35 changed files with 2 additions and 26978 deletions
--- a/.flake8
+++ b/.flake8
@ -1,2 +0,0 @@
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--- a/.gitattributes
+++ b/.gitattributes
@ -1 +0,0 @@
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+++ b/.gitignore
@ -1,278 +0,0 @@
 # 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
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 ### VisualStudioCode ###
 .vscode/*
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--- a/.vscode/extensions.json
+++ b/.vscode/extensions.json
@ -1,13 +0,0 @@
 {
    "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"
    ]
 }
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@ -1,16 +0,0 @@
 {
    // 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
        }
    ]
 }
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@ -1,38 +0,0 @@
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 }
--- a/README.md
+++ b/README.md
@ -1 +1,2 @@
-Лабораторные работы по МИИ
+# ISEbd-31_Baygulov_A.A._MAI
--- a/assets/lec2-split.png
+++ b/assets/lec2-split.png
--- a/assets/quantile.png
+++ b/assets/quantile.png
--- a/backend/init.py
+++ b/backend/init.py
@ -1,52 +0,0 @@
 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
--- a/backend/api.py
+++ b/backend/api.py
@ -1,57 +0,0 @@
 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")
--- a/backend/service.py
+++ b/backend/service.py
@ -1,59 +0,0 @@
 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
--- a/data/dollar.csv
+++ b/data/dollar.csv
@ -1,244 +0,0 @@
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 "23.12.2023","91.9389","","",""
 "26.12.2023","91.9690","","",""
 "27.12.2023","91.7069","","",""
 "28.12.2023","91.7051","","",""
 "29.12.2023","90.3041","","",""
 "30.12.2023","89.6883","","",""
 "10.01.2024","90.4040","","",""
 "11.01.2024","89.3939","","",""
 "12.01.2024","88.7818","","",""
 "13.01.2024","88.1324","","",""
 "16.01.2024","87.6772","","",""
 "17.01.2024","87.6457","","",""
 "18.01.2024","88.3540","","",""
 "19.01.2024","88.6610","","",""
 "20.01.2024","88.5896","","",""
 "23.01.2024","87.9724","","",""
 "24.01.2024","87.9199","","",""
 "25.01.2024","88.2829","","",""
 "26.01.2024","88.6562","","",""
 "27.01.2024","89.5159","","",""
 "30.01.2024","89.6090","","",""
 "31.01.2024","89.2887","","",""
 "01.02.2024","89.6678","","",""
 "02.02.2024","90.2299","","",""
 "03.02.2024","90.6626","","",""
 "06.02.2024","91.2434","","",""
 "07.02.2024","90.6842","","",""
