",3.19%,"2,770,836",40,"2,267,050","23,861",6,17,46%,1.15%
+17,Turkey,"84,339,067",1.09%,"909,452",110,"769,630","283,922",2.1,32,76%,1.08%
+18,Iran,"83,992,949",1.30%,"1,079,043",52,"1,628,550","-55,000",2.2,32,76%,1.08%
+19,Germany,"83,783,942",0.32%,"266,897",240,"348,560","543,822",1.6,46,76%,1.07%
+20,Thailand,"69,799,978",0.25%,"174,396",137,"510,890","19,444",1.5,40,51%,0.90%
+21,United Kingdom,"67,886,011",0.53%,"355,839",281,"241,930","260,650",1.8,40,83%,0.87%
+22,France,"65,273,511",0.22%,"143,783",119,"547,557","36,527",1.9,42,82%,0.84%
+23,Italy,"60,461,826",-0.15%,"-88,249",206,"294,140","148,943",1.3,47,69%,0.78%
+24,Tanzania,"59,734,218",2.98%,"1,728,755",67,"885,800","-40,076",4.9,18,37%,0.77%
+25,South Africa,"59,308,690",1.28%,"750,420",49,"1,213,090","145,405",2.4,28,67%,0.76%
+26,Myanmar,"54,409,800",0.67%,"364,380",83,"653,290","-163,313",2.2,29,31%,0.70%
+27,Kenya,"53,771,296",2.28%,"1,197,323",94,"569,140","-10,000",3.5,20,28%,0.69%
+28,South Korea,"51,269,185",0.09%,"43,877",527,"97,230","11,731",1.1,44,82%,0.66%
+29,Colombia,"50,882,891",1.08%,"543,448",46,"1,109,500","204,796",1.8,31,80%,0.65%
+30,Spain,"46,754,778",0.04%,"18,002",94,"498,800","40,000",1.3,45,80%,0.60%
+31,Uganda,"45,741,007",3.32%,"1,471,413",229,"199,810","168,694",5,17,26%,0.59%
+32,Argentina,"45,195,774",0.93%,"415,097",17,"2,736,690","4,800",2.3,32,93%,0.58%
+33,Algeria,"43,851,044",1.85%,"797,990",18,"2,381,740","-10,000",3.1,29,73%,0.56%
+34,Sudan,"43,849,260",2.42%,"1,036,022",25,"1,765,048","-50,000",4.4,20,35%,0.56%
+35,Ukraine,"43,733,762",-0.59%,"-259,876",75,"579,320","10,000",1.4,41,69%,0.56%
+36,Iraq,"40,222,493",2.32%,"912,710",93,"434,320","7,834",3.7,21,73%,0.52%
+37,Afghanistan,"38,928,346",2.33%,"886,592",60,"652,860","-62,920",4.6,18,25%,0.50%
+38,Poland,"37,846,611",-0.11%,"-41,157",124,"306,230","-29,395",1.4,42,60%,0.49%
+39,Canada,"37,742,154",0.89%,"331,107",4,"9,093,510","242,032",1.5,41,81%,0.48%
+40,Morocco,"36,910,560",1.20%,"438,791",83,"446,300","-51,419",2.4,30,64%,0.47%
+41,Saudi Arabia,"34,813,871",1.59%,"545,343",16,"2,149,690","134,979",2.3,32,84%,0.45%
+42,Uzbekistan,"33,469,203",1.48%,"487,487",79,"425,400","-8,863",2.4,28,50%,0.43%
+43,Peru,"32,971,854",1.42%,"461,401",26,"1,280,000","99,069",2.3,31,79%,0.42%
+44,Angola,"32,866,272",3.27%,"1,040,977",26,"1,246,700","6,413",5.6,17,67%,0.42%
+45,Malaysia,"32,365,999",1.30%,"416,222",99,"328,550","50,000",2,30,78%,0.42%
+46,Mozambique,"31,255,435",2.93%,"889,399",40,"786,380","-5,000",4.9,18,38%,0.40%
+47,Ghana,"31,072,940",2.15%,"655,084",137,"227,540","-10,000",3.9,22,57%,0.40%
+48,Yemen,"29,825,964",2.28%,"664,042",56,"527,970","-30,000",3.8,20,38%,0.38%
+49,Nepal,"29,136,808",1.85%,"528,098",203,"143,350","41,710",1.9,25,21%,0.37%
+50,Venezuela,"28,435,940",-0.28%,"-79,889",32,"882,050","-653,249",2.3,30,N.A.,0.36%
+51,Madagascar,"27,691,018",2.68%,"721,711",48,"581,795","-1,500",4.1,20,39%,0.36%
+52,Cameroon,"26,545,863",2.59%,"669,483",56,"472,710","-4,800",4.6,19,56%,0.34%
+53,Côte d'Ivoire,"26,378,274",2.57%,"661,730",83,"318,000","-8,000",4.7,19,51%,0.34%
+54,North Korea,"25,778,816",0.44%,"112,655",214,"120,410","-5,403",1.9,35,63%,0.33%
+55,Australia,"25,499,884",1.18%,"296,686",3,"7,682,300","158,246",1.8,38,86%,0.33%
+56,Niger,"24,206,644",3.84%,"895,929",19,"1,266,700","4,000",7,15,17%,0.31%
+57,Taiwan,"23,816,775",0.18%,"42,899",673,"35,410","30,001",1.2,42,79%,0.31%
+58,Sri Lanka,"21,413,249",0.42%,"89,516",341,"62,710","-97,986",2.2,34,18%,0.27%
+59,Burkina Faso,"20,903,273",2.86%,"581,895",76,"273,600","-25,000",5.2,18,31%,0.27%
+60,Mali,"20,250,833",3.02%,"592,802",17,"1,220,190","-40,000",5.9,16,44%,0.26%
+61,Romania,"19,237,691",-0.66%,"-126,866",84,"230,170","-73,999",1.6,43,55%,0.25%
+62,Malawi,"19,129,952",2.69%,"501,205",203,"94,280","-16,053",4.3,18,18%,0.25%
+63,Chile,"19,116,201",0.87%,"164,163",26,"743,532","111,708",1.7,35,85%,0.25%
+64,Kazakhstan,"18,776,707",1.21%,"225,280",7,"2,699,700","-18,000",2.8,31,58%,0.24%
+65,Zambia,"18,383,955",2.93%,"522,925",25,"743,390","-8,000",4.7,18,45%,0.24%
+66,Guatemala,"17,915,568",1.90%,"334,096",167,"107,160","-9,215",2.9,23,52%,0.23%
+67,Ecuador,"17,643,054",1.55%,"269,392",71,"248,360","36,400",2.4,28,63%,0.23%
+68,Syria,"17,500,658",2.52%,"430,523",95,"183,630","-427,391",2.8,26,60%,0.22%
+69,Netherlands,"17,134,872",0.22%,"37,742",508,"33,720","16,000",1.7,43,92%,0.22%
+70,Senegal,"16,743,927",2.75%,"447,563",87,"192,530","-20,000",4.7,19,49%,0.21%
+71,Cambodia,"16,718,965",1.41%,"232,423",95,"176,520","-30,000",2.5,26,24%,0.21%
+72,Chad,"16,425,864",3.00%,"478,988",13,"1,259,200","2,000",5.8,17,23%,0.21%
+73,Somalia,"15,893,222",2.92%,"450,317",25,"627,340","-40,000",6.1,17,47%,0.20%
+74,Zimbabwe,"14,862,924",1.48%,"217,456",38,"386,850","-116,858",3.6,19,38%,0.19%
+75,Guinea,"13,132,795",2.83%,"361,549",53,"245,720","-4,000",4.7,18,39%,0.17%
+76,Rwanda,"12,952,218",2.58%,"325,268",525,"24,670","-9,000",4.1,20,18%,0.17%
+77,Benin,"12,123,200",2.73%,"322,049",108,"112,760","-2,000",4.9,19,48%,0.16%
+78,Burundi,"11,890,784",3.12%,"360,204",463,"25,680","2,001",5.5,17,14%,0.15%
+79,Tunisia,"11,818,619",1.06%,"123,900",76,"155,360","-4,000",2.2,33,70%,0.15%
+80,Bolivia,"11,673,021",1.39%,"159,921",11,"1,083,300","-9,504",2.8,26,69%,0.15%
+81,Belgium,"11,589,623",0.44%,"50,295",383,"30,280","48,000",1.7,42,98%,0.15%
+82,Haiti,"11,402,528",1.24%,"139,451",414,"27,560","-35,000",3,24,57%,0.15%
+83,Cuba,"11,326,616",-0.06%,"-6,867",106,"106,440","-14,400",1.6,42,78%,0.15%
+84,South Sudan,"11,193,725",1.19%,"131,612",18,"610,952","-174,200",4.7,19,25%,0.14%
+85,Dominican Republic,"10,847,910",1.01%,"108,952",225,"48,320","-30,000",2.4,28,85%,0.14%
+86,Czech Republic (Czechia),"10,708,981",0.18%,"19,772",139,"77,240","22,011",1.6,43,74%,0.14%
+87,Greece,"10,423,054",-0.48%,"-50,401",81,"128,900","-16,000",1.3,46,85%,0.13%
+88,Jordan,"10,203,134",1.00%,"101,440",115,"88,780","10,220",2.8,24,91%,0.13%
+89,Portugal,"10,196,709",-0.29%,"-29,478",111,"91,590","-6,000",1.3,46,66%,0.13%
+90,Azerbaijan,"10,139,177",0.91%,"91,459",123,"82,658","1,200",2.1,32,56%,0.13%
+91,Sweden,"10,099,265",0.63%,"62,886",25,"410,340","40,000",1.9,41,88%,0.13%
+92,Honduras,"9,904,607",1.63%,"158,490",89,"111,890","-6,800",2.5,24,57%,0.13%
+93,United Arab Emirates,"9,890,402",1.23%,"119,873",118,"83,600","40,000",1.4,33,86%,0.13%
+94,Hungary,"9,660,351",-0.25%,"-24,328",107,"90,530","6,000",1.5,43,72%,0.12%
+95,Tajikistan,"9,537,645",2.32%,"216,627",68,"139,960","-20,000",3.6,22,27%,0.12%
+96,Belarus,"9,449,323",-0.03%,"-3,088",47,"202,910","8,730",1.7,40,79%,0.12%
+97,Austria,"9,006,398",0.57%,"51,296",109,"82,409","65,000",1.5,43,57%,0.12%
+98,Papua New Guinea,"8,947,024",1.95%,"170,915",20,"452,860",-800,3.6,22,13%,0.11%
+99,Serbia,"8,737,371",-0.40%,"-34,864",100,"87,460","4,000",1.5,42,56%,0.11%
+100,Israel,"8,655,535",1.60%,"136,158",400,"21,640","10,000",3,30,93%,0.11%
+101,Switzerland,"8,654,622",0.74%,"63,257",219,"39,516","52,000",1.5,43,74%,0.11%
+102,Togo,"8,278,724",2.43%,"196,358",152,"54,390","-2,000",4.4,19,43%,0.11%
+103,Sierra Leone,"7,976,983",2.10%,"163,768",111,"72,180","-4,200",4.3,19,43%,0.10%
+104,Hong Kong,"7,496,981",0.82%,"60,827","7,140","1,050","29,308",1.3,45,N.A.,0.10%
+105,Laos,"7,275,560",1.48%,"106,105",32,"230,800","-14,704",2.7,24,36%,0.09%
+106,Paraguay,"7,132,538",1.25%,"87,902",18,"397,300","-16,556",2.4,26,62%,0.09%
+107,Bulgaria,"6,948,445",-0.74%,"-51,674",64,"108,560","-4,800",1.6,45,76%,0.09%
+108,Libya,"6,871,292",1.38%,"93,840",4,"1,759,540","-1,999",2.3,29,78%,0.09%
+109,Lebanon,"6,825,445",-0.44%,"-30,268",667,"10,230","-30,012",2.1,30,78%,0.09%
+110,Nicaragua,"6,624,554",1.21%,"79,052",55,"120,340","-21,272",2.4,26,57%,0.08%
+111,Kyrgyzstan,"6,524,195",1.69%,"108,345",34,"191,800","-4,000",3,26,36%,0.08%
+112,El Salvador,"6,486,205",0.51%,"32,652",313,"20,720","-40,539",2.1,28,73%,0.08%
+113,Turkmenistan,"6,031,200",1.50%,"89,111",13,"469,930","-5,000",2.8,27,53%,0.08%
+114,Singapore,"5,850,342",0.79%,"46,005","8,358",700,"27,028",1.2,42,N.A.,0.08%
+115,Denmark,"5,792,202",0.35%,"20,326",137,"42,430","15,200",1.8,42,88%,0.07%
+116,Finland,"5,540,720",0.15%,"8,564",18,"303,890","14,000",1.5,43,86%,0.07%
+117,Congo,"5,518,087",2.56%,"137,579",16,"341,500","-4,000",4.5,19,70%,0.07%
+118,Slovakia,"5,459,642",0.05%,"2,629",114,"48,088","1,485",1.5,41,54%,0.07%
+119,Norway,"5,421,241",0.79%,"42,384",15,"365,268","28,000",1.7,40,83%,0.07%
