lab1 done

.flake8

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+[flake8]
+max-line-length = 120

.gitattributes

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+* text=crlf

.gitignore

@@ -0,0 +1,278 @@
+
+# Created by https://www.toptal.com/developers/gitignore/api/python,pycharm+all
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,pycharm+all
+
+### 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
+
+# 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]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  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 +1 @@
-# ISEbd-31_Baygulov_A.A._MAI
-
+Лабораторные работы по МИИ

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

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data/orders/customers.csv

@@ -0,0 +1,101 @@
+customer_id,customer_unique_id,customer_zip_code_prefix,customer_city,customer_state
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data/orders/order_items.csv

@@ -0,0 +1,116 @@
+order_id,order_item_id,product_id,seller_id,shipping_limit_date,price,freight_value
+0a4a2fccb27bd83a892fa503987a595b,1,f7d7b5c58704fb359a74580622800051,4a3ca9315b744ce9f8e9374361493884,2017-04-28 20:55:09,38.5,24.84
+0e782c3705510e717d28907746cbda82,1,79da264732f717f10ebf5d102aa6c32a,562fc2f2c2863ab7e79a9e4388a58a14,2018-05-07 08:52:58,29.99,7.39
+10c320f977c6a18f91b2d14be13128c6,1,b3be1f83cef05668c25e134852d44545,3b15288545f8928d3e65a8f949a28291,2017-05-16 21:02:45,110.99,21.27
+116f0b09343b49556bbad5f35bee0cdf,1,a47295965bd091207681b541b26e40a5,ea8482cd71df3c1969d7b9473ff13abc,2018-01-02 23:50:22,27.99,15.1
+136cce7faa42fdb2cefd53fdc79a6098,1,a1804276d9941ac0733cfd409f5206eb,dc8798cbf453b7e0f98745e396cc5616,2017-04-19 13:25:17,49.9,16.05
+138849fd84dff2fb4ca70a0a34c4aa1c,1,304fad8dc4d2012dc4062839972f2d96,6860153b69cc696d5dcfe1cdaaafcf62,2018-02-08 02:53:07,39.47,13.37
+1790eea0b567cf50911c057cf20f90f9,1,2d8f2be4f08788ee3bf5356af2b2ee6c,d91fb3b7d041e83b64a00a3edfb37e4f,2018-04-22 22:10:26,186.9,38.0
+1e7aff52cdbb2451ace09d0f848c3699,1,8c591ab0ca519558779df02023177f44,a1043bafd471dff536d0c462352beb48,2017-05-25 19:05:17,119.99,34.2
+203096f03d82e0dffbc41ebc2e2bcfb7,1,5ac9d9e379c606e36a8094a6046f75dc,633ecdf879b94b5337cca303328e4a25,2017-09-25 04:04:09,109.9,8.96
+20e0101b20700188cadb288126949685,1,64d0feb1bcf9c7fe7b5dad3271c10910,e5a38146df062edaf55c38afa99e42dc,2018-01-26 19:36:35,89.18,16.38
+23f553848a03aaab35bb3f9f87725125,1,cac9e5692471a0700418aa3400b9b2b1,36890be00bbfc1cdb9a4a38a6af05a69,2018-06-15 09:31:23,99.2,18.57
+25f4376934e13d3508486352e11a5db0,1,aca2eb7d00ea1a7b8ebd4e68314663af,955fee9216a65b617aa5c0531780ce60,2018-05-22 01:17:39,69.9,12.43
+2711a938db643b3f0b62ee2c8a2784aa,1,ad1128daf194f4b6ac4256e16233497c,1ca7077d890b907f89be8c954a02686a,2017-12-29 02:15:31,45.0,14.1
+2807d0e504d6d4894d41672727bc139f,1,6893767814d1ac82a81bcd365e1cc918,8b321bb669392f5163d04c59e235e066,2018-02-08 20:50:22,9.5,7.78
+2ce1ad82022c1ba30c2079502ac725aa,1,f35927953ed82e19d06ad3aac2f06353,669ae81880e08f269a64487cfb287169,2017-08-17 04:15:29,115.0,15.56
+2edfd6d1f0b4cd0db4bf37b1b224d855,1,30469bb5ea377eae7121981e2f0778e4,80e6699fe29150b372a0c8a1ebf7dcc8,2017-06-21 03:05:45,113.0,28.15
+34513ce0c4fab462a55830c0989c7edb,1,f7e0fa615b386bc9a8b9eb52bc1fff76,87142160b41353c4e5fca2360caf6f92,2017-07-19 20:10:08,98.0,16.13
+3bc77ce8be27211bac313c2daa402d1a,1,f497ba62f1d6b4f6a3a3266fa8623ad3,6df688df543f90e9b38f4319e75a9d88,2017-04-12 22:50:24,58.2,8.78
+403b97836b0c04a622354cf531062e5f,1,638bbb2a5e4f360b71f332ddfebfd672,c4af86330efa7a2620772227d2d670c9,2018-01-12 19:09:04,1299.0,77.45
+40c5e18f7d112b59b3e5113a59a905b3,1,595fac2a385ac33a80bd5114aec74eb8,ef0ace09169ac090589d85746e3e036f,2018-06-15 10:58:32,119.9,8.78
+41bb5cee06dbf170878a9ef93ac7e7f5,1,43ee88561093499d9e571d4db5f20b79,23613d49c3ac2bd302259e55c06c050c,2018-05-28 08:52:24,10.9,12.79
+432aaf21d85167c2c86ec9448c4e42cc,1,72d3bf1d3a790f8874096fcf860e3eff,0bae85eb84b9fb3bd773911e89288d54,2018-03-07 15:10:47,38.25,16.11
+434d158e96bdd6972ad6e6d73ddcfd22,1,c7df652246ed7b3300aaf46960c141e4,a5cba26a62b8b4d0145b68b841e62e7f,2018-06-13 03:35:15,445.0,63.17
+47770eb9100c2d0c44946d9cf07ec65d,1,aa4383b373c6aca5d8797843e5594415,4869f7a5dfa277a7dca6462dcf3b52b2,2018-08-13 08:55:23,159.9,19.22
+47aa4816b27ba60ec948cd019cc1afc1,1,1501b0033c68a37fa9560033a440e529,33cbbec1e7e1044aaf11d152172c776f,2018-06-29 03:31:40,53.44,18.47
+53cdb2fc8bc7dce0b6741e2150273451,1,595fac2a385ac33a80bd5114aec74eb8,289cdb325fb7e7f891c38608bf9e0962,2018-07-30 03:24:27,118.7,22.76
+5820a1100976432c7968a52da59e9364,1,1deda1acffb44ed38494667d7e49a9f3,f52c2422904463fdd7741f99045fecb6,2018-07-31 11:44:19,33.9,18.34
+5acce57f8d9dfd55fa48e212a641a69d,1,0cd9f302c8a5b076ffa5c3567c6705fd,85d9eb9ddc5d00ca9336a2219c97bb13,2017-08-08 02:56:02,27.9,15.1
+5ff96c15d0b717ac6ad1f3d77225a350,1,10adb53d8faa890ca7c2f0cbcb68d777,1900267e848ceeba8fa32d80c1a5f5a8,2018-07-27 17:55:14,19.9,12.8
+60550084e6b4c0cb89a87df1f3e5ebd9,1,9b37a918bcf2c8e1064e867cf1df4637,f27e33c6d29b5138fa9967bcd445b6d5,2018-03-01 02:10:52,39.9,26.89
+634e8f4c0f6744a626f77f39770ac6aa,1,69d980b4120a76616d7b237d731d6156,744dac408745240a2c2528fb1b6028f3,2017-08-15 18:45:18,219.0,15.28
+641fb0752bf5b5940c376b3a8bb9dc52,1,60184212dae4e6b0da32bf54271a8c4a,b33e7c55446eabf8fe1a42d037ac7d6d,2017-12-21 00:14:55,369.0,17.33
+6514b8ad8028c9f2cc2374ded245783f,1,4520766ec412348b8d4caa5e8a18c464,16090f2ca825584b5a147ab24aa30c86,2017-05-22 13:22:11,59.99,15.17