 "08.02.2024","91.1514","","",""
 "09.02.2024","91.2561","","",""
 "10.02.2024","90.8901","","",""
 "13.02.2024","91.0758","","",""
 "14.02.2024","91.2057","","",""
 "15.02.2024","91.4316","","",""
 "16.02.2024","91.8237","","",""
 "17.02.2024","92.5492","","",""
 "20.02.2024","92.4102","","",""
 "21.02.2024","92.3490","","",""
 "22.02.2024","92.4387","","",""
 "23.02.2024","92.7519","","",""
 "27.02.2024","92.6321","","",""
 "28.02.2024","92.0425","","",""
 "29.02.2024","91.8692","","",""
 "01.03.2024","90.8423","","",""
 "02.03.2024","91.3336","","",""
 "05.03.2024","91.3534","","",""
 "06.03.2024","91.1604","","",""
 "07.03.2024","90.3412","","",""
 "08.03.2024","90.7493","","",""
 "12.03.2024","90.6252","","",""
 "13.03.2024","90.8818","","",""
 "19.03.2024","91.9829","","",""
 "20.03.2024","92.2243","","",""
 "21.03.2024","92.6861","","",""
 "22.03.2024","91.9499","","",""
 "23.03.2024","92.6118","","",""
 "26.03.2024","92.7761","","",""
--- a/data/orders/customers.csv
+++ b/data/orders/customers.csv
@ -1,101 +0,0 @@
 customer_id,customer_unique_id,customer_zip_code_prefix,customer_city,customer_state
 41ce2a54c0b03bf3443c3d931a367089,3a653a41f6f9fc3d2a113cf8398680e8,75265,vianopolis,GO
 7f8c8b9c2ae27bf3300f670c3d478be8,634f09f6075fe9032e6c19609ffe995a,44024,feira de santana,BA
 569cf68214806a39acc0f39344aea67f,c2551ea089b7ebbc67a2ea8757152514,44380,cruz das almas,BA
 d3e3b74c766bc6214e0c830b17ee2341,e97109680b052ee858d93a539597bba7,35400,ouro preto,MG
 d2b091571da224a1b36412c18bc3bbfe,d699688533772c15a061e8ce81cb56df,4001,sao paulo,SP
 3b6828a50ffe546942b7a473d70ac0fc,ccafc1c3f270410521c3c6f3b249870f,74820,goiania,GO
 148348ff65384b4249b762579532e248,db979bdfe0bbba29ecd3df3f6c50bea2,87711,paranavai,PR
 4f28355e5c17a4a42d3ce2439a1d4501,4acce2834231e13b1514915adda5ec2b,21910,rio de janeiro,RJ
 3187789bec990987628d7a9beb4dd6ac,6087cfc70fd833cf2db637a5e6e9d76b,88780,imbituba,SC
 8628fac2267e8c8804525da99c10ed0e,7973a6ba9c81ecaeb3d628c33c7c7c48,85555,palmas,PR
 4fa1cd166fa598be6de80fa84eaade43,68954feaafe4dd638f3bd3e2afa174ec,8473,sao paulo,SP
 cf8ffeddf027932e51e4eae73b384059,6cbe8a392b76916e84c2faf69d0d0da0,13454,santa barbara d'oeste,SP
 48558a50a7ba1aab61891936d2ca7681,42f80af2e6c585667e4eb416859ae153,39370,jequitai,MG
 8b212b9525f9e74e85e37ed6df37693e,f4a7ef6bd931f83d75d83b71c94e90df,13568,sao carlos,SP
 c77ee2d8ba1614a4d489a44166894938,9c9cef121cb812cb301babddc2d8331e,38067,uberaba,MG
 be8c14c16a4d47194ccdfe10f1fc5b1a,c86a25b8f5f6c203bb3471553bdc3200,13157,cosmopolis,SP