+120,Oman,"5,106,626",2.65%,"131,640",16,"309,500","87,400",2.9,31,87%,0.07%
+121,State of Palestine,"5,101,414",2.41%,"119,994",847,"6,020","-10,563",3.7,21,80%,0.07%
+122,Costa Rica,"5,094,118",0.92%,"46,557",100,"51,060","4,200",1.8,33,80%,0.07%
+123,Liberia,"5,057,681",2.44%,"120,307",53,"96,320","-5,000",4.4,19,53%,0.06%
+124,Ireland,"4,937,786",1.13%,"55,291",72,"68,890","23,604",1.8,38,63%,0.06%
+125,Central African Republic,"4,829,767",1.78%,"84,582",8,"622,980","-40,000",4.8,18,43%,0.06%
+126,New Zealand,"4,822,233",0.82%,"39,170",18,"263,310","14,881",1.9,38,87%,0.06%
+127,Mauritania,"4,649,658",2.74%,"123,962",5,"1,030,700","5,000",4.6,20,57%,0.06%
+128,Panama,"4,314,767",1.61%,"68,328",58,"74,340","11,200",2.5,30,68%,0.06%
+129,Kuwait,"4,270,571",1.51%,"63,488",240,"17,820","39,520",2.1,37,N.A.,0.05%
+130,Croatia,"4,105,267",-0.61%,"-25,037",73,"55,960","-8,001",1.4,44,58%,0.05%
+131,Moldova,"4,033,963",-0.23%,"-9,300",123,"32,850","-1,387",1.3,38,43%,0.05%
+132,Georgia,"3,989,167",-0.19%,"-7,598",57,"69,490","-10,000",2.1,38,58%,0.05%
+133,Eritrea,"3,546,421",1.41%,"49,304",35,"101,000","-39,858",4.1,19,63%,0.05%
+134,Uruguay,"3,473,730",0.35%,"11,996",20,"175,020","-3,000",2,36,96%,0.04%
+135,Bosnia and Herzegovina,"3,280,819",-0.61%,"-20,181",64,"51,000","-21,585",1.3,43,52%,0.04%
+136,Mongolia,"3,278,290",1.65%,"53,123",2,"1,553,560",-852,2.9,28,67%,0.04%
+137,Armenia,"2,963,243",0.19%,"5,512",104,"28,470","-4,998",1.8,35,63%,0.04%
+138,Jamaica,"2,961,167",0.44%,"12,888",273,"10,830","-11,332",2,31,55%,0.04%
+139,Qatar,"2,881,053",1.73%,"48,986",248,"11,610","40,000",1.9,32,96%,0.04%
+140,Albania,"2,877,797",-0.11%,"-3,120",105,"27,400","-14,000",1.6,36,63%,0.04%
+141,Puerto Rico,"2,860,853",-2.47%,"-72,555",323,"8,870","-97,986",1.2,44,N.A.,0.04%
+142,Lithuania,"2,722,289",-1.35%,"-37,338",43,"62,674","-32,780",1.7,45,71%,0.03%
+143,Namibia,"2,540,905",1.86%,"46,375",3,"823,290","-4,806",3.4,22,55%,0.03%
+144,Gambia,"2,416,668",2.94%,"68,962",239,"10,120","-3,087",5.3,18,59%,0.03%
+145,Botswana,"2,351,627",2.08%,"47,930",4,"566,730","3,000",2.9,24,73%,0.03%
+146,Gabon,"2,225,734",2.45%,"53,155",9,"257,670","3,260",4,23,87%,0.03%
+147,Lesotho,"2,142,249",0.80%,"16,981",71,"30,360","-10,047",3.2,24,31%,0.03%
+148,North Macedonia,"2,083,374",0.00%,-85,83,"25,220","-1,000",1.5,39,59%,0.03%
+149,Slovenia,"2,078,938",0.01%,284,103,"20,140","2,000",1.6,45,55%,0.03%
+150,Guinea-Bissau,"1,968,001",2.45%,"47,079",70,"28,120","-1,399",4.5,19,45%,0.03%
+151,Latvia,"1,886,198",-1.08%,"-20,545",30,"62,200","-14,837",1.7,44,69%,0.02%
+152,Bahrain,"1,701,575",3.68%,"60,403","2,239",760,"47,800",2,32,89%,0.02%
+153,Equatorial Guinea,"1,402,985",3.47%,"46,999",50,"28,050","16,000",4.6,22,73%,0.02%
+154,Trinidad and Tobago,"1,399,488",0.32%,"4,515",273,"5,130",-800,1.7,36,52%,0.02%
+155,Estonia,"1,326,535",0.07%,887,31,"42,390","3,911",1.6,42,68%,0.02%
+156,Timor-Leste,"1,318,445",1.96%,"25,326",89,"14,870","-5,385",4.1,21,33%,0.02%
+157,Mauritius,"1,271,768",0.17%,"2,100",626,"2,030",0,1.4,37,41%,0.02%
+158,Cyprus,"1,207,359",0.73%,"8,784",131,"9,240","5,000",1.3,37,67%,0.02%
+159,Eswatini,"1,160,164",1.05%,"12,034",67,"17,200","-8,353",3,21,30%,0.01%
+160,Djibouti,"988,000",1.48%,"14,440",43,"23,180",900,2.8,27,79%,0.01%
+161,Fiji,"896,445",0.73%,"6,492",49,"18,270","-6,202",2.8,28,59%,0.01%
+162,Réunion,"895,312",0.72%,"6,385",358,"2,500","-1,256",2.3,36,100%,0.01%
+163,Comoros,"869,601",2.20%,"18,715",467,"1,861","-2,000",4.2,20,29%,0.01%
+164,Guyana,"786,552",0.48%,"3,786",4,"196,850","-6,000",2.5,27,27%,0.01%
+165,Bhutan,"771,608",1.12%,"8,516",20,"38,117",320,2,28,46%,0.01%
+166,Solomon Islands,"686,884",2.55%,"17,061",25,"27,990","-1,600",4.4,20,23%,0.01%
+167,Macao,"649,335",1.39%,"8,890","21,645",30,"5,000",1.2,39,N.A.,0.01%
+168,Montenegro,"628,066",0.01%,79,47,"13,450",-480,1.8,39,68%,0.01%
+169,Luxembourg,"625,978",1.66%,"10,249",242,"2,590","9,741",1.5,40,88%,0.01%
+170,Western Sahara,"597,339",2.55%,"14,876",2,"266,000","5,582",2.4,28,87%,0.01%
+171,Suriname,"586,632",0.90%,"5,260",4,"156,000","-1,000",2.4,29,65%,0.01%
+172,Cabo Verde,"555,987",1.10%,"6,052",138,"4,030","-1,342",2.3,28,68%,0.01%
+173,Micronesia,"548,914",1.00%,"5,428",784,700,"-2,957",2.9,27,68%,0.01%
+174,Maldives,"540,544",1.81%,"9,591","1,802",300,"11,370",1.9,30,35%,0.01%
+175,Malta,"441,543",0.27%,"1,171","1,380",320,900,1.5,43,93%,0.01%
+176,Brunei,"437,479",0.97%,"4,194",83,"5,270",0,1.8,32,80%,0.01%
+177,Guadeloupe,"400,124",0.02%,68,237,"1,690","-1,440",2.2,44,N.A.,0.01%
+178,Belize,"397,628",1.86%,"7,275",17,"22,810","1,200",2.3,25,46%,0.01%
+179,Bahamas,"393,244",0.97%,"3,762",39,"10,010","1,000",1.8,32,86%,0.01%
+180,Martinique,"375,265",-0.08%,-289,354,"1,060",-960,1.9,47,92%,0.00%
+181,Iceland,"341,243",0.65%,"2,212",3,"100,250",380,1.8,37,94%,0.00%
+182,Vanuatu,"307,145",2.42%,"7,263",25,"12,190",120,3.8,21,24%,0.00%
+183,French Guiana,"298,682",2.70%,"7,850",4,"82,200","1,200",3.4,25,87%,0.00%
+184,Barbados,"287,375",0.12%,350,668,430,-79,1.6,40,31%,0.00%
+185,New Caledonia,"285,498",0.97%,"2,748",16,"18,280",502,2,34,72%,0.00%
+186,French Polynesia,"280,908",0.58%,"1,621",77,"3,660","-1,000",2,34,64%,0.00%
+187,Mayotte,"272,815",2.50%,"6,665",728,375,0,3.7,20,46%,0.00%
+188,Sao Tome & Principe,"219,159",1.91%,"4,103",228,960,"-1,680",4.4,19,74%,0.00%
+189,Samoa,"198,414",0.67%,"1,317",70,"2,830","-2,803",3.9,22,18%,0.00%
+190,Saint Lucia,"183,627",0.46%,837,301,610,0,1.4,34,19%,0.00%
+191,Channel Islands,"173,863",0.93%,"1,604",915,190,"1,351",1.5,43,30%,0.00%
+192,Guam,"168,775",0.89%,"1,481",313,540,-506,2.3,31,95%,0.00%
+193,Curaçao,"164,093",0.41%,669,370,444,515,1.8,42,89%,0.00%
+194,Kiribati,"119,449",1.57%,"1,843",147,810,-800,3.6,23,57%,0.00%
+195,Grenada,"112,523",0.46%,520,331,340,-200,2.1,32,35%,0.00%
+196,St. Vincent & Grenadines,"110,940",0.32%,351,284,390,-200,1.9,33,53%,0.00%
+197,Aruba,"106,766",0.43%,452,593,180,201,1.9,41,44%,0.00%
+198,Tonga,"105,695",1.15%,"1,201",147,720,-800,3.6,22,24%,0.00%
+199,U.S. Virgin Islands,"104,425",-0.15%,-153,298,350,-451,2,43,96%,0.00%
+200,Seychelles,"98,347",0.62%,608,214,460,-200,2.5,34,56%,0.00%
+201,Antigua and Barbuda,"97,929",0.84%,811,223,440,0,2,34,26%,0.00%
+202,Isle of Man,"85,033",0.53%,449,149,570,,N.A.,N.A.,53%,0.00%
+203,Andorra,"77,265",0.16%,123,164,470,,N.A.,N.A.,88%,0.00%
+204,Dominica,"71,986",0.25%,178,96,750,,N.A.,N.A.,74%,0.00%
+205,Cayman Islands,"65,722",1.19%,774,274,240,,N.A.,N.A.,97%,0.00%
+206,Bermuda,"62,278",-0.36%,-228,"1,246",50,,N.A.,N.A.,97%,0.00%
+207,Marshall Islands,"59,190",0.68%,399,329,180,,N.A.,N.A.,70%,0.00%
+208,Northern Mariana Islands,"57,559",0.60%,343,125,460,,N.A.,N.A.,88%,0.00%
+209,Greenland,"56,770",0.17%,98,0,"410,450",,N.A.,N.A.,87%,0.00%
+210,American Samoa,"55,191",-0.22%,-121,276,200,,N.A.,N.A.,88%,0.00%
+211,Saint Kitts & Nevis,"53,199",0.71%,376,205,260,,N.A.,N.A.,33%,0.00%
+212,Faeroe Islands,"48,863",0.38%,185,35,"1,396",,N.A.,N.A.,43%,0.00%
+213,Sint Maarten,"42,876",1.15%,488,"1,261",34,,N.A.,N.A.,96%,0.00%
+214,Monaco,"39,242",0.71%,278,"26,337",1,,N.A.,N.A.,N.A.,0.00%
+215,Turks and Caicos,"38,717",1.38%,526,41,950,,N.A.,N.A.,89%,0.00%
+216,Saint Martin,"38,666",1.75%,664,730,53,,N.A.,N.A.,0%,0.00%
+217,Liechtenstein,"38,128",0.29%,109,238,160,,N.A.,N.A.,15%,0.00%
+218,San Marino,"33,931",0.21%,71,566,60,,N.A.,N.A.,97%,0.00%
+219,Gibraltar,"33,691",-0.03%,-10,"3,369",10,,N.A.,N.A.,N.A.,0.00%
+220,British Virgin Islands,"30,231",0.67%,201,202,150,,N.A.,N.A.,52%,0.00%
+221,Caribbean Netherlands,"26,223",0.94%,244,80,328,,N.A.,N.A.,75%,0.00%
+222,Palau,"18,094",0.48%,86,39,460,,N.A.,N.A.,N.A.,0.00%
+223,Cook Islands,"17,564",0.09%,16,73,240,,N.A.,N.A.,75%,0.00%
+224,Anguilla,"15,003",0.90%,134,167,90,,N.A.,N.A.,N.A.,0.00%
+225,Tuvalu,"11,792",1.25%,146,393,30,,N.A.,N.A.,62%,0.00%
+226,Wallis & Futuna,"11,239",-1.69%,-193,80,140,,N.A.,N.A.,0%,0.00%
+227,Nauru,"10,824",0.63%,68,541,20,,N.A.,N.A.,N.A.,0.00%
+228,Saint Barthelemy,"9,877",0.30%,30,470,21,,N.A.,N.A.,0%,0.00%
+229,Saint Helena,"6,077",0.30%,18,16,390,,N.A.,N.A.,27%,0.00%
+230,Saint Pierre & Miquelon,"5,794",-0.48%,-28,25,230,,N.A.,N.A.,100%,0.00%
+231,Montserrat,"4,992",0.06%,3,50,100,,N.A.,N.A.,10%,0.00%
+232,Falkland Islands,"3,480",3.05%,103,0,"12,170",,N.A.,N.A.,66%,0.00%
+233,Niue,"1,626",0.68%,11,6,260,,N.A.,N.A.,46%,0.00%
+234,Tokelau,"1,357",1.27%,17,136,10,,N.A.,N.A.,0%,0.00%
+235,Holy See,801,0.25%,2,"2,003",0,,N.A.,N.A.,N.A.,0.00%