+66e4624ae69e7dc89bd50222b59f581f,1,b37b72d5a56f887725c2862184b8cab8,db4350fd57ae30082dec7acbaacc17f9,2018-03-15 15:30:45,22.99,22.85
+66e4624ae69e7dc89bd50222b59f581f,2,b37b72d5a56f887725c2862184b8cab8,db4350fd57ae30082dec7acbaacc17f9,2018-03-15 15:30:45,22.99,22.85
+686541986ecfb7d9296eb67719973bf0,1,3014e35fd70fce29095ced5cdc89f4ce,5656537e588803a555b8eb41f07a944b,2018-02-15 13:35:31,24.89,15.1
+688052146432ef8253587b930b01a06d,1,d1c427060a0f73f6b889a5c7c61f2ac4,a1043bafd471dff536d0c462352beb48,2018-04-26 09:31:11,119.0,24.97
+688052146432ef8253587b930b01a06d,2,db56f6d2b04c89eae4daba188842fd7b,2a84855fd20af891be03bc5924d2b453,2018-04-26 09:31:11,199.0,3.12
+68873cf91053cd11e6b49a766db5af1a,1,15a9e834e89eab39d973492882c658d6,a673821011d0cec28146ea42f5ab767f,2017-12-07 02:51:18,79.9,11.76
+68e48e68da1f50f7c5838ea75e3a20dd,1,a659cb33082b851fb87a33af8f0fff29,817245bcc3badd82bbd222e0366951a6,2018-06-22 17:00:57,84.9,13.25
+68e48e68da1f50f7c5838ea75e3a20dd,2,a659cb33082b851fb87a33af8f0fff29,817245bcc3badd82bbd222e0366951a6,2018-06-22 17:00:57,84.9,13.25
+68e48e68da1f50f7c5838ea75e3a20dd,3,a659cb33082b851fb87a33af8f0fff29,817245bcc3badd82bbd222e0366951a6,2018-06-22 17:00:57,84.9,13.25
+68e48e68da1f50f7c5838ea75e3a20dd,4,a659cb33082b851fb87a33af8f0fff29,817245bcc3badd82bbd222e0366951a6,2018-06-22 17:00:57,84.9,13.25
+6a0a8bfbbe700284feb0845d95e0867f,1,f8a8f05a35976a91aed5cccc3992c357,4a3ca9315b744ce9f8e9374361493884,2017-11-28 11:46:50,83.9,17.84
+6abaad69b8b349c3a529b4b91ce18e46,1,3dd6c9d499e7c311a29e08afe1fd8fc6,537eb890efff034a88679788b647c564,2018-02-21 09:47:59,42.9,14.1
+6b860b35691d486e45dc98e3514ec5f6,1,c827fb43ad0fb8708f34c2911fdc164b,76d5af76d0271110f9af36c92573f765,2017-12-14 02:49:54,544.0,30.36
+6d25592267349b322799e2beb687871e,1,c3ba4e8d3cb30049213b682e751e9d00,6560211a19b47992c3666cc44a7e94c0,2018-08-30 04:10:18,93.0,7.91
+6d25592267349b322799e2beb687871e,2,c3ba4e8d3cb30049213b682e751e9d00,6560211a19b47992c3666cc44a7e94c0,2018-08-30 04:10:18,93.0,7.91
+6ea2f835b4556291ffdc53fa0b3b95e8,1,be021417a6acb56b9b50d3fd2714baa8,f5f46307a4d15880ca14fab4ad9dfc9b,2017-11-30 00:21:09,339.0,17.12
+6ebaec694d7025e2ad4a05dba887c032,1,e251ebd2858be1aa7d9b2087a6992580,001cca7ae9ae17fb1caed9dfb1094831,2017-05-24 14:05:17,139.0,14.72
+7206b86ea789983f7a273ea7fa0bc2a8,1,9a469eaf45dfbc43d39ba1977a3c07af,d2374cbcbb3ca4ab1086534108cc3ab7,2018-03-30 17:27:57,36.9,12.79
+734e7d1bbaeb2ff82521ca0fe6fb6f79,1,278b3c6462e86b4556b99989513ddf73,d1ef48b38baca7e831711c4a0aeb398f,2018-06-13 08:31:12,29.99,13.47
+76c6e866289321a7c93b82b54852dc33,1,ac1789e492dcd698c5c10b97a671243a,63b9ae557efed31d1f7687917d248a8d,2017-01-27 18:29:09,19.9,16.05
+77e9941864fc840be8e4b1ba5347c0f7,1,a01d1cbb398e386a4a8f8364401a7584,d566c37fa119d5e66c4e9052e83ee4ea,2018-08-07 09:10:14,65.9,37.37
+82566a660a982b15fb86e904c8d32918,1,72a97c271b2e429974398f46b93ae530,094ced053e257ae8cae57205592d6712,2018-06-18 03:13:12,31.9,18.23
+82bce245b1c9148f8d19a55b9ff70644,1,a5a0e71a81ae65aa335e71c06261e260,c8417879a15366a17c30af34c798c332,2017-04-27 05:15:56,38.0,15.56
+82bce245b1c9148f8d19a55b9ff70644,2,a5a0e71a81ae65aa335e71c06261e260,c8417879a15366a17c30af34c798c332,2017-04-27 05:15:56,38.0,15.56
+82bce245b1c9148f8d19a55b9ff70644,3,a5a0e71a81ae65aa335e71c06261e260,c8417879a15366a17c30af34c798c332,2017-04-27 05:15:56,38.0,15.56
+82bce245b1c9148f8d19a55b9ff70644,4,a5a0e71a81ae65aa335e71c06261e260,c8417879a15366a17c30af34c798c332,2017-04-27 05:15:56,38.0,15.56
+82bce245b1c9148f8d19a55b9ff70644,5,a5a0e71a81ae65aa335e71c06261e260,c8417879a15366a17c30af34c798c332,2017-04-27 05:15:56,38.0,15.56
+83018ec114eee8641c97e08f7b4e926f,1,c35498fbb4358837ae16850f50c3fd22,70a12e78e608ac31179aea7f8422044b,2017-11-01 16:07:35,76.0,16.97
+8447ff843b2616c50c0ced28ab1dae03,1,7a10781637204d8d10485c71a6108a2e,4869f7a5dfa277a7dca6462dcf3b52b2,2017-12-29 02:37:45,219.9,18.79
+8563039e855156e48fccee4d611a3196,1,bff2010b28e8fbcff5a9db9d3fea5ac4,955fee9216a65b617aa5c0531780ce60,2018-02-22 15:15:34,78.0,28.95
+85ce859fd6dc634de8d2f1e290444043,1,cce679660c66e6fbd5c8091dfd29e9cd,d2374cbcbb3ca4ab1086534108cc3ab7,2017-11-29 00:14:22,17.9,11.85
+86f21bf63784876b9fd6d35f46581d72,1,5526b1ae9ab2688cf600783cece249df,0b90b6df587eb83608a64ea8b390cf07,2018-04-23 22:49:32,98.44,22.4
+8f06cc6465925031568537b815f1198d,1,12087840651e83b48206b82c213b76fd,5b925e1d006e9476d738aa200751b73b,2017-11-21 11:46:42,299.0,18.34
+91b2a010e1e45e6ba3d133fa997597be,1,ba74c6b75d2ad7503175809688d5a03c,7d13fca15225358621be4086e1eb0964,2018-05-09 12:55:01,178.99,13.69
+948097deef559c742e7ce321e5e58919,1,cd935d283d47f1050c505e1c39c48b67,a3a38f4affed601eb87a97788c949667,2017-08-10 17:25:11,69.9,25.77
+949d5b44dbf5de918fe9c16f97b45f8a,1,d0b61bfb1de832b15ba9d266ca96e5b0,66922902710d126a0e7d26b0e3805106,2017-11-23 19:45:59,45.0,27.2
+95266dbfb7e20354baba07964dac78d5,1,bb7181410b4e02f93f3697f765db53c7,855668e0971d4dfd7bef1b6a4133b41b,2018-01-26 08:07:31,129.99,57.58
+974c1993ab8024d3ed16229183c2308d,1,5e2ba75ad255ff60b1c76c5bf526ae9b,f84a00e60c73a49e7e851c9bdca3a5bb,2017-02-24 11:45:39,69.9,14.66
+989225ba6d0ebd5873335f7e01de2ae7,1,6b64362e89896be7589621df54be089e,77530e9772f57a62c906e1c21538ab82,2017-12-20 13:54:13,49.0,14.1
+9defaf92cff22420e4e8ef7784815a55,1,cf944645d4ff2a3eed3ae17f641ea861,a6fe7de3d16f6149ffe280349a8535a0,2018-05-23 13:30:30,49.9,12.79
+9faeb9b2746b9d7526aef5acb08e2aa0,1,f48eb5c2fde13ca63664f0bb05f55346,f7ba60f8c3f99e7ee4042fdef03b70c4,2018-07-30 14:55:10,60.0,15.52