 f5618502bee8eafdee72fb6955e2ebdf,fa0ee7ceb94193fb02aa78ce3a55695a,6395,carapicuiba,SP
 3df704f53d3f1d4818840b34ec672a9f,04cf8185c71090d28baa4407b2e6d600,5271,sao paulo,SP
 67407057a7d5ee17d1cd09523f484d13,7cfba6e55439cae3fd2479d62fafe67f,22240,rio de janeiro,RJ
 afb19a4b667cb708caab312757ec3d3f,a7e7b19ff34ab885f7b7331de2417cf3,78043,cuiaba,MT
 caded193e8e47b8362864762a83db3c5,08fb46d35bb3ab4037202c23592d1259,13215,jundiai,SP
 241e78de29b3090cfa1b5d73a8130c72,c63e44efa43f3947087aee96b388d949,4658,sao paulo,SP
 62b423aab58096ca514ba6aa06be2f98,9c9242ad7f1b52d926ea76778e1c0c57,18052,sorocaba,SP
 494dded5b201313c64ed7f100595b95c,f2a85dec752b8517b5e58a06ff3cd937,20780,rio de janeiro,RJ
 e28dd4261bed9c7ba89ecaf411b88f7c,b6aa1d5781553afaa244c3e42246d93c,88302,itajai,SC
 4afc1dcca5fe8926fc97d60a4497f8ab,a464f750556546a0989d9326ec003ccf,8220,sao paulo,SP
 761df82feda9778854c6dafdaeb567e4,1428917cd397d4f9ac0fde76dd6f2266,69317,boa vista,RR
 82f0b75bb50fcb30711e5277e36b3983,4a8c8f751984985cd49f74249da95aae,8485,sao paulo,SP
 f5458ddc3545711efa883dd7ae7c4497,661a5e18a28b34880ccc60112f2b8e8e,62360,ibiapina,CE
 295ae9b35379e077273387ff64354b6f,f1f4f45c8602d0db1329eed1c8e935d4,19780,quata,SP
 3a874b4d4c4b6543206ff5d89287f0c3,a25d5f94840d3c6a1a49f271ed83f4ec,21715,rio de janeiro,RJ
 f54a9f0e6b351c431402b8461ea51999,39382392765b6dc74812866ee5ee92a7,99655,faxinalzinho,RS
 456dc10730fbdba34615447ea195d643,1974875b4a1d2e2ee6d586e3ba4d7602,5634,sao paulo,SP
 f178c1827f67a8467b0385b7378d951a,9d9ab3b77f0416765b3fbedf94a942a4,12070,taubate,SP
 0bf19317b1830a69e55b40710576aa7a,5ddb4fdd9cef2450d17ae20639815885,13218,jundiai,SP
 8644be24d48806bc3a88fd59fb47ceb1,4ca5f90433afb5493247f0bafb583483,73350,brasilia,DF
 3391c4bc11a817e7973e498b0b023158,1b542f810484d8c042aed33a7c61a218,4561,sao paulo,SP
 fee181bf648906d1c57f84f216976286,4754e3b66497719a91b36268ed9c5718,13760,tapiratiba,SP
 74805bc388861fa350ed2fade8444e0b,5d710d9a48ebb7fe5ffc2940ff29f346,38401,uberlandia,MG
 6772a0a230a2667d16c3620f000e1348,c7a9a76a4b24a7e7b2caff982409b7ee,58600,santa luzia,PB
 4632eb5a8f175f6fe020520ae0c678f3,6da92ae920ab16fc4eceb8fcd7bd43ce,8280,sao paulo,SP
 d9ef95f98d8da3b492bb8c0447910498,a2649503b92028291f011a976619b322,26572,mesquita,RJ
 059f7fc5719c7da6cbafe370971a8d70,d0ff1a7468fcc46b8fc658ab35d2a12c,13186,hortolandia,SP
 ddaff536587109b89777e0353215e150,c796780c7daeab9e94cc052b1f103b21,26600,paracambi,RJ