data/titanic.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
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lec2.ipynb

@@ -0,0 +1,838 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Загрузка данных в DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 235 entries, 1 to 235\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                   Non-Null Count  Dtype \n",
+      "---  ------                   --------------  ----- \n",
+      " 0   Country (or dependency)  235 non-null    object\n",
+      " 1   Population 2020          235 non-null    object\n",
+      " 2   Yearly Change            235 non-null    object\n",
+      " 3   Net Change               235 non-null    object\n",
+      " 4   Density(P/Km²)           235 non-null    object\n",
+      " 5   Land Area (Km²)          235 non-null    object\n",
+      " 6   Migrants (net)           201 non-null    object\n",
+      " 7   Fert. Rate               235 non-null    object\n",
+      " 8   MedAge                   235 non-null    object\n",
+      " 9   Urban Pop %              235 non-null    object\n",
+      " 10  World Share              235 non-null    object\n",
+      "dtypes: object(11)\n",
+      "memory usage: 22.0+ KB\n",
+      "(235, 11)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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",
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+       "        text-align: right;\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country (or dependency)</th>\n",
+       "      <th>Population 2020</th>\n",
+       "      <th>Yearly Change</th>\n",
+       "      <th>Net Change</th>\n",
+       "      <th>Density(P/Km²)</th>\n",
+       "      <th>Land Area (Km²)</th>\n",
+       "      <th>Migrants (net)</th>\n",
+       "      <th>Fert. Rate</th>\n",
+       "      <th>MedAge</th>\n",
+       "      <th>Urban Pop %</th>\n",
+       "      <th>World Share</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>no</th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1,439,323,776</td>\n",
+       "      <td>0.39%</td>\n",
+       "      <td>5,540,090</td>\n",
+       "      <td>153</td>\n",
+       "      <td>9,388,211</td>\n",
+       "      <td>-348,399</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>38</td>\n",
+       "      <td>61%</td>\n",
+       "      <td>18.47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1,380,004,385</td>\n",
+       "      <td>0.99%</td>\n",
+       "      <td>13,586,631</td>\n",
+       "      <td>464</td>\n",
+       "      <td>2,973,190</td>\n",
+       "      <td>-532,687</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>35%</td>\n",
+       "      <td>17.70%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>331,002,651</td>\n",
+       "      <td>0.59%</td>\n",
+       "      <td>1,937,734</td>\n",
+       "      <td>36</td>\n",
+       "      <td>9,147,420</td>\n",
+       "      <td>954,806</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>38</td>\n",
+       "      <td>83%</td>\n",
+       "      <td>4.25%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Indonesia</td>\n",
+       "      <td>273,523,615</td>\n",
+       "      <td>1.07%</td>\n",
+       "      <td>2,898,047</td>\n",
+       "      <td>151</td>\n",
+       "      <td>1,811,570</td>\n",
+       "      <td>-98,955</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>56%</td>\n",
+       "      <td>3.51%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Pakistan</td>\n",
+       "      <td>220,892,340</td>\n",
+       "      <td>2.00%</td>\n",
+       "      <td>4,327,022</td>\n",
+       "      <td>287</td>\n",
+       "      <td>770,880</td>\n",
+       "      <td>-233,379</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>23</td>\n",
+       "      <td>35%</td>\n",
+       "      <td>2.83%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Country (or dependency) Population 2020 Yearly Change  Net Change  \\\n",
+       "no                                                                     \n",
+       "1                    China   1,439,323,776         0.39%   5,540,090   \n",
+       "2                    India   1,380,004,385         0.99%  13,586,631   \n",
+       "3            United States     331,002,651         0.59%   1,937,734   \n",
+       "4                Indonesia     273,523,615         1.07%   2,898,047   \n",
+       "5                 Pakistan     220,892,340         2.00%   4,327,022   \n",
+       "\n",
+       "   Density(P/Km²) Land Area (Km²) Migrants (net) Fert. Rate MedAge  \\\n",
+       "no                                                                   \n",
+       "1             153       9,388,211       -348,399        1.7     38   \n",
+       "2             464       2,973,190       -532,687        2.2     28   \n",
+       "3              36       9,147,420        954,806        1.8     38   \n",
+       "4             151       1,811,570        -98,955        2.3     30   \n",
+       "5             287         770,880       -233,379        3.6     23   \n",
+       "\n",
+       "   Urban Pop % World Share  \n",
+       "no                          \n",
+       "1          61%      18.47%  \n",
+       "2          35%      17.70%  \n",
+       "3          83%       4.25%  \n",
+       "4          56%       3.51%  \n",
+       "5          35%       2.83%  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"data/population.csv\", index_col=\"no\")\n",
+    "\n",
+    "df.info()\n",
+    "\n",
+    "print(df.shape)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Country (or dependency)     0\n",
+      "Population 2020             0\n",
+      "Yearly Change               0\n",
+      "Net Change                  0\n",
+      "Density  (P/Km²)            0\n",
+      "Land Area (Km²)             0\n",
+      "Migrants (net)             34\n",
+      "Fert. Rate                  0\n",
+      "Med. Age                    0\n",
+      "Urban Pop %                 0\n",
+      "World Share                 0\n",
+      "dtype: int64\n",
+      "\n",
+      "Country (or dependency)    False\n",
+      "Population 2020            False\n",
+      "Yearly Change              False\n",
+      "Net Change                 False\n",
+      "Density  (P/Km²)           False\n",
+      "Land Area (Km²)            False\n",
+      "Migrants (net)              True\n",
+      "Fert. Rate                 False\n",
+      "Med. Age                   False\n",
+      "Urban Pop %                False\n",
+      "World Share                False\n",
+      "dtype: bool\n",
+      "\n",
+      "Migrants (net) процент пустых значений: %14.47\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# 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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(235, 12)\n",
+      "Country (or dependency)    False\n",
+      "Population 2020            False\n",
+      "Yearly Change              False\n",
+      "Net Change                 False\n",
+      "Density  (P/Km²)           False\n",
+      "Land Area (Km²)            False\n",
+      "Migrants (net)             False\n",
+      "Fert. Rate                 False\n",
+      "Med. Age                   False\n",
+      "Urban Pop %                False\n",
+      "World Share                False\n",
+      "MigrantsFill               False\n",
+      "dtype: bool\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "Cannot convert ['-348,399' '-532,687' '954,806' '-98,955' '-233,379' '21,200' '-60,000'\n '-369,501' '182,456' '-60,000' '71,560' '30,000' '-67,152' '-38,033'\n '-80,000' '23,861' '283,922' '-55,000' '543,822' '19,444' '260,650'\n '36,527' '148,943' '-40,076' '145,405' '-163,313' '-10,000' '11,731'\n '204,796' '40,000' '168,694' '4,800' '-10,000' '-50,000' '10,000' '7,834'\n '-62,920' '-29,395' '242,032' '-51,419' '134,979' '-8,863' '99,069'\n '6,413' '50,000' '-5,000' '-10,000' '-30,000' '41,710' '-653,249'\n '-1,500' '-4,800' '-8,000' '-5,403' '158,246' '4,000' '30,001' '-97,986'\n '-25,000' '-40,000' '-73,999' '-16,053' '111,708' '-18,000' '-8,000'\n '-9,215' '36,400' '-427,391' '16,000' '-20,000' '-30,000' '2,000'\n '-40,000' '-116,858' '-4,000' '-9,000' '-2,000' '2,001' '-4,000' '-9,504'\n '48,000' '-35,000' '-14,400' '-174,200' '-30,000' '22,011' '-16,000'\n '10,220' '-6,000' '1,200' '40,000' '-6,800' '40,000' '6,000' '-20,000'\n '8,730' '65,000' '-800' '4,000' '10,000' '52,000' '-2,000' '-4,200'\n '29,308' '-14,704' '-16,556' '-4,800' '-1,999' '-30,012' '-21,272'\n '-4,000' '-40,539' '-5,000' '27,028' '15,200' '14,000' '-4,000' '1,485'\n '28,000' '87,400' '-10,563' '4,200' '-5,000' '23,604' '-40,000' '14,881'\n '5,000' '11,200' '39,520' '-8,001' '-1,387' '-10,000' '-39,858' '-3,000'\n '-21,585' '-852' '-4,998' '-11,332' '40,000' '-14,000' '-97,986'\n '-32,780' '-4,806' '-3,087' '3,000' '3,260' '-10,047' '-1,000' '2,000'\n '-1,399' '-14,837' '47,800' '16,000' '-800' '3,911' '-5,385' '0' '5,000'\n '-8,353' '900' '-6,202' '-1,256' '-2,000' '-6,000' '320' '-1,600' '5,000'\n '-480' '9,741' '5,582' '-1,000' '-1,342' '-2,957' '11,370' '900' '0'\n '-1,440' '1,200' '1,000' '-960' '380' '120' '1,200' '-79' '502' '-1,000'\n '0' '-1,680' '-2,803' '0' '1,351' '-506' '515' '-800' '-200' '-200' '201'\n '-800' '-451' '-200' '0' nan nan nan nan nan nan nan nan nan nan nan nan\n nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n nan nan nan nan] to numeric",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[11], line 11\u001b[0m\n\u001b[0;32m      8\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMigrantsFill\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMigrants (net)\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mfillna(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Замена пустых данных на медиану\u001b[39;00m\n\u001b[1;32m---> 11\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMigrantsMedian\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMigrants (net)\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mfillna(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMigrants (net)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m     13\u001b[0m df\u001b[38;5;241m.\u001b[39mtail()\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\series.py:6559\u001b[0m, in \u001b[0;36mSeries.median\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m   6551\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m   6552\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(\n\u001b[0;32m   6553\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   6557\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   6558\u001b[0m ):\n\u001b[1;32m-> 6559\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmedian\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:12431\u001b[0m, in \u001b[0;36mNDFrame.median\u001b[1;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12424\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmedian\u001b[39m(\n\u001b[0;32m  12425\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  12426\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  12429\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m  12430\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[1;32m> 12431\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  12432\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmedian\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmedian\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m  12433\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:12377\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[1;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[0;32m  12373\u001b[0m nv\u001b[38;5;241m.\u001b[39mvalidate_func(name, (), kwargs)\n\u001b[0;32m  12375\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m> 12377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m  12378\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[0;32m  12379\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\series.py:6457\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[1;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[0;32m   6452\u001b[0m     \u001b[38;5;66;03m# GH#47500 - change to TypeError to match other methods\u001b[39;00m\n\u001b[0;32m   6453\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   6454\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   6455\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   6456\u001b[0m     )\n\u001b[1;32m-> 6457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelegate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__.<locals>.f\u001b[1;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[0;32m    145\u001b[0m         result \u001b[38;5;241m=\u001b[39m alt(values, axis\u001b[38;5;241m=\u001b[39maxis, skipna\u001b[38;5;241m=\u001b[39mskipna, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 147\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\pandas\\core\\nanops.py:787\u001b[0m, in \u001b[0;36mnanmedian\u001b[1;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[0;32m    785\u001b[0m     inferred \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39minfer_dtype(values)\n\u001b[0;32m    786\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inferred \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstring\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m--> 787\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot convert \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalues\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    788\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    789\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[1;31mTypeError\u001b[0m: Cannot convert ['-348,399' '-532,687' '954,806' '-98,955' '-233,379' '21,200' '-60,000'\n '-369,501' '182,456' '-60,000' '71,560' '30,000' '-67,152' '-38,033'\n '-80,000' '23,861' '283,922' '-55,000' '543,822' '19,444' '260,650'\n '36,527' '148,943' '-40,076' '145,405' '-163,313' '-10,000' '11,731'\n '204,796' '40,000' '168,694' '4,800' '-10,000' '-50,000' '10,000' '7,834'\n '-62,920' '-29,395' '242,032' '-51,419' '134,979' '-8,863' '99,069'\n '6,413' '50,000' '-5,000' '-10,000' '-30,000' '41,710' '-653,249'\n '-1,500' '-4,800' '-8,000' '-5,403' '158,246' '4,000' '30,001' '-97,986'\n '-25,000' '-40,000' '-73,999' '-16,053' '111,708' '-18,000' '-8,000'\n '-9,215' '36,400' '-427,391' '16,000' '-20,000' '-30,000' '2,000'\n '-40,000' '-116,858' '-4,000' '-9,000' '-2,000' '2,001' '-4,000' '-9,504'\n '48,000' '-35,000' '-14,400' '-174,200' '-30,000' '22,011' '-16,000'\n '10,220' '-6,000' '1,200' '40,000' '-6,800' '40,000' '6,000' '-20,000'\n '8,730' '65,000' '-800' '4,000' '10,000' '52,000' '-2,000' '-4,200'\n '29,308' '-14,704' '-16,556' '-4,800' '-1,999' '-30,012' '-21,272'\n '-4,000' '-40,539' '-5,000' '27,028' '15,200' '14,000' '-4,000' '1,485'\n '28,000' '87,400' '-10,563' '4,200' '-5,000' '23,604' '-40,000' '14,881'\n '5,000' '11,200' '39,520' '-8,001' '-1,387' '-10,000' '-39,858' '-3,000'\n '-21,585' '-852' '-4,998' '-11,332' '40,000' '-14,000' '-97,986'\n '-32,780' '-4,806' '-3,087' '3,000' '3,260' '-10,047' '-1,000' '2,000'\n '-1,399' '-14,837' '47,800' '16,000' '-800' '3,911' '-5,385' '0' '5,000'\n '-8,353' '900' '-6,202' '-1,256' '-2,000' '-6,000' '320' '-1,600' '5,000'\n '-480' '9,741' '5,582' '-1,000' '-1,342' '-2,957' '11,370' '900' '0'\n '-1,440' '1,200' '1,000' '-960' '380' '120' '1,200' '-79' '502' '-1,000'\n '0' '-1,680' '-2,803' '0' '1,351' '-506' '515' '-800' '-200' '-200' '201'\n '-800' '-451' '-200' '0' nan nan nan nan nan nan nan nan nan nan nan nan\n nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n nan nan nan nan] to numeric"