+9faeb9b2746b9d7526aef5acb08e2aa0,2,f48eb5c2fde13ca63664f0bb05f55346,f7ba60f8c3f99e7ee4042fdef03b70c4,2018-07-30 14:55:10,60.0,15.52
+a4591c265e18cb1dcee52889e2d8acc3,1,060cb19345d90064d1015407193c233d,8581055ce74af1daba164fdbd55a40de,2017-07-13 22:10:13,147.9,27.36
+a685d016c8a26f71a0bb67821070e398,1,ebd7c847c1e1cb69ec374ae0ebee1f4c,391fc6631aebcf3004804e51b40bcf1e,2017-03-17 18:14:36,84.9,14.36
+a6aeb116d2cb5013eb8a94585b71ffef,1,163e6400e6dadd0fe04775c5e9331fda,855668e0971d4dfd7bef1b6a4133b41b,2017-09-19 14:44:39,50.0,9.34
+a910f58086d58b3ae6f37aa712d377b9,1,75d6b6963340c6063f7f4cfcccfe6a30,cc419e0650a3c5ba77189a1882b7556a,2017-09-22 09:35:18,56.99,15.84
+a910f58086d58b3ae6f37aa712d377b9,2,75d6b6963340c6063f7f4cfcccfe6a30,cc419e0650a3c5ba77189a1882b7556a,2017-09-22 09:35:18,56.99,15.84
+acce194856392f074dbf9dada14d8d82,1,d70f38e7f79c630f8ea00c993897042c,977f9f63dd360c2a32ece2f93ad6d306,2018-06-13 00:35:10,90.9,48.64
+acce194856392f074dbf9dada14d8d82,2,9451e630d725c4bb7a5a206b48b99486,d673a59aac7a70d8b01e6902bf090a11,2018-06-13 00:35:10,39.5,48.64
+ad21c59c0840e6cb83a9ceb5573f8159,1,65266b2da20d04dbe00c5c2d3bb7859e,2c9e548be18521d1c43cde1c582c6de8,2018-02-19 20:31:37,19.9,8.72
+b276e4f8c0fb86bd82fce576f21713e0,1,c6c1f263e076bd9c1f1640250a5d0c29,fe2032dab1a61af8794248c8196565c9,2018-08-02 23:45:15,179.0,9.41
+b52cc4919de82b4d696a4380d10804a3,1,7564c1759c04fc0a38f2aa84f7a370ee,6860153b69cc696d5dcfe1cdaaafcf62,2018-06-19 02:30:26,42.99,12.03
+b8801cccd8068de30112e4f49903d74a,1,154e7e31ebfa092203795c972e5804a6,cc419e0650a3c5ba77189a1882b7556a,2017-08-08 03:25:08,19.99,7.78
+bd4bd0194d6d29f83b8557d4b89b572a,1,7f457254a89d62960399e075711b3deb,ea8482cd71df3c1969d7b9473ff13abc,2018-08-02 03:50:24,24.99,12.84
+cadbb3657dac2dbbd5b84b12e7b78aad,1,9d2ff462feaaf88912539b8647e17ab4,00fc707aaaad2d31347cf883cd2dfe10,2018-03-13 02:48:54,394.9,14.89
+ccbabeb0b02433bd0fcbac46e70339f2,1,89321f94e35fc6d7903d36f74e351d40,16090f2ca825584b5a147ab24aa30c86,2018-02-27 03:31:34,27.9,15.1
+d17dc4a904426827ca80f2ccb3a6be56,1,ba4bfbf74dbe7ab37e263b9326da0523,f8db351d8c4c4c22c6835c19a46f01b0,2017-05-18 20:42:45,36.9,17.92
+d22e9fa5731b9e30e8b27afcdc2f8563,1,f410090aec61f7c73748ca894286edcd,980640c45d7a4635885491d077167e4d,2018-08-07 23:35:13,99.0,22.62
+d3d6788577c9592da441752e8a1dd5e3,1,7c1bd920dbdf22470b68bde975dd3ccf,cc419e0650a3c5ba77189a1882b7556a,2017-09-27 07:55:14,58.99,17.66
+d887b52c6516beb39e8cd44a5f8b60f7,1,39a9942865c056ed2006a5e8c11d9537,ba5daa4041e1f15cdf34b76e3e18a450,2018-02-08 12:50:30,84.9,15.35
+dcb36b511fcac050b97cd5c05de84dc3,1,009c09f439988bc06a93d6b8186dce73,89a51f50b8095ea78d5768f34c13a76f,2018-06-18 18:59:02,132.4,14.05
+dd78f560c270f1909639c11b925620ea,1,00baba5b58e274d0332a0c8a0a66f877,d3f39f05462b79a4562d35893a28f159,2018-03-16 02:30:56,47.9,12.79
+e425680f760cbc130be3e53a9773c584,1,9ecadb84c81da840dbf3564378b586e9,1025f0e2d44d7041d6cf58b6550e0bfa,2017-09-08 08:30:17,38.4,11.85
+e481f51cbdc54678b7cc49136f2d6af7,1,87285b34884572647811a353c7ac498a,3504c0cb71d7fa48d967e0e4c94d59d9,2017-10-06 11:07:15,29.99,8.72
+e4de6d53ecff736bc68804b0b6e9f635,1,90b58782fdd04cb829667fcc41fb65f5,7c67e1448b00f6e969d365cea6b010ab,2017-10-27 03:49:34,179.99,51.13
+e69bfb5eb88e0ed6a785585b27e16dbf,1,9a78fb9862b10749a117f7fc3c31f051,7c67e1448b00f6e969d365cea6b010ab,2017-08-11 12:05:32,149.99,19.77
+e6ce16cb79ec1d90b1da9085a6118aeb,1,08574b074924071f4e201e151b152b4e,001cca7ae9ae17fb1caed9dfb1094831,2017-05-22 19:50:18,99.0,30.53
+e6ce16cb79ec1d90b1da9085a6118aeb,2,08574b074924071f4e201e151b152b4e,001cca7ae9ae17fb1caed9dfb1094831,2017-05-22 19:50:18,99.0,30.53
+ec341c54a5ebf8ee0a67a8632aa7579b,1,22f5b63060a1185e5ec7721efd622321,4c8b8048e33af2bf94f2eb547746a916,2017-08-31 17:04:12,14.82,15.1
+ecab90c9933c58908d3d6add7c6f5ae3,1,c0db539123a403f670c50237d970b215,f7720c4fa8e3aba4546301ab80ea1f1b,2018-03-01 14:28:03,30.1,33.24
+ee64d42b8cf066f35eac1cf57de1aa85,1,c50ca07e9e4db9ea5011f06802c0aea0,e9779976487b77c6d4ac45f75ec7afe9,2018-06-13 04:30:33,14.49,7.87
+f169bd689fb8b32ccd62df9050aebc0b,1,20a8603c265d777e25da064113d556f5,e70053bf73d1b5863932e53a9fa47496,2018-04-29 23:31:10,759.0,13.08
+f271576bed568e896f99eb710cd3a6f8,1,d457916b4fdc60154ed93b5dd3e6fd69,76d64c4aca3a7baf218bf93ef7fa768d,2018-01-11 21:51:28,329.9,82.48
+f271576bed568e896f99eb710cd3a6f8,2,d457916b4fdc60154ed93b5dd3e6fd69,76d64c4aca3a7baf218bf93ef7fa768d,2018-01-11 21:51:28,329.9,82.48
+f346ad4ee8f630e5e4ddaf862a34e6dd,1,4ce99ff9dcb7821acd8e599d5d4a6531,70125af26c2d6d4ef401a1d02ae7701f,2018-08-07 13:24:34,39.9,13.76
+f3e7c359154d965827355f39d6b1fdac,1,e99d69efe684efaa643f99805f7c81bc,55c26bcb609f480eb7868594245febb5,2018-08-14 03:24:51,89.9,14.21
+f70a0aff17df5a6cdd9a7196128bd354,1,cafd558df4c3c9d1c338ba6930ea9a62,5dceca129747e92ff8ef7a997dc4f8ca,2017-08-17 02:45:24,279.0,34.19
+f7959f8385f34c4f645327465a1c9fc4,1,c1234c80dafde7ef3311b3eabd5069ed,dc4a0fc896dc34b0d5bfec8438291c80,2017-04-11 08:05:08,17.9,10.96
+f848643eec1d69395095eb3840d2051e,1,2b4609f8948be18874494203496bc318,cc419e0650a3c5ba77189a1882b7556a,2018-03-23 09:09:31,79.99,8.91
+fa516182d28f96f5f5c651026b0749ee,1,e932008cf0ea7c93a077dd8d7e5f49eb,fcdd820084f17e9982427971e4e9d47f,2018-04-19 13:30:02,190.0,19.41
+fbf9ac61453ac646ce8ad9783d7d0af6,1,7b717060aa783eb7f23a747a3a733dd7,c0563dd588b775f2e37747ef6ad6c92c,2018-02-28 02:30:44,109.9,15.53
+fdf128b3630c21adc9ca4fb8a51b68ec,1,89321f94e35fc6d7903d36f74e351d40,16090f2ca825584b5a147ab24aa30c86,2018-07-18 14:31:10,27.9,18.3