 dd5095632e3953fc0947b8ab5176b0be,da45a9a1df408c39f013b9b0b505042c,70680,brasilia,DF
 df9b032b2ad0fd6bf37dfb48e5f83845,410979f3cfd34e467d4fad78bd0f0219,89440,irineopolis,SC
 684fa6da5134b9e4dab731e00011712d,ddf60e20e6e262e2136801ce5cd628b0,49030,aracaju,SE
 2b56e94c2f66f2d97cfa63356f69cee8,cc1a30280651daf2d918ed7868575771,95270,flores da cunha,RS
 9f6618c17568ac301465fe7ad056c674,e3bcfea9bab07b492391664fc1ffc28a,44180,antonio cardoso,BA
 29cb486c739f9774c8eb542e07b56cd2,2ae3c67452283d5a0d30b32e0d33296e,71505,brasilia,DF
 5f16605299d698660e0606f7eae2d2f9,92fd8aa5948e20c43a014c44c025c5e1,77480,alvorada,TO
 f88197465ea7920adcdbec7375364d82,7c142cf63193a1473d2e66489a9ae977,59296,sao goncalo do amarante,RN
 5dda11942d4f77bee3a46d71e442aec4,6a0e43f0d7e1b5539e4c58a26ebe35da,46740,boninal,BA
 a90391a47de936d56c66a5366cba1462,32de2a7a93dbfc527b3f584744b9c6ce,37310,bom jardim de minas,MG
 9916715c2ab6ee1710c9c32f0a534ad2,bf0303939d54b8df5da4762bbaab1955,22631,rio de janeiro,RJ
 636e15840ab051faa13d3f781b6e4233,65e5aaf9f721945f29cdba45c206cb83,14090,ribeirao preto,SP
 8ab97904e6daea8866dbdbc4fb7aad2c,72632f0f9dd73dfee390c9b22eb56dd6,9195,santo andre,SP
 388025bec8128ff20ec1a316ed4dcf02,f9effeed3df9ae063a58c0759b96f8b2,85804,cascavel,PR
 c7340080e394356141681bd4c9b8fe31,3e4fd73f1e86b135b9b121d6abbe9597,19400,presidente venceslau,SP
 503740e9ca751ccdda7ba28e9ab8f608,80bb27c7c16e8f973207a5086ab329e2,86320,congonhinhas,PR
 816f8653d5361cbf94e58c33f2502a5c,37363700139c1aef873bbcd916e57dfd,5778,sao paulo,SP
 9ef432eb6251297304e76186b10a928d,7c396fd4830fd04220f754e42b4e5bff,3149,sao paulo,SP
 68451b39b1314302c08c65a29f1140fc,781ae350edb16842380e81d7c7feb431,20740,rio de janeiro,RJ
 b673f0597cb0c4d12778f731045f361a,04e495a3f45df8b41be2e934bbc16961,94055,gravatai,RS
 3a897024068ed42a183de61d5727d866,adeefbe14d26d3bf90facfeaae35d605,4845,sao paulo,SP
 52142aa69d8d0e1247ab0cada0f76023,a6fefcd9f434474cf6fcd8ed1102fd63,55540,palmares,PE
 55e6b290205c84ddd23ddf5eb134efd4,7f2eb9cf900070f2e7a7f0e95719f85b,13145,paulinia,SP
 ed0271e0b7da060a393796590e7b737a,36edbb3fb164b1f16485364b6fb04c73,98900,santa rosa,RS
 a9d37ddc8ba4d9f6dbac7d8ec378cc95,3c0402bcc3ec3b33fc4430eb6c08720a,89225,joinville,SC
 b0830fb4747a6c6d20dea0b8c802d7ef,af07308b275d755c9edb36a90c618231,47813,barreiras,BA
 bb2f5e670f7155dc622c57e4b31d0a69,31b8fa2573bde01af4737e8ed29c348b,2346,sao paulo,SP
 756fb9391752dad934e0fe3733378e57,394b2ce444baae9ae609f5d32000de0f,47850,luis eduardo magalhaes,BA
 5bb39c890c91b1d26801aa19a9336eac,a71cac9f356cfeb9db35061020806212,2407,sao paulo,SP