+     ]
+    }
+   ],
+   "source": [
+    "fillna_df = df.fillna(0)\n",
+    "\n",
+    "print(fillna_df.shape) # размеры\n",
+    "\n",
+    "print(fillna_df.isnull().any())\n",
+    "\n",
+    "# Замена пустых данных на 0\n",
+    "df[\"MigrantsFill\"] = df[\"Migrants (net)\"].fillna(0)\n",
+    "\n",
+    "# Замена пустых данных на медиану\n",
+    "df[\"MigrantsMedian\"] = df[\"Migrants (net)\"].fillna(df[\"Migrants (net)\"].median())\n",
+    "\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>Country (or dependency)</th>\n",
+       "      <th>Population 2020</th>\n",
+       "      <th>Yearly Change</th>\n",
+       "      <th>Net Change</th>\n",
+       "      <th>Density  (P/Km²)</th>\n",
+       "      <th>Land Area (Km²)</th>\n",
+       "      <th>Migrants (net)</th>\n",
+       "      <th>Fert. Rate</th>\n",
+       "      <th>Med. Age</th>\n",
+       "      <th>Urban Pop %</th>\n",
+       "      <th>World Share</th>\n",
+       "      <th>MigrantsFill</th>\n",
+       "      <th>MigrantsCopy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>no</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>Montserrat</td>\n",
+       "      <td>4,992</td>\n",
+       "      <td>0.06%</td>\n",
+       "      <td>3</td>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>10%</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>Falkland Islands</td>\n",
+       "      <td>3,480</td>\n",
+       "      <td>3.05%</td>\n",
+       "      <td>103</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12,170</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>66%</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>Niue</td>\n",
+       "      <td>1,626</td>\n",
+       "      <td>0.68%</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>260</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>46%</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>Tokelau</td>\n",
+       "      <td>1,357</td>\n",
+       "      <td>1.27%</td>\n",
+       "      <td>17</td>\n",
+       "      <td>136</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>0%</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>Holy See</td>\n",
+       "      <td>801</td>\n",
+       "      <td>0.25%</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2,003</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>0.00%</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Country (or dependency) Population 2020 Yearly Change Net Change  \\\n",
+       "no                                                                     \n",
+       "231              Montserrat           4,992         0.06%          3   \n",
+       "232        Falkland Islands           3,480         3.05%        103   \n",
+       "233                    Niue           1,626         0.68%         11   \n",
+       "234                 Tokelau           1,357         1.27%         17   \n",
+       "235                Holy See             801         0.25%          2   \n",
+       "\n",
+       "    Density  (P/Km²) Land Area (Km²) Migrants (net) Fert. Rate Med. Age  \\\n",
+       "no                                                                        \n",
+       "231               50             100            NaN       N.A.     N.A.   \n",
+       "232                0          12,170            NaN       N.A.     N.A.   \n",
+       "233                6             260            NaN       N.A.     N.A.   \n",
+       "234              136              10            NaN       N.A.     N.A.   \n",
+       "235            2,003               0            NaN       N.A.     N.A.   \n",
+       "\n",
+       "    Urban Pop % World Share MigrantsFill MigrantsCopy  \n",
+       "no                                                     \n",
+       "231         10%       0.00%            0            0  \n",
+       "232         66%       0.00%            0            0  \n",
+       "233         46%       0.00%            0            0  \n",
+       "234          0%       0.00%            0            0  \n",
+       "235        N.A.       0.00%            0            0  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"MigrantsCopy\"] = df[\"Migrants (net)\"]\n",
+    "\n",
+    "# Замена данных сразу в DataFrame без копирования\n",
+    "df.fillna({\"MigrantsCopy\": 0}, inplace=True)\n",
+    "\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Удаление наблюдений с пропусками"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(201, 13)\n",
+      "Country (or dependency)    False\n",
+      "Population 2020            False\n",
+      "Yearly Change              False\n",
+      "Net Change                 False\n",
+      "Density  (P/Km²)           False\n",
+      "Land Area (Km²)            False\n",
+      "Migrants (net)             False\n",
+      "Fert. Rate                 False\n",
+      "Med. Age                   False\n",
+      "Urban Pop %                False\n",
+      "World Share                False\n",
+      "MigrantsFill               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": [
+    "<img src=\"assets/lec2-split.png\" width=\"600\" style=\"background-color: white\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MedAge\n",
+      "N.A.    34\n",
+      "19      14\n",
+      "28      12\n",
+      "43      11\n",
+      "32      11\n",
+      "42      10\n",
+      "18      10\n",
+      "20       9\n",
+      "30       8\n",
+      "38       7\n",
+      "26       7\n",
+      "40       7\n",
+      "22       7\n",
+      "31       6\n",
+      "34       6\n",
+      "24       6\n",
+      "17       6\n",
+      "44       5\n",
+      "29       5\n",
+      "41       5\n",
+      "33       5\n",
+      "21       5\n",
+      "45       5\n",
+      "23       4\n",
+      "37       4\n",
+      "36       4\n",
+      "25       4\n",
+      "27       4\n",
+      "39       3\n",
+      "46       3\n",
+      "35       3\n",
+      "47       2\n",
+      "48       1\n",
+      "15       1\n",
+      "16       1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[17], line 6\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mMedAge\u001b[38;5;241m.\u001b[39mvalue_counts())\n\u001b[0;32m      4\u001b[0m data \u001b[38;5;241m=\u001b[39m df[[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedAge\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFert. Rate\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDensity(P/Km²)\u001b[39m\u001b[38;5;124m\"\u001b[39m]]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m----> 6\u001b[0m df_train, df_val, df_test \u001b[38;5;241m=\u001b[39m \u001b[43msplit_stratified_into_train_val_test\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstratify_colname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMedAge\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfrac_train\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.60\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfrac_val\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.20\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfrac_test\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.20\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     12\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mОбучающая выборка: \u001b[39m\u001b[38;5;124m\"\u001b[39m, df_train\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28mprint\u001b[39m(df_train\u001b[38;5;241m.\u001b[39mMedAge\u001b[38;5;241m.\u001b[39mvalue_counts())\n",
+      "Cell \u001b[1;32mIn[16], line 57\u001b[0m, in \u001b[0;36msplit_stratified_into_train_val_test\u001b[1;34m(df_input, stratify_colname, frac_train, frac_val, frac_test, random_state)\u001b[0m\n\u001b[0;32m     52\u001b[0m y \u001b[38;5;241m=\u001b[39m df_input[\n\u001b[0;32m     53\u001b[0m     [stratify_colname]\n\u001b[0;32m     54\u001b[0m ]  \u001b[38;5;66;03m# Dataframe of just the column on which to stratify.\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;66;03m# Split original dataframe into train and temp dataframes.\u001b[39;00m\n\u001b[1;32m---> 57\u001b[0m df_train, df_temp, y_train, y_temp \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_test_split\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     58\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstratify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfrac_train\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandom_state\u001b[49m\n\u001b[0;32m     59\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     61\u001b[0m \u001b[38;5;66;03m# Split the temp dataframe into val and test dataframes.\u001b[39;00m\n\u001b[0;32m     62\u001b[0m relative_frac_test \u001b[38;5;241m=\u001b[39m frac_test \u001b[38;5;241m/\u001b[39m (frac_val \u001b[38;5;241m+\u001b[39m frac_test)\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n\u001b[1;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    223\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2806\u001b[0m, in \u001b[0;36mtrain_test_split\u001b[1;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[0;32m   2802\u001b[0m         CVClass \u001b[38;5;241m=\u001b[39m ShuffleSplit\n\u001b[0;32m   2804\u001b[0m     cv \u001b[38;5;241m=\u001b[39m CVClass(test_size\u001b[38;5;241m=\u001b[39mn_test, train_size\u001b[38;5;241m=\u001b[39mn_train, random_state\u001b[38;5;241m=\u001b[39mrandom_state)\n\u001b[1;32m-> 2806\u001b[0m     train, test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43marrays\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstratify\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2808\u001b[0m train, test \u001b[38;5;241m=\u001b[39m ensure_common_namespace_device(arrays[\u001b[38;5;241m0\u001b[39m], train, test)\n\u001b[0;32m   2810\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\n\u001b[0;32m   2811\u001b[0m     chain\u001b[38;5;241m.\u001b[39mfrom_iterable(\n\u001b[0;32m   2812\u001b[0m         (_safe_indexing(a, train), _safe_indexing(a, test)) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m arrays\n\u001b[0;32m   2813\u001b[0m     )\n\u001b[0;32m   2814\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:1843\u001b[0m, in \u001b[0;36mBaseShuffleSplit.split\u001b[1;34m(self, X, y, groups)\u001b[0m\n\u001b[0;32m   1813\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Generate indices to split data into training and test set.\u001b[39;00m\n\u001b[0;32m   1814\u001b[0m \n\u001b[0;32m   1815\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1840\u001b[0m \u001b[38;5;124;03mto an integer.\u001b[39;00m\n\u001b[0;32m   1841\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1842\u001b[0m X, y, groups \u001b[38;5;241m=\u001b[39m indexable(X, y, groups)\n\u001b[1;32m-> 1843\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iter_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m   1844\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\1\\Desktop\\улгту\\3 курс\\МИИ\\mai\\.venv\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2252\u001b[0m, in \u001b[0;36mStratifiedShuffleSplit._iter_indices\u001b[1;34m(self, X, y, groups)\u001b[0m\n\u001b[0;32m   2250\u001b[0m class_counts \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mbincount(y_indices)\n\u001b[0;32m   2251\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmin(class_counts) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m-> 2252\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   2253\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe least populated class in y has only 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2254\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m member, which is too few. The minimum\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2255\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m number of groups for any class cannot\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2256\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m be less than 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2257\u001b[0m     )\n\u001b[0;32m   2259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_train \u001b[38;5;241m<\u001b[39m n_classes:\n\u001b[0;32m   2260\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   2261\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe train_size = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m should be greater or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2262\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mequal to the number of classes = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (n_train, n_classes)\n\u001b[0;32m   2263\u001b[0m     )\n",
+      "\u001b[1;31mValueError\u001b[0m: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2."
+     ]
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print(df.MedAge.value_counts())\n",
+    "\n",
+    "data = df[[\"MedAge\", \"Fert. Rate\", \"Density(P/Km²)\"]].copy()\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "    data,\n",
+    "    stratify_colname=\"MedAge\",\n",
+    "    frac_train=0.60,\n",
+    "    frac_val=0.20,\n",
+    "    frac_test=0.20,\n",
+    ")\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train.MedAge.value_counts())\n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val.MedAge.value_counts())\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test.MedAge.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": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'imblearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimblearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mover_sampling\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ADASYN\n\u001b[0;32m      3\u001b[0m ada \u001b[38;5;241m=\u001b[39m ADASYN()\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mОбучающая выборка: \u001b[39m\u001b[38;5;124m\"\u001b[39m, df_train\u001b[38;5;241m.\u001b[39mshape)\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'imblearn'"
+     ]
+    }
+   ],
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