data/orders/orders.csv

@@ -0,0 +1,101 @@
+order_id,customer_id,order_status,order_purchase_timestamp,order_approved_at,order_delivered_carrier_date,order_delivered_customer_date,order_estimated_delivery_date
+e481f51cbdc54678b7cc49136f2d6af7,9ef432eb6251297304e76186b10a928d,delivered,2017-10-02 10:56:33,2017-10-02 11:07:15,2017-10-04 19:55:00,2017-10-10 21:25:13,2017-10-18 00:00:00
+53cdb2fc8bc7dce0b6741e2150273451,b0830fb4747a6c6d20dea0b8c802d7ef,delivered,2018-07-24 20:41:37,2018-07-26 03:24:27,2018-07-26 14:31:00,2018-08-07 15:27:45,2018-08-13 00:00:00
+47770eb9100c2d0c44946d9cf07ec65d,41ce2a54c0b03bf3443c3d931a367089,delivered,2018-08-08 08:38:49,2018-08-08 08:55:23,2018-08-08 13:50:00,2018-08-17 18:06:29,2018-09-04 00:00:00
+949d5b44dbf5de918fe9c16f97b45f8a,f88197465ea7920adcdbec7375364d82,delivered,2017-11-18 19:28:06,2017-11-18 19:45:59,2017-11-22 13:39:59,2017-12-02 00:28:42,2017-12-15 00:00:00
+ad21c59c0840e6cb83a9ceb5573f8159,8ab97904e6daea8866dbdbc4fb7aad2c,delivered,2018-02-13 21:18:39,2018-02-13 22:20:29,2018-02-14 19:46:34,2018-02-16 18:17:02,2018-02-26 00:00:00
+a4591c265e18cb1dcee52889e2d8acc3,503740e9ca751ccdda7ba28e9ab8f608,delivered,2017-07-09 21:57:05,2017-07-09 22:10:13,2017-07-11 14:58:04,2017-07-26 10:57:55,2017-08-01 00:00:00
+136cce7faa42fdb2cefd53fdc79a6098,ed0271e0b7da060a393796590e7b737a,invoiced,2017-04-11 12:22:08,2017-04-13 13:25:17,,,2017-05-09 00:00:00
+6514b8ad8028c9f2cc2374ded245783f,9bdf08b4b3b52b5526ff42d37d47f222,delivered,2017-05-16 13:10:30,2017-05-16 13:22:11,2017-05-22 10:07:46,2017-05-26 12:55:51,2017-06-07 00:00:00
+76c6e866289321a7c93b82b54852dc33,f54a9f0e6b351c431402b8461ea51999,delivered,2017-01-23 18:29:09,2017-01-25 02:50:47,2017-01-26 14:16:31,2017-02-02 14:08:10,2017-03-06 00:00:00
+e69bfb5eb88e0ed6a785585b27e16dbf,31ad1d1b63eb9962463f764d4e6e0c9d,delivered,2017-07-29 11:55:02,2017-07-29 12:05:32,2017-08-10 19:45:24,2017-08-16 17:14:30,2017-08-23 00:00:00
+e6ce16cb79ec1d90b1da9085a6118aeb,494dded5b201313c64ed7f100595b95c,delivered,2017-05-16 19:41:10,2017-05-16 19:50:18,2017-05-18 11:40:40,2017-05-29 11:18:31,2017-06-07 00:00:00
+34513ce0c4fab462a55830c0989c7edb,7711cf624183d843aafe81855097bc37,delivered,2017-07-13 19:58:11,2017-07-13 20:10:08,2017-07-14 18:43:29,2017-07-19 14:04:48,2017-08-08 00:00:00
+82566a660a982b15fb86e904c8d32918,d3e3b74c766bc6214e0c830b17ee2341,delivered,2018-06-07 10:06:19,2018-06-09 03:13:12,2018-06-11 13:29:00,2018-06-19 12:05:52,2018-07-18 00:00:00
+5ff96c15d0b717ac6ad1f3d77225a350,19402a48fe860416adf93348aba37740,delivered,2018-07-25 17:44:10,2018-07-25 17:55:14,2018-07-26 13:16:00,2018-07-30 15:52:25,2018-08-08 00:00:00
+432aaf21d85167c2c86ec9448c4e42cc,3df704f53d3f1d4818840b34ec672a9f,delivered,2018-03-01 14:14:28,2018-03-01 15:10:47,2018-03-02 21:09:20,2018-03-12 23:36:26,2018-03-21 00:00:00
+dcb36b511fcac050b97cd5c05de84dc3,3b6828a50ffe546942b7a473d70ac0fc,delivered,2018-06-07 19:03:12,2018-06-12 23:31:02,2018-06-11 14:54:00,2018-06-21 15:34:32,2018-07-04 00:00:00
+403b97836b0c04a622354cf531062e5f,738b086814c6fcc74b8cc583f8516ee3,delivered,2018-01-02 19:00:43,2018-01-02 19:09:04,2018-01-03 18:19:09,2018-01-20 01:38:59,2018-02-06 00:00:00
+116f0b09343b49556bbad5f35bee0cdf,3187789bec990987628d7a9beb4dd6ac,delivered,2017-12-26 23:41:31,2017-12-26 23:50:22,2017-12-28 18:33:05,2018-01-08 22:36:36,2018-01-29 00:00:00
+85ce859fd6dc634de8d2f1e290444043,059f7fc5719c7da6cbafe370971a8d70,delivered,2017-11-21 00:03:41,2017-11-21 00:14:22,2017-11-23 21:32:26,2017-11-27 18:28:00,2017-12-11 00:00:00
+83018ec114eee8641c97e08f7b4e926f,7f8c8b9c2ae27bf3300f670c3d478be8,delivered,2017-10-26 15:54:26,2017-10-26 16:08:14,2017-10-26 21:46:53,2017-11-08 22:22:00,2017-11-23 00:00:00
+203096f03d82e0dffbc41ebc2e2bcfb7,d2b091571da224a1b36412c18bc3bbfe,delivered,2017-09-18 14:31:30,2017-09-19 04:04:09,2017-10-06 17:50:03,2017-10-09 22:23:46,2017-09-28 00:00:00
+f848643eec1d69395095eb3840d2051e,4fa1cd166fa598be6de80fa84eaade43,delivered,2018-03-15 08:52:40,2018-03-15 09:09:31,2018-03-15 19:52:48,2018-03-19 18:08:32,2018-03-29 00:00:00
+2807d0e504d6d4894d41672727bc139f,72ae281627a6102d9b3718528b420f8a,delivered,2018-02-03 20:37:35,2018-02-03 20:50:22,2018-02-05 22:37:28,2018-02-08 16:13:46,2018-02-21 00:00:00
+95266dbfb7e20354baba07964dac78d5,a166da34890074091a942054b36e4265,delivered,2018-01-08 07:55:29,2018-01-08 08:07:31,2018-01-24 23:16:37,2018-01-26 17:32:38,2018-02-21 00:00:00
+f3e7c359154d965827355f39d6b1fdac,62b423aab58096ca514ba6aa06be2f98,delivered,2018-08-09 11:44:40,2018-08-10 03:24:51,2018-08-10 12:29:00,2018-08-13 18:24:27,2018-08-17 00:00:00
+fbf9ac61453ac646ce8ad9783d7d0af6,3a874b4d4c4b6543206ff5d89287f0c3,delivered,2018-02-20 23:46:53,2018-02-22 02:30:46,2018-02-26 22:25:22,2018-03-21 22:03:54,2018-03-12 00:00:00
+acce194856392f074dbf9dada14d8d82,7e20bf5ca92da68200643bda76c504c6,delivered,2018-06-04 00:00:13,2018-06-05 00:35:10,2018-06-05 13:24:00,2018-06-16 15:20:55,2018-07-18 00:00:00
+dd78f560c270f1909639c11b925620ea,8b212b9525f9e74e85e37ed6df37693e,delivered,2018-03-12 01:50:26,2018-03-12 03:28:34,2018-03-12 21:06:37,2018-03-21 14:41:50,2018-03-28 00:00:00
+91b2a010e1e45e6ba3d133fa997597be,cce89a605105b148387c52e286ac8335,delivered,2018-05-02 11:45:38,2018-05-03 12:55:01,2018-05-10 16:16:00,2018-05-16 20:56:24,2018-05-23 00:00:00
+ecab90c9933c58908d3d6add7c6f5ae3,761df82feda9778854c6dafdaeb567e4,delivered,2018-02-25 13:50:30,2018-02-25 14:47:35,2018-02-26 22:28:50,2018-03-27 23:29:14,2018-04-13 00:00:00
+f70a0aff17df5a6cdd9a7196128bd354,456dc10730fbdba34615447ea195d643,delivered,2017-08-10 11:58:33,2017-08-12 02:45:24,2017-08-17 15:35:07,2017-08-18 14:28:02,2017-08-23 00:00:00
+1790eea0b567cf50911c057cf20f90f9,52142aa69d8d0e1247ab0cada0f76023,delivered,2018-04-16 21:15:39,2018-04-16 22:10:26,2018-04-18 13:05:09,2018-05-05 12:28:34,2018-05-15 00:00:00
+989225ba6d0ebd5873335f7e01de2ae7,816f8653d5361cbf94e58c33f2502a5c,delivered,2017-12-12 13:56:04,2017-12-14 13:54:13,2017-12-16 00:18:57,2018-01-03 18:03:36,2018-01-08 00:00:00