 7e20bf5ca92da68200643bda76c504c6,576ea0cab426cd8a00fad9a9c90a4494,41213,salvador,BA
 2a1dfb647f32f4390e7b857c67458536,5f7d7732b351ce851a158528581af05f,54330,jaboatao dos guararapes,PE
 cce89a605105b148387c52e286ac8335,bd13608b9c6033892ce62269b50a0afc,9182,santo andre,SP
 738b086814c6fcc74b8cc583f8516ee3,6e26bbeaa107ec34112c64e1ee31c0f5,21381,rio de janeiro,RJ
 81e08b08e5ed4472008030d70327c71f,0e764fc1a13e47e900c3d59a989753e8,36045,juiz de fora,MG
 911e4c37f5cafe1604fe6767034bf1ae,51838d41add414a0b1b989b7d251d9ee,13068,campinas,SP
 f26a435864aebedff7f7c84f82ee229f,bb4d84a2b45b22ed710ac8c0dec63d1a,8552,poa,SP
 9bdf08b4b3b52b5526ff42d37d47f222,932afa1e708222e5821dac9cd5db4cae,26525,nilopolis,RJ
 64fb950e760ec8b0db79154a1fa9c1bf,b11b7871c2b8be2d11fab954f58542f2,18017,sorocaba,SP
 bf141bf67fbe428d558bcf0e018eab60,c756e1910755edd88c00ac3f46baba4b,31255,belo horizonte,MG
 3135962ee745ef39b85576df7ddbaa99,00b2ca23369b68c4d4105ecea9c0cb93,62970,alto santo,CE
 1833a0540067becaf59368fe4cd4303a,ca73adc05ad5d0d880de79b5ea3253b3,4053,sao paulo,SP
 7f2178c5d771e17f507d3c1637339298,12e7a2c201751ddc979e7a45cef500f3,1038,sao paulo,SP
 c622b892a190735ef81c0087973fa16d,439ced9aafa171a1ac88efa951c7db0a,85618,flor da serra do sul,PR
 79183cd650e2bb0d475b0067d45946ac,c77154776ead8e798c2d684205938f71,90620,porto alegre,RS
 332df68ccac2f2f7d9e11299188f8bce,bb7ef994cc22b1fc694ac59fb377b824,39135,presidente kubitschek,MG
 a166da34890074091a942054b36e4265,451e48381edab7f1f6dbfa6d728616ff,89070,blumenau,SC
 f5afca14dfa9dc64251cf2b45c54c363,38cad70d154a4dcc42b598d5c01f7ef1,25211,duque de caxias,RJ
 31ad1d1b63eb9962463f764d4e6e0c9d,299905e3934e9e181bfb2e164dd4b4f8,18075,sorocaba,SP
 7711cf624183d843aafe81855097bc37,782987b81c92239d922aa49d6bd4200b,4278,sao paulo,SP
 72ae281627a6102d9b3718528b420f8a,b8df986511d928829c3192c2ed081eba,3323,sao paulo,SP
 12fd2740039676063a874b9567dfa651,372e0fc66eacb8698e4f9997d366d961,12230,sao jose dos campos,SP
 19402a48fe860416adf93348aba37740,e2dfa3127fedbbca9707b36304996dab,4812,sao paulo,SP
 9b18f3fc296990b97854e351334a32f6,b2cac0b16835dabf811b204127f58afa,6330,carapicuiba,SP
 05e996469a2bf9559c7122b87e156724,5229b8e4d7d2b9b676c2083c17b1ecd0,93180,portao,RS
 2932d241d1f31e6df6c701d52370ae02,f7603d34c795584792a484186233e6e5,3942,sao paulo,SP
 93ada7a24817edda9f4ab998fa823d16,cd148470c375939669971e8a032b16b4,14091,ribeirao preto,SP
--- a/data/orders/order_items.csv
+++ b/data/orders/order_items.csv