poetry.toml

@@ -0,0 +1,2 @@
+[virtualenvs]
+in-project = true

pyproject.toml

@@ -0,0 +1,29 @@
+[tool.poetry]
+name = "mai"
+version = "1.0.0"
+description = "MAI Examples"
+authors = ["Aleksey Filippov <al.filippov@ulstu.ru>"]
+readme = "readme.md"
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "^3.12"
+jupyter = "^1.1.1"
+numpy = "^2.1.0"
+pandas = "^2.2.2"
+matplotlib = "^3.9.2"
+flask = "^3.0.3"
+apiflask = "^2.2.0"
+flask-cors = "^5.0.0"
+scikit-learn = "^1.5.2"
+imbalanced-learn = "^0.12.3"
+featuretools = "^1.31.0"
+
+
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.29.5"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

readme.md

@@ -0,0 +1,55 @@
+## Окружение и примеры для выполнения лабораторных работ по дисциплине "Методы ИИ"
+
+### Python
+
+Используется Python версии 3.12
+
+Установщик https://www.python.org/ftp/python/3.12.5/python-3.12.5-amd64.exe
+
+### Poetry
+
+Для создания и настройки окружения проекта необходимо установить poetry
+
+**Для Windows (Powershell)**
+
+```
+(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -
+```
+
+**Linux, macOS, Windows (WSL)**
+
+```
+curl -sSL https://install.python-poetry.org | python3 -
+```
+
+**Добавление poetry в PATH**
+
+1. Открыть настройки переменных среды \
+    \
+   <img src="docs/path1.png" width="300"> \
+    \
+   <img src="docs/path2.png" width="400"> \
+2. Изменить переменную Path текущего пользователя \
+    \
+   <img src="docs/path3.png" width="500"> \
+3. Добавление пути `%APPDATA%\Python\Scripts` до исполняемого файла poetry \
+    \
+   <img src="docs/path4.png" width="400">
+
+### Создание окружения
+
+```
+poetry install
+```
+
+### Запуск тестового сервиса
+
+Запустить тестовый сервис можно с помощью VSCode (см. launch.json в каталоге .vscode).
+
+Также запустить тестовый сервис можно с помощью командной строки:
+
+1. Активация виртуального окружения -- `poetry shell`
+
+2. Запуск сервиса -- `python run.py`
+
+Для выходы из виртуального окружения используется команду `exit`

run.py

@@ -0,0 +1,16 @@
+from backend import create_app
+
+app = create_app()
+
+
+def __main():
+    app.run(
+        host="127.0.0.1",
+        port=8080,
+        debug=True,
+        use_reloader=False,
+    )
+
+
+if __name__ == "__main__":
+    __main()

sklearn-env/Scripts/Activate.ps1

@@ -0,0 +1,502 @@
+<#
+.Synopsis
+Activate a Python virtual environment for the current PowerShell session.
+
+.Description
+Pushes the python executable for a virtual environment to the front of the
+$Env:PATH environment variable and sets the prompt to signify that you are
+in a Python virtual environment. Makes use of the command line switches as
+well as the `pyvenv.cfg` file values present in the virtual environment.
+
+.Parameter VenvDir
+Path to the directory that contains the virtual environment to activate. The
+default value for this is the parent of the directory that the Activate.ps1
+script is located within.
+
+.Parameter Prompt
+The prompt prefix to display when this virtual environment is activated. By
+default, this prompt is the name of the virtual environment folder (VenvDir)
+surrounded by parentheses and followed by a single space (ie. '(.venv) ').
+
+.Example
+Activate.ps1
+Activates the Python virtual environment that contains the Activate.ps1 script.
+
+.Example
+Activate.ps1 -Verbose
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and shows extra information about the activation as it executes.
+
+.Example
+Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
+Activates the Python virtual environment located in the specified location.
+
+.Example
+Activate.ps1 -Prompt "MyPython"
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and prefixes the current prompt with the specified string (surrounded in
+parentheses) while the virtual environment is active.
+
+.Notes
+On Windows, it may be required to enable this Activate.ps1 script by setting the
+execution policy for the user. You can do this by issuing the following PowerShell
+command:
+
+PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+For more information on Execution Policies: 
+https://go.microsoft.com/fwlink/?LinkID=135170
+
+#>
+Param(
+    [Parameter(Mandatory = $false)]
+    [String]
+    $VenvDir,
+    [Parameter(Mandatory = $false)]
+    [String]
+    $Prompt
+)
+
+<# Function declarations --------------------------------------------------- #>
+
+<#
+.Synopsis
+Remove all shell session elements added by the Activate script, including the
+addition of the virtual environment's Python executable from the beginning of
+the PATH variable.
+
+.Parameter NonDestructive
+If present, do not remove this function from the global namespace for the
+session.
+
+#>
+function global:deactivate ([switch]$NonDestructive) {
+    # Revert to original values
+
+    # The prior prompt:
+    if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
+        Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
+        Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
+    }
+
+    # The prior PYTHONHOME:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
+        Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
+    }
+
+    # The prior PATH:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
+        Remove-Item -Path Env:_OLD_VIRTUAL_PATH
+    }
+
+    # Just remove the VIRTUAL_ENV altogether:
+    if (Test-Path -Path Env:VIRTUAL_ENV) {
+        Remove-Item -Path env:VIRTUAL_ENV
+    }
+
+    # Just remove VIRTUAL_ENV_PROMPT altogether.
+    if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
+        Remove-Item -Path env:VIRTUAL_ENV_PROMPT
+    }
+
+    # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
+    if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
+        Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
+    }
+
+    # Leave deactivate function in the global namespace if requested:
+    if (-not $NonDestructive) {
+        Remove-Item -Path function:deactivate
+    }
+}
+
+<#
+.Description
+Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
+given folder, and returns them in a map.
+
+For each line in the pyvenv.cfg file, if that line can be parsed into exactly
+two strings separated by `=` (with any amount of whitespace surrounding the =)
+then it is considered a `key = value` line. The left hand string is the key,
+the right hand is the value.
+
+If the value starts with a `'` or a `"` then the first and last character is
+stripped from the value before being captured.
+
+.Parameter ConfigDir
+Path to the directory that contains the `pyvenv.cfg` file.
+#>
+function Get-PyVenvConfig(
+    [String]
+    $ConfigDir
+) {
+    Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
+
+    # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
+    $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
+
+    # An empty map will be returned if no config file is found.
+    $pyvenvConfig = @{ }
+
+    if ($pyvenvConfigPath) {
+
+        Write-Verbose "File exists, parse `key = value` lines"
+        $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
+
+        $pyvenvConfigContent | ForEach-Object {
+            $keyval = $PSItem -split "\s*=\s*", 2
+            if ($keyval[0] -and $keyval[1]) {
+                $val = $keyval[1]
+
+                # Remove extraneous quotations around a string value.
+                if ("'""".Contains($val.Substring(0, 1))) {
+                    $val = $val.Substring(1, $val.Length - 2)
+                }
+
+                $pyvenvConfig[$keyval[0]] = $val
+                Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
+            }
+        }
+    }
+    return $pyvenvConfig
+}
+
+
+<# Begin Activate script --------------------------------------------------- #>
+
+# Determine the containing directory of this script
+$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
+$VenvExecDir = Get-Item -Path $VenvExecPath
+
+Write-Verbose "Activation script is located in path: '$VenvExecPath'"
+Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
+Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
+
+# Set values required in priority: CmdLine, ConfigFile, Default
+# First, get the location of the virtual environment, it might not be
+# VenvExecDir if specified on the command line.
+if ($VenvDir) {
+    Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
+}
+else {
+    Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
+    $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
+    Write-Verbose "VenvDir=$VenvDir"
+}
+
+# Next, read the `pyvenv.cfg` file to determine any required value such
+# as `prompt`.
+$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
+
+# Next, set the prompt from the command line, or the config file, or
+# just use the name of the virtual environment folder.
+if ($Prompt) {
+    Write-Verbose "Prompt specified as argument, using '$Prompt'"
+}
+else {
+    Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
+    if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
+        Write-Verbose "  Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
+        $Prompt = $pyvenvCfg['prompt'];
+    }
+    else {
+        Write-Verbose "  Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
+        Write-Verbose "  Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
+        $Prompt = Split-Path -Path $venvDir -Leaf
+    }
+}
+
+Write-Verbose "Prompt = '$Prompt'"
+Write-Verbose "VenvDir='$VenvDir'"
+
+# Deactivate any currently active virtual environment, but leave the
+# deactivate function in place.
+deactivate -nondestructive
+
+# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
+# that there is an activated venv.
+$env:VIRTUAL_ENV = $VenvDir
+
+if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
+
+    Write-Verbose "Setting prompt to '$Prompt'"
+
+    # Set the prompt to include the env name
+    # Make sure _OLD_VIRTUAL_PROMPT is global
+    function global:_OLD_VIRTUAL_PROMPT { "" }
+    Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
+    New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
+
+    function global:prompt {
+        Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
+        _OLD_VIRTUAL_PROMPT
+    }
+    $env:VIRTUAL_ENV_PROMPT = $Prompt
+}
+
+# Clear PYTHONHOME
+if (Test-Path -Path Env:PYTHONHOME) {
+    Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
+    Remove-Item -Path Env:PYTHONHOME
+}
+
+# Add the venv to the PATH
+Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
+$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"
+
+# SIG # Begin signature block
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+# SIG # End signature block

sklearn-env/Scripts/activate

@@ -0,0 +1,70 @@
+# This file must be used with "source bin/activate" *from bash*
+# You cannot run it directly
+
+deactivate () {
+    # reset old environment variables
+    if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
+        PATH="${_OLD_VIRTUAL_PATH:-}"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
+        PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # Call hash to forget past commands. Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    hash -r 2> /dev/null
+
+    if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
+        PS1="${_OLD_VIRTUAL_PS1:-}"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    unset VIRTUAL_ENV_PROMPT
+    if [ ! "${1:-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+# on Windows, a path can contain colons and backslashes and has to be converted:
+if [ "${OSTYPE:-}" = "cygwin" ] || [ "${OSTYPE:-}" = "msys" ] ; then
+    # transform D:\path\to\venv to /d/path/to/venv on MSYS
+    # and to /cygdrive/d/path/to/venv on Cygwin
+    export VIRTUAL_ENV=$(cygpath "C:\Users\1\Desktop\улгту\3 курс\МИИ\mai\sklearn-env")
+else
+    # use the path as-is
+    export VIRTUAL_ENV="C:\Users\1\Desktop\улгту\3 курс\МИИ\mai\sklearn-env"
+fi
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/Scripts:$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
+# could use `if (set -u; : $PYTHONHOME) ;` in bash
+if [ -n "${PYTHONHOME:-}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
+    _OLD_VIRTUAL_PS1="${PS1:-}"
+    PS1="(sklearn-env) ${PS1:-}"
+    export PS1
+    VIRTUAL_ENV_PROMPT="(sklearn-env) "
+    export VIRTUAL_ENV_PROMPT
+fi
+
+# Call hash to forget past commands. Without forgetting
+# past commands the $PATH changes we made may not be respected
+hash -r 2> /dev/null