+d887b52c6516beb39e8cd44a5f8b60f7,d9ef95f98d8da3b492bb8c0447910498,delivered,2018-02-03 12:38:58,2018-02-03 12:50:30,2018-02-05 21:26:53,2018-02-22 00:07:55,2018-03-07 00:00:00
+b276e4f8c0fb86bd82fce576f21713e0,cf8ffeddf027932e51e4eae73b384059,delivered,2018-07-29 23:34:51,2018-07-29 23:45:15,2018-07-30 14:43:00,2018-07-31 22:48:50,2018-08-06 00:00:00
+8563039e855156e48fccee4d611a3196,5f16605299d698660e0606f7eae2d2f9,delivered,2018-02-17 15:59:46,2018-02-17 16:15:34,2018-02-20 23:03:56,2018-03-20 00:59:25,2018-03-20 00:00:00
+60550084e6b4c0cb89a87df1f3e5ebd9,f5458ddc3545711efa883dd7ae7c4497,delivered,2018-02-21 18:15:12,2018-02-23 02:10:52,2018-02-27 18:52:09,2018-03-13 23:58:43,2018-03-29 00:00:00
+5acce57f8d9dfd55fa48e212a641a69d,295ae9b35379e077273387ff64354b6f,delivered,2017-07-31 21:37:10,2017-08-02 02:56:02,2017-08-03 18:32:48,2017-08-08 21:24:41,2017-08-22 00:00:00
+434d158e96bdd6972ad6e6d73ddcfd22,2a1dfb647f32f4390e7b857c67458536,delivered,2018-06-01 12:23:13,2018-06-05 03:35:15,2018-06-08 11:49:00,2018-06-18 21:32:52,2018-07-17 00:00:00
+7206b86ea789983f7a273ea7fa0bc2a8,3391c4bc11a817e7973e498b0b023158,delivered,2018-03-26 17:12:18,2018-03-26 17:28:27,2018-03-28 17:22:53,2018-04-05 22:11:18,2018-04-12 00:00:00
+1e7aff52cdbb2451ace09d0f848c3699,ddaff536587109b89777e0353215e150,delivered,2017-05-19 18:53:40,2017-05-19 19:05:17,2017-05-22 10:16:07,2017-05-31 13:58:46,2017-06-12 00:00:00
+6ea2f835b4556291ffdc53fa0b3b95e8,c7340080e394356141681bd4c9b8fe31,delivered,2017-11-24 21:27:48,2017-11-25 00:21:09,2017-12-13 21:14:05,2017-12-28 18:59:23,2017-12-21 00:00:00
+948097deef559c742e7ce321e5e58919,8644be24d48806bc3a88fd59fb47ceb1,delivered,2017-08-04 17:10:39,2017-08-04 17:25:11,2017-08-07 17:52:01,2017-08-12 14:08:40,2017-09-01 00:00:00
+d22e9fa5731b9e30e8b27afcdc2f8563,756fb9391752dad934e0fe3733378e57,delivered,2018-08-04 23:25:30,2018-08-04 23:35:13,2018-08-06 15:03:00,2018-08-13 23:34:42,2018-09-13 00:00:00
+ee64d42b8cf066f35eac1cf57de1aa85,caded193e8e47b8362864762a83db3c5,shipped,2018-06-04 16:44:48,2018-06-05 04:31:18,2018-06-05 14:32:00,,2018-06-28 00:00:00
+6ebaec694d7025e2ad4a05dba887c032,4f28355e5c17a4a42d3ce2439a1d4501,delivered,2017-05-18 13:55:47,2017-05-18 14:05:17,2017-05-19 12:01:38,2017-05-29 12:47:20,2017-06-09 00:00:00
+d17dc4a904426827ca80f2ccb3a6be56,569cf68214806a39acc0f39344aea67f,delivered,2017-05-14 20:28:25,2017-05-14 20:42:45,2017-05-16 08:17:46,2017-05-25 09:14:31,2017-06-12 00:00:00
+25f4376934e13d3508486352e11a5db0,12fd2740039676063a874b9567dfa651,delivered,2018-05-17 16:59:11,2018-05-18 01:17:39,2018-05-18 13:02:00,2018-05-21 15:22:11,2018-05-25 00:00:00
+5820a1100976432c7968a52da59e9364,2b56e94c2f66f2d97cfa63356f69cee8,delivered,2018-07-29 11:24:17,2018-07-29 11:44:19,2018-07-30 13:47:00,2018-08-02 22:09:11,2018-08-13 00:00:00
+2ce1ad82022c1ba30c2079502ac725aa,7f2178c5d771e17f507d3c1637339298,delivered,2017-08-09 20:19:05,2017-08-11 04:15:29,2017-08-11 17:52:32,2017-08-16 17:16:44,2017-08-31 00:00:00
+138849fd84dff2fb4ca70a0a34c4aa1c,9b18f3fc296990b97854e351334a32f6,delivered,2018-02-01 14:02:19,2018-02-03 02:53:07,2018-02-06 19:13:26,2018-02-14 13:41:59,2018-02-23 00:00:00
+47aa4816b27ba60ec948cd019cc1afc1,148348ff65384b4249b762579532e248,delivered,2018-06-26 13:42:52,2018-06-27 08:35:32,2018-06-27 13:20:00,2018-07-03 18:37:46,2018-07-20 00:00:00
+9faeb9b2746b9d7526aef5acb08e2aa0,79183cd650e2bb0d475b0067d45946ac,delivered,2018-07-26 14:39:59,2018-07-26 14:55:10,2018-07-27 12:04:00,2018-07-31 22:26:55,2018-08-16 00:00:00
+641fb0752bf5b5940c376b3a8bb9dc52,f5afca14dfa9dc64251cf2b45c54c363,delivered,2017-12-15 00:06:10,2017-12-15 00:14:55,2017-12-19 01:58:00,2018-01-03 15:09:32,2018-01-16 00:00:00
+e425680f760cbc130be3e53a9773c584,f178c1827f67a8467b0385b7378d951a,delivered,2017-08-31 08:15:24,2017-08-31 08:30:17,2017-08-31 20:06:14,2017-09-04 20:59:55,2017-09-20 00:00:00
+40c5e18f7d112b59b3e5113a59a905b3,67407057a7d5ee17d1cd09523f484d13,delivered,2018-06-11 10:25:52,2018-06-11 10:58:32,2018-06-14 13:03:00,2018-06-19 00:31:13,2018-07-16 00:00:00
+734e7d1bbaeb2ff82521ca0fe6fb6f79,2932d241d1f31e6df6c701d52370ae02,delivered,2018-06-11 08:18:19,2018-06-11 08:31:50,2018-06-11 14:54:00,2018-06-14 21:32:21,2018-07-05 00:00:00
+66e4624ae69e7dc89bd50222b59f581f,684fa6da5134b9e4dab731e00011712d,delivered,2018-03-09 14:50:15,2018-03-09 15:40:39,2018-03-15 00:31:19,2018-04-03 13:28:46,2018-04-02 00:00:00
+a685d016c8a26f71a0bb67821070e398,911e4c37f5cafe1604fe6767034bf1ae,delivered,2017-03-13 18:14:36,2017-03-13 18:14:36,2017-03-22 14:03:09,2017-04-06 13:37:16,2017-03-30 00:00:00
+2edfd6d1f0b4cd0db4bf37b1b224d855,241e78de29b3090cfa1b5d73a8130c72,delivered,2017-06-13 21:11:26,2017-06-15 03:05:45,2017-06-16 14:55:37,2017-06-19 18:51:28,2017-07-06 00:00:00
+68873cf91053cd11e6b49a766db5af1a,4632eb5a8f175f6fe020520ae0c678f3,delivered,2017-11-30 22:02:15,2017-12-02 02:51:18,2017-12-04 22:07:01,2017-12-05 20:28:40,2017-12-18 00:00:00
+f346ad4ee8f630e5e4ddaf862a34e6dd,dd5095632e3953fc0947b8ab5176b0be,delivered,2018-08-05 13:09:48,2018-08-05 13:24:34,2018-08-06 13:41:00,2018-08-10 18:35:40,2018-08-15 00:00:00
+8f06cc6465925031568537b815f1198d,9916715c2ab6ee1710c9c32f0a534ad2,delivered,2017-11-15 11:31:41,2017-11-15 11:46:42,2017-11-16 22:03:00,2017-11-22 22:41:07,2017-12-05 00:00:00
+ccbabeb0b02433bd0fcbac46e70339f2,c77ee2d8ba1614a4d489a44166894938,delivered,2018-02-19 20:31:09,2018-02-21 06:15:25,2018-02-22 21:04:23,2018-03-09 22:22:25,2018-03-13 00:00:00
+688052146432ef8253587b930b01a06d,81e08b08e5ed4472008030d70327c71f,delivered,2018-04-22 08:48:13,2018-04-24 18:25:22,2018-04-23 19:19:14,2018-04-24 19:31:58,2018-05-15 00:00:00
+f271576bed568e896f99eb710cd3a6f8,5dda11942d4f77bee3a46d71e442aec4,delivered,2018-01-07 21:44:54,2018-01-07 21:51:28,2018-01-10 21:56:09,2018-01-17 20:26:31,2018-02-14 00:00:00