@ -1,116 +0,0 @@
 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
--- a/data/orders/orders.csv
+++ b/data/orders/orders.csv
@ -1,101 +0,0 @@
 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
--- a/data/orders/products.csv
+++ b/data/orders/products.csv
@ -1,101 +0,0 @@
 product_id,product_category_name,product_name_lenght,product_description_lenght,product_photos_qty,product_weight_g,product_length_cm,product_height_cm,product_width_cm
 278b3c6462e86b4556b99989513ddf73,eletroportateis,58.0,587.0,3.0,350.0,20.0,20.0,20.0
 3014e35fd70fce29095ced5cdc89f4ce,telefonia,51.0,244.0,1.0,125.0,17.0,10.0,14.0
 15a9e834e89eab39d973492882c658d6,cama_mesa_banho,52.0,530.0,6.0,949.0,30.0,20.0,26.0
 db56f6d2b04c89eae4daba188842fd7b,malas_acessorios,56.0,450.0,3.0,12450.0,40.0,25.0,57.0
 154e7e31ebfa092203795c972e5804a6,beleza_saude,48.0,575.0,1.0,100.0,20.0,15.0,15.0
 20a8603c265d777e25da064113d556f5,telefonia,59.0,474.0,3.0,475.0,17.0,14.0,14.0
 87285b34884572647811a353c7ac498a,utilidades_domesticas,40.0,268.0,4.0,500.0,19.0,8.0,13.0
 7c1bd920dbdf22470b68bde975dd3ccf,beleza_saude,59.0,492.0,2.0,200.0,22.0,10.0,18.0
 b37b72d5a56f887725c2862184b8cab8,telefonia,59.0,566.0,1.0,150.0,19.0,4.0,11.0
 ac1789e492dcd698c5c10b97a671243a,moveis_decoracao,41.0,432.0,2.0,300.0,35.0,35.0,15.0
 f410090aec61f7c73748ca894286edcd,papelaria,60.0,1847.0,3.0,450.0,35.0,50.0,12.0
 e251ebd2858be1aa7d9b2087a6992580,ferramentas_jardim,34.0,511.0,4.0,8875.0,40.0,14.0,43.0
 1501b0033c68a37fa9560033a440e529,eletroportateis,58.0,1160.0,6.0,410.0,24.0,22.0,17.0
 43ee88561093499d9e571d4db5f20b79,moveis_decoracao,39.0,161.0,3.0,200.0,20.0,20.0,20.0
 f8a8f05a35976a91aed5cccc3992c357,moveis_decoracao,63.0,418.0,1.0,1500.0,45.0,15.0,35.0
 7564c1759c04fc0a38f2aa84f7a370ee,construcao_ferramentas_construcao,59.0,2432.0,3.0,1200.0,16.0,11.0,11.0
 ebd7c847c1e1cb69ec374ae0ebee1f4c,moveis_decoracao,50.0,228.0,3.0,1200.0,40.0,15.0,30.0
 2b4609f8948be18874494203496bc318,beleza_saude,59.0,492.0,3.0,250.0,22.0,10.0,18.0
 c7df652246ed7b3300aaf46960c141e4,beleza_saude,28.0,1455.0,1.0,683.0,29.0,15.0,22.0
 2d8f2be4f08788ee3bf5356af2b2ee6c,climatizacao,52.0,331.0,4.0,100.0,27.0,13.0,17.0
 f7d7b5c58704fb359a74580622800051,cama_mesa_banho,53.0,223.0,1.0,950.0,45.0,15.0,35.0
 304fad8dc4d2012dc4062839972f2d96,construcao_ferramentas_construcao,59.0,1775.0,2.0,1700.0,16.0,11.0,11.0
 60184212dae4e6b0da32bf54271a8c4a,relogios_presentes,59.0,476.0,2.0,394.0,17.0,11.0,14.0