sklearn-env/Scripts/activate.bat

@@ -0,0 +1,34 @@
+@echo off
+
+rem This file is UTF-8 encoded, so we need to update the current code page while executing it
+for /f "tokens=2 delims=:." %%a in ('"%SystemRoot%\System32\chcp.com"') do (
+    set _OLD_CODEPAGE=%%a
+)
+if defined _OLD_CODEPAGE (
+    "%SystemRoot%\System32\chcp.com" 65001 > nul
+)
+
+set VIRTUAL_ENV=C:\Users\1\Desktop\улгту\3 курс\МИИ\mai\sklearn-env
+
+if not defined PROMPT set PROMPT=$P$G
+
+if defined _OLD_VIRTUAL_PROMPT set PROMPT=%_OLD_VIRTUAL_PROMPT%
+if defined _OLD_VIRTUAL_PYTHONHOME set PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%
+
+set _OLD_VIRTUAL_PROMPT=%PROMPT%
+set PROMPT=(sklearn-env) %PROMPT%
+
+if defined PYTHONHOME set _OLD_VIRTUAL_PYTHONHOME=%PYTHONHOME%
+set PYTHONHOME=
+
+if defined _OLD_VIRTUAL_PATH set PATH=%_OLD_VIRTUAL_PATH%
+if not defined _OLD_VIRTUAL_PATH set _OLD_VIRTUAL_PATH=%PATH%
+
+set PATH=%VIRTUAL_ENV%\Scripts;%PATH%
+set VIRTUAL_ENV_PROMPT=(sklearn-env) 
+
+:END
+if defined _OLD_CODEPAGE (
+    "%SystemRoot%\System32\chcp.com" %_OLD_CODEPAGE% > nul
+    set _OLD_CODEPAGE=
+)

sklearn-env/Scripts/deactivate.bat

@@ -0,0 +1,22 @@
+@echo off
+
+if defined _OLD_VIRTUAL_PROMPT (
+    set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
+)
+set _OLD_VIRTUAL_PROMPT=
+
+if defined _OLD_VIRTUAL_PYTHONHOME (
+    set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
+    set _OLD_VIRTUAL_PYTHONHOME=
+)
+
+if defined _OLD_VIRTUAL_PATH (
+    set "PATH=%_OLD_VIRTUAL_PATH%"
+)
+
+set _OLD_VIRTUAL_PATH=
+
+set VIRTUAL_ENV=
+set VIRTUAL_ENV_PROMPT=
+
+:END

sklearn-env/pyvenv.cfg

@@ -0,0 +1,5 @@
+home = C:\Python312
+include-system-site-packages = false
+version = 3.12.5
+executable = C:\Python312\python.exe
+command = C:\Python312\python.exe -m venv C:\Users\1\Desktop\улгту\3 курс\МИИ\mai\sklearn-env

test1.csv

@@ -0,0 +1,235 @@
+,Country/Territory,Capital,Continent
+0,Afghanistan,Kabul,Asia
+1,Albania,Tirana,Europe
+2,Algeria,Algiers,Africa
+3,American Samoa,Pago Pago,Oceania
+4,Andorra,Andorra la Vella,Europe
+5,Angola,Luanda,Africa
+6,Anguilla,The Valley,North America
+7,Antigua and Barbuda,Saint Johns,North America
+8,Argentina,Buenos Aires,South America
+9,Armenia,Yerevan,Asia
+10,Aruba,Oranjestad,North America
+11,Australia,Canberra,Oceania
+12,Austria,Vienna,Europe
+13,Azerbaijan,Baku,Asia
+14,Bahamas,Nassau,North America
+15,Bahrain,Manama,Asia
+16,Bangladesh,Dhaka,Asia
+17,Barbados,Bridgetown,North America
+18,Belarus,Minsk,Europe
+19,Belgium,Brussels,Europe
+20,Belize,Belmopan,North America
+21,Benin,Porto-Novo,Africa
+22,Bermuda,Hamilton,North America
+23,Bhutan,Thimphu,Asia
+24,Bolivia,Sucre,South America
+25,Bosnia and Herzegovina,Sarajevo,Europe
+26,Botswana,Gaborone,Africa
+27,Brazil,Brasilia,South America
+28,British Virgin Islands,Road Town,North America
+29,Brunei,Bandar Seri Begawan,Asia
+30,Bulgaria,Sofia,Europe
+31,Burkina Faso,Ouagadougou,Africa
+32,Burundi,Bujumbura,Africa
+33,Cambodia,Phnom Penh,Asia
+34,Cameroon,Yaounde,Africa
+35,Canada,Ottawa,North America
+36,Cape Verde,Praia,Africa
+37,Cayman Islands,George Town,North America
+38,Central African Republic,Bangui,Africa
+39,Chad,N'Djamena,Africa
+40,Chile,Santiago,South America
+41,China,Beijing,Asia
+42,Colombia,Bogota,South America
+43,Comoros,Moroni,Africa
+44,Cook Islands,Avarua,Oceania
+45,Costa Rica,San Jos??,North America
+46,Croatia,Zagreb,Europe
+47,Cuba,Havana,North America
+48,Curacao,Willemstad,North America
+49,Cyprus,Nicosia,Europe
+50,Czech Republic,Prague,Europe
+51,Denmark,Copenhagen,Europe
+52,Djibouti,Djibouti,Africa
+53,Dominica,Roseau,North America
+54,Dominican Republic,Santo Domingo,North America
+55,DR Congo,Kinshasa,Africa
+56,Ecuador,Quito,South America
+57,Egypt,Cairo,Africa
+58,El Salvador,San Salvador,North America
+59,Equatorial Guinea,Malabo,Africa
+60,Eritrea,Asmara,Africa
+61,Estonia,Tallinn,Europe
+62,Eswatini,Mbabane,Africa
+63,Ethiopia,Addis Ababa,Africa
+64,Falkland Islands,Stanley,South America
+65,Faroe Islands,Trshavn,Europe
+66,Fiji,Suva,Oceania
+67,Finland,Helsinki,Europe
+68,France,Paris,Europe
+69,French Guiana,Cayenne,South America
+70,French Polynesia,Papeete,Oceania
+71,Gabon,Libreville,Africa
+72,Gambia,Banjul,Africa
+73,Georgia,Tbilisi,Asia
+74,Germany,Berlin,Europe
+75,Ghana,Accra,Africa
+76,Gibraltar,Gibraltar,Europe
+77,Greece,Athens,Europe
+78,Greenland,Nuuk,North America
+79,Grenada,Saint George's,North America
+80,Guadeloupe,Basse-Terre,North America
+81,Guam,Hagta,Oceania
+82,Guatemala,Guatemala City,North America
+83,Guernsey,Saint Peter Port,Europe
+84,Guinea,Conakry,Africa
+85,Guinea-Bissau,Bissau,Africa
+86,Guyana,Georgetown,South America
+87,Haiti,Port-au-Prince,North America
+88,Honduras,Tegucigalpa,North America
+89,Hong Kong,Hong Kong,Asia
+90,Hungary,Budapest,Europe
+91,Iceland,Reykjavk,Europe
+92,India,New Delhi,Asia
+93,Indonesia,Jakarta,Asia
+94,Iran,Tehran,Asia
+95,Iraq,Baghdad,Asia
+96,Ireland,Dublin,Europe
+97,Isle of Man,Douglas,Europe
+98,Israel,Jerusalem,Asia
+99,Italy,Rome,Europe
+100,Ivory Coast,Yamoussoukro,Africa
+101,Jamaica,Kingston,North America
+102,Japan,Tokyo,Asia
+103,Jersey,Saint Helier,Europe
+104,Jordan,Amman,Asia
+105,Kazakhstan,Nursultan,Asia
+106,Kenya,Nairobi,Africa
+107,Kiribati,Tarawa,Oceania
+108,Kuwait,Kuwait City,Asia
+109,Kyrgyzstan,Bishkek,Asia
+110,Laos,Vientiane,Asia
+111,Latvia,Riga,Europe
+112,Lebanon,Beirut,Asia
+113,Lesotho,Maseru,Africa
+114,Liberia,Monrovia,Africa
+115,Libya,Tripoli,Africa
+116,Liechtenstein,Vaduz,Europe
+117,Lithuania,Vilnius,Europe
+118,Luxembourg,Luxembourg,Europe
+119,Macau,Concelho de Macau,Asia
+120,Madagascar,Antananarivo,Africa
+121,Malawi,,Africa
+122,Malaysia,Kuala Lumpur,Asia
+123,Maldives,Mal??,Asia
+124,Mali,Bamako,Africa
+125,Malta,Valletta,Europe
+126,Marshall Islands,Majuro,Oceania
+127,Martinique,,North America
+128,Mauritania,Nouakchott,Africa
+129,Mauritius,Port Louis,Africa
+130,Mayotte,Mamoudzou,Africa
+131,Mexico,Mexico City,North America
+132,Micronesia,Palikir,Oceania
+133,Moldova,Chisinau,Europe
+134,Monaco,Monaco,Europe
+135,Mongolia,Ulaanbaatar,Asia
+136,Montenegro,Podgorica,Europe
+137,Montserrat,Brades,North America
+138,Morocco,Rabat,Africa
+139,Mozambique,Maputo,Africa
+140,Myanmar,Nay Pyi Taw,Asia
+141,Namibia,Windhoek,Africa
+142,Nauru,Yaren,Oceania
+143,Nepal,Kathmandu,Asia
+144,Netherlands,Amsterdam,Europe
+145,New Caledonia,Noum??a,Oceania
+146,New Zealand,Wellington,Oceania
+147,Nicaragua,Managua,North America
+148,Niger,Niamey,Africa
+149,Nigeria,Abuja,Africa
+150,Niue,Alofi,Oceania
+151,North Korea,Pyongyang,Asia
+152,North Macedonia,Skopje,Europe
+153,Northern Mariana Islands,Saipan,Oceania
+154,Norway,Oslo,Europe
+155,Oman,Muscat,Asia
+156,Pakistan,Islamabad,Asia
+157,Palau,Ngerulmud,Oceania
+158,Palestine,Ramallah,Asia
+159,Panama,Panama City,North America
+160,Papua New Guinea,Port Moresby,Oceania
+161,Paraguay,Asunci??n,South America
+162,Peru,Lima,South America
+163,Philippines,Manila,Asia
+164,Poland,Warsaw,Europe
+165,Portugal,Lisbon,Europe
+166,Puerto Rico,San Juan,North America
+167,Qatar,Doha,Asia
+168,Republic of the Congo,Brazzaville,Africa
+169,Reunion,Saint-Denis,Africa
+170,Romania,Bucharest,Europe
+171,Russia,Moscow,Europe
+172,Rwanda,Kigali,Africa
+173,Saint Barthelemy,Gustavia,North America
+174,Saint Kitts and Nevis,Basseterre,North America
+175,Saint Lucia,Castries,North America
+176,Saint Martin,Marigot,North America
+177,Saint Pierre and Miquelon,Saint-Pierre,North America
+178,Saint Vincent and the Grenadines,Kingstown,North America
+179,Samoa,Apia,Oceania
+180,San Marino,San Marino,Europe
+181,Sao Tome and Principe,So Tom,Africa
+182,Saudi Arabia,Riyadh,Asia
+183,Senegal,Dakar,Africa
+184,Serbia,Belgrade,Europe
+185,Seychelles,Victoria,Africa
+186,Sierra Leone,Freetown,Africa
+187,Singapore,Singapore,Asia
+188,Sint Maarten,Philipsburg,North America
+189,Slovakia,Bratislava,Europe
+190,Slovenia,Ljubljana,Europe
+191,Solomon Islands,Honiara,Oceania
+192,Somalia,Mogadishu,Africa
+193,South Africa,Pretoria,Africa
+194,South Korea,Seoul,Asia
+195,South Sudan,Juba,Africa
+196,Spain,Madrid,Europe
+197,Sri Lanka,Colombo,Asia
+198,Sudan,Khartoum,Africa
+199,Suriname,Paramaribo,South America
+200,Sweden,Stockholm,Europe
+201,Switzerland,Bern,Europe
+202,Syria,Damascus,Asia
+203,Taiwan,Taipei,Asia
+204,Tajikistan,Dushanbe,Asia
+205,Tanzania,Dodoma,Africa
+206,Thailand,Bangkok,Asia
+207,Timor-Leste,Dili,Asia
+208,Togo,Lom,Africa
+209,Tokelau,Nukunonu,Oceania
+210,Tonga,Nukualofa,Oceania
+211,Trinidad and Tobago,Port-of-Spain,North America
+212,Tunisia,Tunis,Africa
+213,Turkey,Ankara,Asia
+214,Turkmenistan,Ashgabat,Asia
+215,Turks and Caicos Islands,Cockburn Town,North America
+216,Tuvalu,Funafuti,Oceania
+217,Uganda,Kampala,Africa
+218,Ukraine,Kiev,Europe
+219,United Arab Emirates,Abu Dhabi,Asia
+220,United Kingdom,London,Europe
+221,United States,"Washington, D.C.",North America
+222,United States Virgin Islands,Charlotte Amalie,North America
+223,Uruguay,Montevideo,South America
+224,Uzbekistan,Tashkent,Asia
+225,Vanuatu,Port-Vila,Oceania
+226,Vatican City,Vatican City,Europe
+227,Venezuela,Caracas,South America
+228,Vietnam,Hanoi,Asia
+229,Wallis and Futuna,Mata-Utu,Oceania
+230,Western Sahara,El Aain,Africa
+231,Yemen,Sanaa,Asia
+232,Zambia,Lusaka,Africa
+233,Zimbabwe,Harare,Africa