+686541986ecfb7d9296eb67719973bf0,74805bc388861fa350ed2fade8444e0b,delivered,2018-02-10 13:26:59,2018-02-10 13:35:31,2018-02-14 20:47:38,2018-02-20 22:13:08,2018-03-12 00:00:00
+68e48e68da1f50f7c5838ea75e3a20dd,4afc1dcca5fe8926fc97d60a4497f8ab,delivered,2018-06-18 16:02:23,2018-06-18 17:00:57,2018-06-19 15:55:00,2018-06-22 21:18:51,2018-07-13 00:00:00
+b52cc4919de82b4d696a4380d10804a3,be8c14c16a4d47194ccdfe10f1fc5b1a,delivered,2018-06-13 13:47:39,2018-06-15 02:37:29,2018-06-15 14:22:00,2018-06-18 22:32:44,2018-06-26 00:00:00
+fdf128b3630c21adc9ca4fb8a51b68ec,a9d37ddc8ba4d9f6dbac7d8ec378cc95,delivered,2018-07-15 08:33:19,2018-07-16 14:31:10,2018-07-17 15:33:00,2018-07-24 16:41:18,2018-08-02 00:00:00
+a6aeb116d2cb5013eb8a94585b71ffef,bb2f5e670f7155dc622c57e4b31d0a69,delivered,2017-09-13 14:27:11,2017-09-13 14:44:39,2017-09-15 18:42:29,2017-09-16 15:40:08,2017-09-25 00:00:00
+fa516182d28f96f5f5c651026b0749ee,55e6b290205c84ddd23ddf5eb134efd4,delivered,2018-04-13 08:44:17,2018-04-13 13:30:02,2018-04-13 22:19:21,2018-04-19 20:41:45,2018-05-08 00:00:00
+6abaad69b8b349c3a529b4b91ce18e46,f5618502bee8eafdee72fb6955e2ebdf,delivered,2018-02-15 10:33:30,2018-02-15 10:47:59,2018-02-20 14:15:09,2018-02-24 19:15:56,2018-03-07 00:00:00
+974c1993ab8024d3ed16229183c2308d,a90391a47de936d56c66a5366cba1462,delivered,2017-02-20 11:45:39,2017-02-22 03:10:20,2017-02-23 06:47:35,2017-03-09 14:27:58,2017-03-21 00:00:00
+82bce245b1c9148f8d19a55b9ff70644,388025bec8128ff20ec1a316ed4dcf02,delivered,2017-04-20 17:15:46,2017-04-21 05:15:56,2017-04-24 09:34:13,2017-05-10 09:17:55,2017-05-12 00:00:00
+a910f58086d58b3ae6f37aa712d377b9,afb19a4b667cb708caab312757ec3d3f,delivered,2017-09-15 09:19:48,2017-09-15 09:35:18,2017-09-18 18:20:00,2017-09-25 20:14:48,2017-10-11 00:00:00
+bd4bd0194d6d29f83b8557d4b89b572a,636e15840ab051faa13d3f781b6e4233,delivered,2018-07-28 16:52:55,2018-07-31 03:50:24,2018-08-01 16:01:00,2018-08-06 18:44:46,2018-08-08 00:00:00
+634e8f4c0f6744a626f77f39770ac6aa,05e996469a2bf9559c7122b87e156724,delivered,2017-08-09 18:32:47,2017-08-09 18:45:18,2017-08-10 20:21:53,2017-08-16 18:17:54,2017-08-31 00:00:00
+6d25592267349b322799e2beb687871e,5bb39c890c91b1d26801aa19a9336eac,delivered,2018-08-26 22:04:34,2018-08-28 04:10:18,2018-08-28 12:56:00,2018-08-29 12:40:53,2018-08-30 00:00:00
+b8801cccd8068de30112e4f49903d74a,f26a435864aebedff7f7c84f82ee229f,delivered,2017-07-30 03:06:35,2017-07-30 03:25:08,2017-07-31 16:42:54,2017-08-01 14:27:31,2017-08-16 00:00:00
+2711a938db643b3f0b62ee2c8a2784aa,29cb486c739f9774c8eb542e07b56cd2,delivered,2017-12-22 00:17:37,2017-12-23 02:15:31,2017-12-27 19:54:46,2018-01-09 19:52:32,2018-01-19 00:00:00
+3bc77ce8be27211bac313c2daa402d1a,bf141bf67fbe428d558bcf0e018eab60,delivered,2017-04-06 22:39:29,2017-04-06 22:50:24,2017-04-07 14:54:18,2017-04-11 12:31:36,2017-04-27 00:00:00
+10c320f977c6a18f91b2d14be13128c6,b673f0597cb0c4d12778f731045f361a,delivered,2017-05-09 20:48:59,2017-05-09 21:02:45,2017-05-10 11:22:15,2017-05-18 13:22:35,2017-06-01 00:00:00
+0a4a2fccb27bd83a892fa503987a595b,6772a0a230a2667d16c3620f000e1348,delivered,2017-04-20 20:42:44,2017-04-20 20:55:09,2017-04-25 08:23:08,2017-05-11 13:07:46,2017-05-25 00:00:00
+e4de6d53ecff736bc68804b0b6e9f635,9f6618c17568ac301465fe7ad056c674,delivered,2017-10-16 14:56:50,2017-10-17 03:49:34,2017-10-27 22:14:21,2017-11-08 21:25:24,2017-11-21 00:00:00
+6b860b35691d486e45dc98e3514ec5f6,fee181bf648906d1c57f84f216976286,delivered,2017-12-08 09:42:43,2017-12-09 02:49:54,2017-12-11 15:19:04,2017-12-19 18:43:35,2018-01-03 00:00:00
+ec341c54a5ebf8ee0a67a8632aa7579b,df9b032b2ad0fd6bf37dfb48e5f83845,delivered,2017-08-26 16:53:30,2017-08-27 17:04:12,2017-08-30 13:26:32,2017-09-08 20:39:56,2017-09-21 00:00:00
+cadbb3657dac2dbbd5b84b12e7b78aad,93ada7a24817edda9f4ab998fa823d16,delivered,2018-02-27 12:55:42,2018-03-01 02:48:54,2018-03-03 02:27:03,2018-03-16 14:59:01,2018-03-29 00:00:00
+9defaf92cff22420e4e8ef7784815a55,64fb950e760ec8b0db79154a1fa9c1bf,delivered,2018-05-11 13:10:51,2018-05-11 13:36:50,2018-05-16 14:43:00,2018-05-21 16:09:55,2018-06-05 00:00:00
+20e0101b20700188cadb288126949685,48558a50a7ba1aab61891936d2ca7681,delivered,2018-01-22 19:22:22,2018-01-22 19:36:35,2018-01-24 23:32:21,2018-02-15 20:08:15,2018-02-19 00:00:00
+0e782c3705510e717d28907746cbda82,3a897024068ed42a183de61d5727d866,delivered,2018-05-01 08:12:37,2018-05-01 08:52:58,2018-05-02 19:01:00,2018-05-04 14:02:26,2018-05-16 00:00:00
+d3d6788577c9592da441752e8a1dd5e3,8628fac2267e8c8804525da99c10ed0e,delivered,2017-09-19 22:17:15,2017-09-20 07:55:14,2017-09-22 17:23:09,2017-10-10 18:43:53,2017-10-13 00:00:00
+86f21bf63784876b9fd6d35f46581d72,332df68ccac2f2f7d9e11299188f8bce,delivered,2018-04-11 22:32:31,2018-04-11 22:49:32,2018-04-14 00:02:39,2018-04-27 23:14:42,2018-05-21 00:00:00
+8447ff843b2616c50c0ced28ab1dae03,e28dd4261bed9c7ba89ecaf411b88f7c,delivered,2017-12-20 23:45:07,2017-12-22 02:37:45,2017-12-23 13:10:45,2018-01-09 18:14:02,2018-01-22 00:00:00
+f169bd689fb8b32ccd62df9050aebc0b,82f0b75bb50fcb30711e5277e36b3983,delivered,2018-04-22 23:23:18,2018-04-24 19:24:14,2018-04-27 13:46:00,2018-04-30 17:57:25,2018-05-07 00:00:00
+77e9941864fc840be8e4b1ba5347c0f7,3135962ee745ef39b85576df7ddbaa99,delivered,2018-08-03 08:59:39,2018-08-03 09:31:36,2018-08-03 10:10:00,2018-08-17 00:49:41,2018-08-27 00:00:00
+41bb5cee06dbf170878a9ef93ac7e7f5,1833a0540067becaf59368fe4cd4303a,delivered,2018-05-14 08:35:33,2018-05-14 08:52:24,2018-05-16 14:46:00,2018-05-18 14:48:38,2018-06-08 00:00:00
+6a0a8bfbbe700284feb0845d95e0867f,68451b39b1314302c08c65a29f1140fc,delivered,2017-11-22 11:32:22,2017-11-22 11:46:50,2017-11-27 13:39:35,2017-12-28 19:43:00,2017-12-11 00:00:00
+f7959f8385f34c4f645327465a1c9fc4,0bf19317b1830a69e55b40710576aa7a,delivered,2017-03-30 07:50:33,2017-03-30 08:05:08,2017-03-30 10:55:54,2017-04-10 02:59:52,2017-04-26 00:00:00
+23f553848a03aaab35bb3f9f87725125,c622b892a190735ef81c0087973fa16d,delivered,2018-06-05 09:10:34,2018-06-05 09:32:22,2018-06-06 15:37:00,2018-06-18 12:36:54,2018-07-23 00:00:00