 d1c427060a0f73f6b889a5c7c61f2ac4,informatica_acessorios,59.0,1893.0,1.0,6550.0,20.0,20.0,20.0
 cf944645d4ff2a3eed3ae17f641ea861,fashion_underwear_e_moda_praia,52.0,579.0,1.0,450.0,42.0,4.0,14.0
 6893767814d1ac82a81bcd365e1cc918,eletronicos,26.0,511.0,1.0,200.0,25.0,7.0,16.0
 d70f38e7f79c630f8ea00c993897042c,bebes,53.0,233.0,1.0,10950.0,41.0,40.0,40.0
 4520766ec412348b8d4caa5e8a18c464,automotivo,59.0,956.0,1.0,50.0,16.0,16.0,17.0
 5ac9d9e379c606e36a8094a6046f75dc,beleza_saude,46.0,2345.0,6.0,525.0,21.0,16.0,13.0
 9d2ff462feaaf88912539b8647e17ab4,informatica_acessorios,42.0,315.0,1.0,813.0,32.0,16.0,16.0
 72d3bf1d3a790f8874096fcf860e3eff,brinquedos,57.0,341.0,2.0,583.0,20.0,21.0,20.0
 64d0feb1bcf9c7fe7b5dad3271c10910,moveis_decoracao,58.0,696.0,7.0,750.0,25.0,15.0,35.0
 08574b074924071f4e201e151b152b4e,ferramentas_jardim,36.0,450.0,1.0,9000.0,42.0,12.0,39.0
 30469bb5ea377eae7121981e2f0778e4,esporte_lazer,57.0,574.0,4.0,5950.0,20.0,30.0,80.0
 9b37a918bcf2c8e1064e867cf1df4637,eletronicos,57.0,1710.0,6.0,1207.0,20.0,10.0,20.0
 65266b2da20d04dbe00c5c2d3bb7859e,papelaria,38.0,316.0,4.0,250.0,51.0,15.0,15.0
 00baba5b58e274d0332a0c8a0a66f877,perfumaria,27.0,406.0,4.0,200.0,18.0,7.0,12.0
 aa4383b373c6aca5d8797843e5594415,automotivo,46.0,232.0,1.0,420.0,24.0,19.0,21.0
 f497ba62f1d6b4f6a3a3266fa8623ad3,beleza_saude,45.0,1276.0,1.0,83.0,13.0,8.0,12.0
 a5a0e71a81ae65aa335e71c06261e260,utilidades_domesticas,57.0,698.0,3.0,705.0,34.0,22.0,28.0
 c0db539123a403f670c50237d970b215,ferramentas_jardim,56.0,1313.0,2.0,850.0,20.0,20.0,20.0
 aca2eb7d00ea1a7b8ebd4e68314663af,moveis_decoracao,44.0,903.0,6.0,2600.0,50.0,10.0,30.0
 c6c1f263e076bd9c1f1640250a5d0c29,perfumaria,32.0,102.0,1.0,425.0,24.0,12.0,16.0
 6b64362e89896be7589621df54be089e,moveis_decoracao,57.0,2435.0,2.0,3000.0,69.0,11.0,11.0
 f7e0fa615b386bc9a8b9eb52bc1fff76,informatica_acessorios,59.0,2574.0,1.0,325.0,21.0,21.0,21.0
 7a10781637204d8d10485c71a6108a2e,relogios_presentes,42.0,236.0,1.0,342.0,18.0,13.0,15.0
 9451e630d725c4bb7a5a206b48b99486,bebes,52.0,300.0,1.0,350.0,31.0,10.0,12.0
 ba4bfbf74dbe7ab37e263b9326da0523,esporte_lazer,60.0,521.0,1.0,650.0,24.0,10.0,20.0
 90b58782fdd04cb829667fcc41fb65f5,moveis_escritorio,34.0,794.0,1.0,7417.0,102.0,46.0,11.0
 79da264732f717f10ebf5d102aa6c32a,telefonia,59.0,675.0,5.0,150.0,17.0,8.0,14.0
 c827fb43ad0fb8708f34c2911fdc164b,esporte_lazer,53.0,699.0,1.0,10600.0,26.0,30.0,26.0
 ba74c6b75d2ad7503175809688d5a03c,relogios_presentes,59.0,1088.0,2.0,292.0,17.0,8.0,12.0