test2.csv

@@ -0,0 +1,233 @@
+,Country/Territory,Capital,Continent
+0,Afghanistan,Kabul,Asia
+1,Albania,Tirana,Europe
+2,Algeria,Algiers,Africa
+3,American Samoa,Pago Pago,Oceania
+4,Andorra,Andorra la Vella,Europe
+5,Angola,Luanda,Africa
+6,Anguilla,The Valley,North America
+7,Antigua and Barbuda,Saint Johns,North America
+8,Argentina,Buenos Aires,South America
+9,Armenia,Yerevan,Asia
+10,Aruba,Oranjestad,North America
+11,Australia,Canberra,Oceania
+12,Austria,Vienna,Europe
+13,Azerbaijan,Baku,Asia
+14,Bahamas,Nassau,North America
+15,Bahrain,Manama,Asia
+16,Bangladesh,Dhaka,Asia
+17,Barbados,Bridgetown,North America
+18,Belarus,Minsk,Europe
+19,Belgium,Brussels,Europe
+20,Belize,Belmopan,North America
+21,Benin,Porto-Novo,Africa
+22,Bermuda,Hamilton,North America
+23,Bhutan,Thimphu,Asia
+24,Bolivia,Sucre,South America
+25,Bosnia and Herzegovina,Sarajevo,Europe
+26,Botswana,Gaborone,Africa
+27,Brazil,Brasilia,South America
+28,British Virgin Islands,Road Town,North America
+29,Brunei,Bandar Seri Begawan,Asia
+30,Bulgaria,Sofia,Europe
+31,Burkina Faso,Ouagadougou,Africa
+32,Burundi,Bujumbura,Africa
+33,Cambodia,Phnom Penh,Asia
+34,Cameroon,Yaounde,Africa
+35,Canada,Ottawa,North America
+36,Cape Verde,Praia,Africa
+37,Cayman Islands,George Town,North America
+38,Central African Republic,Bangui,Africa
+39,Chad,N'Djamena,Africa
+40,Chile,Santiago,South America
+41,China,Beijing,Asia
+42,Colombia,Bogota,South America
+43,Comoros,Moroni,Africa
+44,Cook Islands,Avarua,Oceania
+45,Costa Rica,San Jos??,North America
+46,Croatia,Zagreb,Europe
+47,Cuba,Havana,North America
+48,Curacao,Willemstad,North America
+49,Cyprus,Nicosia,Europe
+50,Czech Republic,Prague,Europe
+51,Denmark,Copenhagen,Europe
+52,Djibouti,Djibouti,Africa
+53,Dominica,Roseau,North America
+54,Dominican Republic,Santo Domingo,North America
+55,DR Congo,Kinshasa,Africa
+56,Ecuador,Quito,South America
+57,Egypt,Cairo,Africa
+58,El Salvador,San Salvador,North America
+59,Equatorial Guinea,Malabo,Africa
+60,Eritrea,Asmara,Africa
+61,Estonia,Tallinn,Europe
+62,Eswatini,Mbabane,Africa
+63,Ethiopia,Addis Ababa,Africa
+64,Falkland Islands,Stanley,South America
+65,Faroe Islands,Trshavn,Europe
+66,Fiji,Suva,Oceania
+67,Finland,Helsinki,Europe
+68,France,Paris,Europe
+69,French Guiana,Cayenne,South America
+70,French Polynesia,Papeete,Oceania
+71,Gabon,Libreville,Africa
+72,Gambia,Banjul,Africa
+73,Georgia,Tbilisi,Asia
+74,Germany,Berlin,Europe
+75,Ghana,Accra,Africa
+76,Gibraltar,Gibraltar,Europe
+77,Greece,Athens,Europe
+78,Greenland,Nuuk,North America
+79,Grenada,Saint George's,North America
+80,Guadeloupe,Basse-Terre,North America
+81,Guam,Hagta,Oceania
+82,Guatemala,Guatemala City,North America
+83,Guernsey,Saint Peter Port,Europe
+84,Guinea,Conakry,Africa
+85,Guinea-Bissau,Bissau,Africa
+86,Guyana,Georgetown,South America
+87,Haiti,Port-au-Prince,North America
+88,Honduras,Tegucigalpa,North America
+89,Hong Kong,Hong Kong,Asia
+90,Hungary,Budapest,Europe
+91,Iceland,Reykjavk,Europe
+92,India,New Delhi,Asia
+93,Indonesia,Jakarta,Asia
+94,Iran,Tehran,Asia
+95,Iraq,Baghdad,Asia
+96,Ireland,Dublin,Europe
+97,Isle of Man,Douglas,Europe
+98,Israel,Jerusalem,Asia
+99,Italy,Rome,Europe
+100,Ivory Coast,Yamoussoukro,Africa
+101,Jamaica,Kingston,North America
+102,Japan,Tokyo,Asia
+103,Jersey,Saint Helier,Europe
+104,Jordan,Amman,Asia
+105,Kazakhstan,Nursultan,Asia
+106,Kenya,Nairobi,Africa
+107,Kiribati,Tarawa,Oceania
+108,Kuwait,Kuwait City,Asia
+109,Kyrgyzstan,Bishkek,Asia
+110,Laos,Vientiane,Asia
+111,Latvia,Riga,Europe
+112,Lebanon,Beirut,Asia
+113,Lesotho,Maseru,Africa
+114,Liberia,Monrovia,Africa
+115,Libya,Tripoli,Africa
+116,Liechtenstein,Vaduz,Europe
+117,Lithuania,Vilnius,Europe
+118,Luxembourg,Luxembourg,Europe
+119,Macau,Concelho de Macau,Asia
+120,Madagascar,Antananarivo,Africa
+122,Malaysia,Kuala Lumpur,Asia
+123,Maldives,Mal??,Asia
+124,Mali,Bamako,Africa
+125,Malta,Valletta,Europe
+126,Marshall Islands,Majuro,Oceania
+128,Mauritania,Nouakchott,Africa
+129,Mauritius,Port Louis,Africa
+130,Mayotte,Mamoudzou,Africa
+131,Mexico,Mexico City,North America
+132,Micronesia,Palikir,Oceania
+133,Moldova,Chisinau,Europe
+134,Monaco,Monaco,Europe
+135,Mongolia,Ulaanbaatar,Asia
+136,Montenegro,Podgorica,Europe
+137,Montserrat,Brades,North America
+138,Morocco,Rabat,Africa
+139,Mozambique,Maputo,Africa
+140,Myanmar,Nay Pyi Taw,Asia
+141,Namibia,Windhoek,Africa
+142,Nauru,Yaren,Oceania
+143,Nepal,Kathmandu,Asia
+144,Netherlands,Amsterdam,Europe
+145,New Caledonia,Noum??a,Oceania
+146,New Zealand,Wellington,Oceania
+147,Nicaragua,Managua,North America
+148,Niger,Niamey,Africa
+149,Nigeria,Abuja,Africa
+150,Niue,Alofi,Oceania
+151,North Korea,Pyongyang,Asia
+152,North Macedonia,Skopje,Europe
+153,Northern Mariana Islands,Saipan,Oceania
+154,Norway,Oslo,Europe
+155,Oman,Muscat,Asia
+156,Pakistan,Islamabad,Asia
+157,Palau,Ngerulmud,Oceania
+158,Palestine,Ramallah,Asia
+159,Panama,Panama City,North America
+160,Papua New Guinea,Port Moresby,Oceania
+161,Paraguay,Asunci??n,South America
+162,Peru,Lima,South America
+163,Philippines,Manila,Asia
+164,Poland,Warsaw,Europe
+165,Portugal,Lisbon,Europe
+166,Puerto Rico,San Juan,North America
+167,Qatar,Doha,Asia
+168,Republic of the Congo,Brazzaville,Africa
+169,Reunion,Saint-Denis,Africa
+170,Romania,Bucharest,Europe
+171,Russia,Moscow,Europe
+172,Rwanda,Kigali,Africa
+173,Saint Barthelemy,Gustavia,North America
+174,Saint Kitts and Nevis,Basseterre,North America
+175,Saint Lucia,Castries,North America
+176,Saint Martin,Marigot,North America
+177,Saint Pierre and Miquelon,Saint-Pierre,North America
+178,Saint Vincent and the Grenadines,Kingstown,North America
+179,Samoa,Apia,Oceania
+180,San Marino,San Marino,Europe
+181,Sao Tome and Principe,So Tom,Africa
+182,Saudi Arabia,Riyadh,Asia
+183,Senegal,Dakar,Africa
+184,Serbia,Belgrade,Europe
+185,Seychelles,Victoria,Africa
+186,Sierra Leone,Freetown,Africa
+187,Singapore,Singapore,Asia
+188,Sint Maarten,Philipsburg,North America
+189,Slovakia,Bratislava,Europe
+190,Slovenia,Ljubljana,Europe
+191,Solomon Islands,Honiara,Oceania
+192,Somalia,Mogadishu,Africa
+193,South Africa,Pretoria,Africa
+194,South Korea,Seoul,Asia
+195,South Sudan,Juba,Africa
+196,Spain,Madrid,Europe
+197,Sri Lanka,Colombo,Asia
+198,Sudan,Khartoum,Africa
+199,Suriname,Paramaribo,South America
+200,Sweden,Stockholm,Europe
+201,Switzerland,Bern,Europe
+202,Syria,Damascus,Asia
+203,Taiwan,Taipei,Asia
+204,Tajikistan,Dushanbe,Asia
+205,Tanzania,Dodoma,Africa
+206,Thailand,Bangkok,Asia
+207,Timor-Leste,Dili,Asia
+208,Togo,Lom,Africa
+209,Tokelau,Nukunonu,Oceania
+210,Tonga,Nukualofa,Oceania
+211,Trinidad and Tobago,Port-of-Spain,North America
+212,Tunisia,Tunis,Africa
+213,Turkey,Ankara,Asia
+214,Turkmenistan,Ashgabat,Asia
+215,Turks and Caicos Islands,Cockburn Town,North America
+216,Tuvalu,Funafuti,Oceania
+217,Uganda,Kampala,Africa
+218,Ukraine,Kiev,Europe
+219,United Arab Emirates,Abu Dhabi,Asia
+220,United Kingdom,London,Europe
+221,United States,"Washington, D.C.",North America
+222,United States Virgin Islands,Charlotte Amalie,North America
+223,Uruguay,Montevideo,South America
+224,Uzbekistan,Tashkent,Asia
+225,Vanuatu,Port-Vila,Oceania
+226,Vatican City,Vatican City,Europe
+227,Venezuela,Caracas,South America
+228,Vietnam,Hanoi,Asia
+229,Wallis and Futuna,Mata-Utu,Oceania
+230,Western Sahara,El Aain,Africa
+231,Yemen,Sanaa,Asia
+232,Zambia,Lusaka,Africa
+233,Zimbabwe,Harare,Africa

transformers.py

@@ -0,0 +1,27 @@
+import numpy as np
+import pandas as pd
+from sklearn.base import BaseEstimator, TransformerMixin
+
+
+class TitanicFeatures(BaseEstimator, TransformerMixin):
+    def __init__(self):
+        pass
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X, y=None):
+        def get_title(name) -> str:
+            return name.split(",")[1].split(".")[0].strip()
+
+        def get_cabin_type(cabin) -> str:
+            if pd.isna(cabin):
+                return "unknown"
+            return cabin[0]
+
+        X["Is_married"] = [1 if get_title(name) == "Mrs" else 0 for name in X["Name"]]
+        X["Cabin_type"] = [get_cabin_type(cabin) for cabin in X["Cabin"]]
+        return X
+
+    def get_feature_names_out(self, features_in):
+        return np.append(features_in, ["Is_married", "Cabin_type"], axis=0)