data/orders/products.csv

@@ -0,0 +1,101 @@
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+f35927953ed82e19d06ad3aac2f06353,livros_interesse_geral,39.0,724.0,1.0,450.0,20.0,20.0,20.0

data/orders/sellers.csv

@@ -0,0 +1,88 @@
+seller_id,seller_zip_code_prefix,seller_city,seller_state
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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
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+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
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+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
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+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
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+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
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+98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
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+115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
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+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
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+126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
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+131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
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labs/lab2/lab2.ipynb

@@ -0,0 +1,58 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'neo.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 7\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Вывод всех столбцов\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mneo.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mcolumns)\n",
+      "File \u001b[1;32mc:\\Users\\tellsense\\maigit\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m   1014\u001b[0m     dialect,\n\u001b[0;32m   1015\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m   1023\u001b[0m )\n\u001b[0;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\tellsense\\maigit\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\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    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mc:\\Users\\tellsense\\maigit\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\tellsense\\maigit\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[1;32mc:\\Users\\tellsense\\maigit\\.venv\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'neo.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Вывод всех столбцов\n",
+    "\n",
+    "df = pd.read_csv(\"neo.csv\")\n",
+    "print(df.columns)"
+   ]
+  }
+ ],
+ "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

lec2.ipynb

@@ -0,0 +1,959 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Загрузка данных в DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 891 entries, 1 to 891\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column    Non-Null Count  Dtype  \n",
+      "---  ------    --------------  -----  \n",
+      " 0   Survived  891 non-null    int64  \n",
+      " 1   Pclass    891 non-null    int64  \n",
+      " 2   Name      891 non-null    object \n",
+      " 3   Sex       891 non-null    object \n",
+      " 4   Age       714 non-null    float64\n",
+      " 5   SibSp     891 non-null    int64  \n",
+      " 6   Parch     891 non-null    int64  \n",
+      " 7   Ticket    891 non-null    object \n",
+      " 8   Fare      891 non-null    float64\n",
+      " 9   Cabin     204 non-null    object \n",
+      " 10  Embarked  889 non-null    object \n",
+      "dtypes: float64(2), int64(4), object(5)\n",
+      "memory usage: 83.5+ KB\n",
+      "(891, 11)\n"
+     ]
+    },
+    {
+     "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>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</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>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Survived  Pclass  \\\n",
+       "PassengerId                     \n",
+       "1                   0       3   \n",
+       "2                   1       1   \n",
+       "3                   1       3   \n",
+       "4                   1       1   \n",
+       "5                   0       3   \n",
+       "\n",
+       "                                                          Name     Sex   Age  \\\n",
+       "PassengerId                                                                    \n",
+       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
+       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
+       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
+       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
+       "5                                     Allen, Mr. William Henry    male  35.0   \n",
+       "\n",
+       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
+       "PassengerId                                                          \n",
+       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
+       "2                1      0          PC 17599  71.2833   C85        C  \n",
+       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "4                1      0            113803  53.1000  C123        S  \n",
+       "5                0      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"data/titanic.csv\", index_col=\"PassengerId\")\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": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Survived      0\n",
+      "Pclass        0\n",
+      "Name          0\n",
+      "Sex           0\n",
+      "Age         177\n",
+      "SibSp         0\n",
+      "Parch         0\n",
+      "Ticket        0\n",
+      "Fare          0\n",
+      "Cabin       687\n",
+      "Embarked      2\n",
+      "dtype: int64\n",
+      "\n",
+      "Survived    False\n",
+      "Pclass      False\n",
+      "Name        False\n",
+      "Sex         False\n",
+      "Age          True\n",
+      "SibSp       False\n",
+      "Parch       False\n",
+      "Ticket      False\n",
+      "Fare        False\n",
+      "Cabin        True\n",
+      "Embarked     True\n",
+      "dtype: bool\n",
+      "\n",
+      "Age процент пустых значений: %19.87\n",
+      "Cabin процент пустых значений: %77.10\n",
+      "Embarked процент пустых значений: %0.22\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Заполнение пропущенных данных\n",
+    "\n",
+    "https://pythonmldaily.com/posts/pandas-dataframes-search-drop-empty-values\n",
+    "\n",
+    "https://scales.arabpsychology.com/stats/how-to-fill-nan-values-with-median-in-pandas/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(891, 11)\n",
+      "Survived    False\n",
+      "Pclass      False\n",
+      "Name        False\n",
+      "Sex         False\n",
+      "Age         False\n",
+      "SibSp       False\n",
+      "Parch       False\n",
+      "Ticket      False\n",
+      "Fare        False\n",
+      "Cabin       False\n",
+      "Embarked    False\n",
+      "dtype: bool\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>AgeFillNA</th>\n",
+       "      <th>AgeFillMedian</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</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>887</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Survived  Pclass                                      Name  \\\n",
+       "PassengerId                                                               \n",
+       "887                 0       2                     Montvila, Rev. Juozas   \n",
+       "888                 1       1              Graham, Miss. Margaret Edith   \n",
+       "889                 0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
+       "890                 1       1                     Behr, Mr. Karl Howell   \n",
+       "891                 0       3                       Dooley, Mr. Patrick   \n",
+       "\n",
+       "                Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \\\n",
+       "PassengerId                                                                 \n",
+       "887            male  27.0      0      0      211536  13.00   NaN        S   \n",
+       "888          female  19.0      0      0      112053  30.00   B42        S   \n",
+       "889          female   NaN      1      2  W./C. 6607  23.45   NaN        S   \n",
+       "890            male  26.0      0      0      111369  30.00  C148        C   \n",
+       "891            male  32.0      0      0      370376   7.75   NaN        Q   \n",
+       "\n",
+       "             AgeFillNA  AgeFillMedian  \n",
+       "PassengerId                            \n",
+       "887               27.0           27.0  \n",
+       "888               19.0           19.0  \n",
+       "889                0.0           28.0  \n",
+       "890               26.0           26.0  \n",
+       "891               32.0           32.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fillna_df = df.fillna(0)\n",
+    "\n",
+    "print(fillna_df.shape)\n",
+    "\n",
+    "print(fillna_df.isnull().any())\n",
+    "\n",
+    "# Замена пустых данных на 0\n",
+    "df[\"AgeFillNA\"] = df[\"Age\"].fillna(0)\n",
+    "\n",
+    "# Замена пустых данных на медиану\n",
+    "df[\"AgeFillMedian\"] = df[\"Age\"].fillna(df[\"Age\"].median())\n",
+    "\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>AgeFillNA</th>\n",
+       "      <th>AgeFillMedian</th>\n",
+       "      <th>AgeCopy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</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",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>891</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Survived  Pclass                                      Name  \\\n",
+       "PassengerId                                                               \n",
+       "887                 0       2                     Montvila, Rev. Juozas   \n",
+       "888                 1       1              Graham, Miss. Margaret Edith   \n",
+       "889                 0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
+       "890                 1       1                     Behr, Mr. Karl Howell   \n",
+       "891                 0       3                       Dooley, Mr. Patrick   \n",
+       "\n",
+       "                Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \\\n",
+       "PassengerId                                                                 \n",
+       "887            male  27.0      0      0      211536  13.00   NaN        S   \n",
+       "888          female  19.0      0      0      112053  30.00   B42        S   \n",
+       "889          female   NaN      1      2  W./C. 6607  23.45   NaN        S   \n",
+       "890            male  26.0      0      0      111369  30.00  C148        C   \n",
+       "891            male  32.0      0      0      370376   7.75   NaN        Q   \n",
+       "\n",
+       "             AgeFillNA  AgeFillMedian  AgeCopy  \n",
+       "PassengerId                                     \n",
+       "887               27.0           27.0     27.0  \n",
+       "888               19.0           19.0     19.0  \n",
+       "889                0.0           28.0      0.0  \n",
+       "890               26.0           26.0     26.0  \n",
+       "891               32.0           32.0     32.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"AgeCopy\"] = df[\"Age\"]\n",
+    "\n",
+    "# Замена данных сразу в DataFrame без копирования\n",
+    "df.fillna({\"AgeCopy\": 0}, inplace=True)\n",
+    "\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Удаление наблюдений с пропусками"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(183, 14)\n",
+      "Survived    False\n",
+      "Pclass      False\n",
+      "Name        False\n",
+      "Sex         False\n",
+      "Age         False\n",
+      "SibSp       False\n",
+      "Parch       False\n",
+      "Ticket      False\n",
+      "Fare        False\n",
+      "Cabin       False\n",
+      "Embarked    False\n",
+      "dtype: bool\n"
+     ]
+    }
+   ],
+   "source": [
+    "dropna_df = df.dropna()\n",
+    "\n",
+    "print(dropna_df.shape)\n",
+    "\n",
+    "print(fillna_df.isnull().any())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Создание выборок данных\n",
+    "\n",
+    "Библиотека scikit-learn\n",
+    "\n",
+    "https://scikit-learn.org/stable/index.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"assets/lec2-split.png\" width=\"600\" style=\"background-color: white\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функция для создания выборок\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
+    "    following fractional ratios provided by the user, where each subset is\n",
+    "    stratified by the values in a specific column (that is, each subset has\n",
+    "    the same relative frequency of the values in the column). It performs this\n",
+    "    splitting by running train_test_split() twice.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    df_input : Pandas dataframe\n",
+    "        Input dataframe to be split.\n",
+    "    stratify_colname : str\n",
+    "        The name of the column that will be used for stratification. Usually\n",
+    "        this column would be for the label.\n",
+    "    frac_train : float\n",
+    "    frac_val   : float\n",
+    "    frac_test  : float\n",
+    "        The ratios with which the dataframe will be split into train, val, and\n",
+    "        test data. The values should be expressed as float fractions and should\n",
+    "        sum to 1.0.\n",
+    "    random_state : int, None, or RandomStateInstance\n",
+    "        Value to be passed to train_test_split().\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    df_train, df_val, df_test :\n",
+    "        Dataframes containing the three splits.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input  # Contains all columns.\n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  # Dataframe of just the column on which to stratify.\n",
+    "\n",
+    "    # Split original dataframe into train and temp dataframes.\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    # Split the temp dataframe into val and test dataframes.\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pclass\n",
+      "3    491\n",
+      "1    216\n",
+      "2    184\n",
+      "Name: count, dtype: int64\n",
+      "Обучающая выборка:  (534, 3)\n",
+      "Pclass\n",
+      "3    294\n",
+      "1    130\n",
+      "2    110\n",
+      "Name: count, dtype: int64\n",
+      "Контрольная выборка:  (178, 3)\n",
+      "Pclass\n",
+      "3    98\n",
+      "1    43\n",
+      "2    37\n",
+      "Name: count, dtype: int64\n",
+      "Тестовая выборка:  (179, 3)\n",
+      "Pclass\n",
+      "3    99\n",
+      "1    43\n",
+      "2    37\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print(df.Pclass.value_counts())\n",
+    "\n",
+    "data = df[[\"Pclass\", \"Survived\", \"AgeFillMedian\"]].copy()\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   data, stratify_colname=\"Pclass\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train.Pclass.value_counts())\n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val.Pclass.value_counts())\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test.Pclass.value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выборка с избытком (oversampling)\n",
+    "\n",
+    "https://www.blog.trainindata.com/oversampling-techniques-for-imbalanced-data/\n",
+    "\n",
+    "https://datacrayon.com/machine-learning/class-imbalance-and-oversampling/\n",
+    "\n",
+    "Выборка с недостатком (undersampling)\n",
+    "\n",
+    "https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/\n",
+    "\n",
+    "Библиотека imbalanced-learn\n",
+    "\n",
+    "https://imbalanced-learn.org/stable/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df_train' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;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[1;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, \u001b[43mdf_train\u001b[49m\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(df_train\u001b[38;5;241m.\u001b[39mPclass\u001b[38;5;241m.\u001b[39mvalue_counts())\n\u001b[0;32m      8\u001b[0m X_resampled, y_resampled \u001b[38;5;241m=\u001b[39m ada\u001b[38;5;241m.\u001b[39mfit_resample(df_train, df_train[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPclass\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df_train' is not defined"
+     ]
+    }
+   ],
+   "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": "3.12.7"
+  }
+ },
+ "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"
+seaborn = "^0.13.2"
+
+
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.29.5"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

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()