utils.py

@@ -0,0 +1,82 @@
+from typing import Tuple
+
+import pandas as pd
+from pandas import DataFrame
+from sklearn.model_selection import train_test_split
+
+
+def split_stratified_into_train_val_test(
+    df_input,
+    target_colname="z",
+    stratify_colname="y",
+    frac_train=0.6,
+    frac_val=0.15,
+    frac_test=0.25,
+    random_state=None,
+) -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:
+    """
+    Splits a Pandas dataframe into three subsets (train, val, and test)
+    following fractional ratios provided by the user, where each subset is
+    stratified by the values in a specific column (that is, each subset has
+    the same relative frequency of the values in the column). It performs this
+    splitting by running train_test_split() twice.
+
+    Parameters
+    ----------
+    df_input : Pandas dataframe
+        Input dataframe to be split.
+    stratify_colname : str
+        The name of the column that will be used for stratification. Usually
+        this column would be for the label.
+    frac_train : float
+    frac_val   : float
+    frac_test  : float
+        The ratios with which the dataframe will be split into train, val, and
+        test data. The values should be expressed as float fractions and should
+        sum to 1.0.
+    random_state : int, None, or RandomStateInstance
+        Value to be passed to train_test_split().
+
+    Returns
+    -------
+    df_train, df_val, df_test :
+        Dataframes containing the three splits.
+    """
+
+    if frac_train + frac_val + frac_test != 1.0:
+        raise ValueError(
+            "fractions %f, %f, %f do not add up to 1.0"
+            % (frac_train, frac_val, frac_test)
+        )
+
+    if stratify_colname not in df_input.columns:
+        raise ValueError("%s is not a column in the dataframe" % (stratify_colname))
+
+    if target_colname not in df_input.columns:
+        raise ValueError("%s is not a column in the dataframe" % (target_colname))
+
+    X = df_input  # Contains all columns.
+    y = df_input[[target_colname]]  # Dataframe of just the column on which to stratify.
+    z = df_input[[stratify_colname]]
+
+    # Split original dataframe into train and temp dataframes.
+    df_train, df_temp, y_train, y_temp = train_test_split(
+        X, y, stratify=z, test_size=(1.0 - frac_train), random_state=random_state
+    )
+
+    if frac_val <= 0:
+        assert len(df_input) == len(df_train) + len(df_temp)
+        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp
+
+    # Split the temp dataframe into val and test dataframes.
+    relative_frac_test = frac_test / (frac_val + frac_test)
+    df_val, df_test, y_val, y_test = train_test_split(
+        df_temp,
+        y_temp,
+        stratify=df_temp[[stratify_colname]],
+        test_size=relative_frac_test,
+        random_state=random_state,
+    )
+
+    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)
+    return df_train, df_val, df_test, y_train, y_val, y_test

utils_clusters.py

@@ -0,0 +1,100 @@
+import math
+from typing import Dict, List, Tuple
+
+import numpy as np
+from pandas import DataFrame
+from sklearn import cluster
+from sklearn.metrics import silhouette_samples, silhouette_score
+
+
+def run_agglomerative(
+    df: DataFrame, num_clusters: int | None = 2
+) -> cluster.AgglomerativeClustering:
+    agglomerative = cluster.AgglomerativeClustering(
+        n_clusters=num_clusters,
+        compute_distances=True,
+    )
+    return agglomerative.fit(df)
+
+
+def get_linkage_matrix(model: cluster.AgglomerativeClustering) -> np.ndarray:
+    counts = np.zeros(model.children_.shape[0])  # type: ignore
+    n_samples = len(model.labels_)
+    for i, merge in enumerate(model.children_):  # type: ignore
+        current_count = 0
+        for child_idx in merge:
+            if child_idx < n_samples:
+                current_count += 1
+            else:
+                current_count += counts[child_idx - n_samples]
+        counts[i] = current_count
+
+    return np.column_stack([model.children_, model.distances_, counts]).astype(float)
+
+
+def print_cluster_result(
+    df: DataFrame, clusters_num: int, labels: np.ndarray, separator: str = ", "
+):
+    for cluster_id in range(clusters_num):
+        cluster_indices = np.where(labels == cluster_id)[0]
+        print(f"Cluster {cluster_id + 1} ({len(cluster_indices)}):")
+        rules = [str(df.index[idx]) for idx in cluster_indices]
+        print(separator.join(rules))
+        print("")
+        print("--------")
+
+
+def run_kmeans(
+    df: DataFrame, num_clusters: int, random_state: int
+) -> Tuple[np.ndarray, np.ndarray]:
+    kmeans = cluster.KMeans(n_clusters=num_clusters, random_state=random_state)
+    labels = kmeans.fit_predict(df)
+    return labels, kmeans.cluster_centers_
+
+
+def fit_kmeans(
+    reduced_data: np.ndarray, num_clusters: int, random_state: int
+) -> cluster.KMeans:
+    kmeans = cluster.KMeans(n_clusters=num_clusters, random_state=random_state)
+    kmeans.fit(reduced_data)
+    return kmeans
+
+
+def _get_kmeans_range(
+    df: DataFrame | np.ndarray, random_state: int
+) -> Tuple[List, range]:
+    max_clusters = int(math.sqrt(len(df)))
+    clusters_range = range(2, max_clusters + 1)
+    kmeans_per_k = [
+        cluster.KMeans(n_clusters=k, random_state=random_state).fit(df)
+        for k in clusters_range
+    ]
+    return kmeans_per_k, clusters_range
+
+
+def get_clusters_inertia(df: DataFrame, random_state: int) -> Tuple[List, range]:
+    kmeans_per_k, clusters_range = _get_kmeans_range(df, random_state)
+    return [model.inertia_ for model in kmeans_per_k], clusters_range
+
+
+def get_clusters_silhouette_scores(
+    df: DataFrame, random_state: int
+) -> Tuple[List, range]:
+    kmeans_per_k, clusters_range = _get_kmeans_range(df, random_state)
+    return [
+        float(silhouette_score(df, model.labels_)) for model in kmeans_per_k
+    ], clusters_range
+
+
+def get_clusters_silhouettes(df: np.ndarray, random_state: int) -> Dict:
+    kmeans_per_k, _ = _get_kmeans_range(df, random_state)
+    clusters_silhouettes: Dict = {}
+    for model in kmeans_per_k:
+        silhouette_value = silhouette_score(df, model.labels_)
+        sample_silhouette_values = silhouette_samples(df, model.labels_)
+        clusters_silhouettes[model.n_clusters] = (
+            silhouette_value,
+            sample_silhouette_values,
+            model,
+        )
+    return clusters_silhouettes

visual.py

@@ -0,0 +1,242 @@
+from typing import Any, Dict, List
+
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+import numpy as np
+from pandas import DataFrame
+from scipy.cluster import hierarchy
+from sklearn.cluster import KMeans
+
+
+def draw_data_2d(
+    df: DataFrame,
+    col1: int,
+    col2: int,
+    y: List | None = None,
+    classes: List | None = None,
+    subplot: Any | None = None,
+):
+    ax = None
+    if subplot is None:
+        _, ax = plt.subplots()
+    else:
+        ax = subplot
+    scatter = ax.scatter(df[df.columns[col1]], df[df.columns[col2]], c=y)
+    ax.set(xlabel=df.columns[col1], ylabel=df.columns[col2])
+    if classes is not None:
+        ax.legend(
+            scatter.legend_elements()[0], classes, loc="lower right", title="Classes"
+        )
+
+
+def draw_dendrogram(linkage_matrix: np.ndarray):
+    hierarchy.dendrogram(linkage_matrix, truncate_mode="level", p=3)
+
+
+def draw_cluster_results(
+    df: DataFrame,
+    col1: int,
+    col2: int,
+    labels: np.ndarray,
+    cluster_centers: np.ndarray,
+    subplot: Any | None = None,
+):
+    ax = None
+    if subplot is None:
+        ax = plt
+    else:
+        ax = subplot
+
+    centroids = cluster_centers
+    u_labels = np.unique(labels)
+
+    for i in u_labels:
+        ax.scatter(
+            df[labels == i][df.columns[col1]],
+            df[labels == i][df.columns[col2]],
+            label=i,
+        )
+
+    ax.scatter(centroids[:, col1], centroids[:, col2], s=80, color="k")
+
+
+def draw_clusters(reduced_data: np.ndarray, kmeans: KMeans):
+    h = 0.02
+
+    x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
+    y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+
+    Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
+
+    Z = Z.reshape(xx.shape)
+    plt.figure(1)
+    plt.clf()
+    plt.imshow(
+        Z,
+        interpolation="nearest",
+        extent=(xx.min(), xx.max(), yy.min(), yy.max()),
+        cmap=plt.cm.Paired,  # type: ignore
+        aspect="auto",
+        origin="lower",
+    )
+
+    plt.plot(reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2)
+    centroids = kmeans.cluster_centers_
+    plt.scatter(
+        centroids[:, 0],
+        centroids[:, 1],
+        marker="x",
+        s=169,
+        linewidths=3,
+        color="w",
+        zorder=10,
+    )
+    plt.title(
+        "K-means clustering (PCA-reduced data)\n"
+        "Centroids are marked with white cross"
+    )
+    plt.xlim(x_min, x_max)
+    plt.ylim(y_min, y_max)
+    plt.xticks(())
+    plt.yticks(())
+
+
+def _draw_cluster_scores(
+    data: List,
+    clusters_range: range,
+    score_name: str,
+    title: str,
+):
+    plt.figure(figsize=(8, 5))
+    plt.plot(clusters_range, data, "bo-")
+    plt.xlabel("$k$", fontsize=8)
+    plt.ylabel(score_name, fontsize=8)
+    plt.title(title)
+
+
+def draw_elbow_diagram(inertias: List, clusters_range: range):
+    _draw_cluster_scores(inertias, clusters_range, "Inertia", "The Elbow Diagram")
+
+
+def draw_silhouettes_diagram(silhouette: List, clusters_range: range):
+    _draw_cluster_scores(
+        silhouette, clusters_range, "Silhouette score", "The Silhouette score"
+    )
+
+
+def _draw_silhouette(
+    ax: Any,
+    reduced_data: np.ndarray,
+    n_clusters: int,
+    silhouette_avg: float,
+    sample_silhouette_values: List,
+    cluster_labels: List,
+):
+    ax.set_xlim([-0.1, 1])
+    ax.set_ylim([0, len(reduced_data) + (n_clusters + 1) * 10])
+
+    y_lower = 10
+    for i in range(n_clusters):
+        ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]
+
+        ith_cluster_silhouette_values.sort()
+
+        size_cluster_i = ith_cluster_silhouette_values.shape[0]
+        y_upper = y_lower + size_cluster_i
+
+        color = cm.nipy_spectral(float(i) / n_clusters)  # type: ignore
+        ax.fill_betweenx(
+            np.arange(y_lower, y_upper),
+            0,
+            ith_cluster_silhouette_values,
+            facecolor=color,
+            edgecolor=color,
+            alpha=0.7,
+        )
+
+        ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
+
+        y_lower = y_upper + 10  # 10 for the 0 samples
+
+    ax.set_title("The silhouette plot for the various clusters.")
+    ax.set_xlabel("The silhouette coefficient values")
+    ax.set_ylabel("Cluster label")
+
+    ax.axvline(x=silhouette_avg, color="red", linestyle="--")
+
+    ax.set_yticks([])
+    ax.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
+
+
+def _draw_cluster_data(
+    ax: Any,
+    reduced_data: np.ndarray,
+    n_clusters: int,
+    cluster_labels: np.ndarray,
+    cluster_centers: np.ndarray,
+):
+    colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)  # type: ignore
+    ax.scatter(
+        reduced_data[:, 0],
+        reduced_data[:, 1],
+        marker=".",
+        s=30,
+        lw=0,
+        alpha=0.7,
+        c=colors,
+        edgecolor="k",
+    )
+
+    ax.scatter(
+        cluster_centers[:, 0],
+        cluster_centers[:, 1],
+        marker="o",
+        c="white",
+        alpha=1,
+        s=200,
+        edgecolor="k",
+    )
+
+    for i, c in enumerate(cluster_centers):
+        ax.scatter(c[0], c[1], marker="$%d$" % i, alpha=1, s=50, edgecolor="k")
+
+    ax.set_title("The visualization of the clustered data.")
+    ax.set_xlabel("Feature space for the 1st feature")
+    ax.set_ylabel("Feature space for the 2nd feature")
+
+
+def draw_silhouettes(reduced_data: np.ndarray, silhouettes: Dict):
+    for key, value in silhouettes.items():
+        fig, (ax1, ax2) = plt.subplots(1, 2)
+        fig.set_size_inches(18, 7)
+
+        n_clusters = key
+        silhouette_avg = value[0]
+        sample_silhouette_values = value[1]
+        cluster_labels = value[2].labels_
+        cluster_centers = value[2].cluster_centers_
+
+        _draw_silhouette(
+            ax1,
+            reduced_data,
+            n_clusters,
+            silhouette_avg,
+            sample_silhouette_values,
+            cluster_labels,
+        )
+
+        _draw_cluster_data(
+            ax2,
+            reduced_data,
+            n_clusters,
+            cluster_labels,
+            cluster_centers,
+        )
+
+        plt.suptitle(
+            "Silhouette analysis for KMeans clustering on sample data with n_clusters = %d"
+            % n_clusters,
+            fontsize=14,
+            fontweight="bold",
+        )