 c1234c80dafde7ef3311b3eabd5069ed,cama_mesa_banho,55.0,122.0,1.0,300.0,20.0,2.0,15.0
 5e2ba75ad255ff60b1c76c5bf526ae9b,beleza_saude,47.0,1346.0,2.0,500.0,20.0,8.0,20.0
 ad1128daf194f4b6ac4256e16233497c,telefonia,32.0,580.0,2.0,100.0,16.0,3.0,11.0
 163e6400e6dadd0fe04775c5e9331fda,bebes,29.0,462.0,1.0,500.0,47.0,10.0,36.0
 009c09f439988bc06a93d6b8186dce73,perfumaria,39.0,991.0,3.0,150.0,20.0,20.0,20.0
 7b717060aa783eb7f23a747a3a733dd7,cool_stuff,46.0,595.0,2.0,500.0,16.0,12.0,22.0
 9ecadb84c81da840dbf3564378b586e9,moveis_decoracao,41.0,789.0,1.0,950.0,20.0,35.0,20.0
 060cb19345d90064d1015407193c233d,automotivo,49.0,608.0,1.0,7150.0,65.0,10.0,65.0
 7f457254a89d62960399e075711b3deb,automotivo,60.0,558.0,6.0,300.0,17.0,4.0,12.0
 72a97c271b2e429974398f46b93ae530,perfumaria,59.0,685.0,1.0,450.0,16.0,17.0,16.0
 a47295965bd091207681b541b26e40a5,telefonia,60.0,818.0,6.0,300.0,17.0,4.0,12.0
 bb7181410b4e02f93f3697f765db53c7,bebes,36.0,1058.0,1.0,14950.0,77.0,20.0,53.0
 595fac2a385ac33a80bd5114aec74eb8,perfumaria,29.0,178.0,1.0,400.0,19.0,13.0,19.0
 22f5b63060a1185e5ec7721efd622321,cama_mesa_banho,32.0,606.0,2.0,400.0,90.0,6.0,12.0
 bff2010b28e8fbcff5a9db9d3fea5ac4,ferramentas_jardim,58.0,769.0,6.0,850.0,90.0,20.0,20.0
 c35498fbb4358837ae16850f50c3fd22,telefonia,59.0,973.0,1.0,325.0,19.0,8.0,22.0
 4ce99ff9dcb7821acd8e599d5d4a6531,esporte_lazer,51.0,192.0,2.0,450.0,35.0,10.0,11.0
 b3be1f83cef05668c25e134852d44545,cama_mesa_banho,52.0,413.0,1.0,1750.0,42.0,11.0,36.0
 c3ba4e8d3cb30049213b682e751e9d00,relogios_presentes,58.0,737.0,3.0,350.0,16.0,2.0,20.0
 a1804276d9941ac0733cfd409f5206eb,,,,,600.0,35.0,35.0,15.0
 cafd558df4c3c9d1c338ba6930ea9a62,bebes,45.0,1009.0,1.0,16450.0,44.0,70.0,32.0
 d457916b4fdc60154ed93b5dd3e6fd69,construcao_ferramentas_construcao,57.0,424.0,1.0,10000.0,30.0,20.0,30.0
 638bbb2a5e4f360b71f332ddfebfd672,construcao_ferramentas_construcao,38.0,143.0,2.0,20850.0,100.0,25.0,50.0
 75d6b6963340c6063f7f4cfcccfe6a30,perfumaria,51.0,999.0,2.0,400.0,18.0,11.0,20.0
 a659cb33082b851fb87a33af8f0fff29,automotivo,60.0,380.0,1.0,150.0,16.0,6.0,11.0
 e932008cf0ea7c93a077dd8d7e5f49eb,climatizacao,60.0,3270.0,4.0,7350.0,105.0,10.0,40.0
 89321f94e35fc6d7903d36f74e351d40,alimentos,59.0,982.0,1.0,150.0,17.0,13.0,13.0
 0cd9f302c8a5b076ffa5c3567c6705fd,informatica_acessorios,22.0,716.0,2.0,200.0,36.0,2.0,28.0
 c50ca07e9e4db9ea5011f06802c0aea0,beleza_saude,59.0,1782.0,1.0,125.0,25.0,14.0,18.0
 e99d69efe684efaa643f99805f7c81bc,papelaria,56.0,115.0,1.0,600.0,33.0,13.0,25.0
 be021417a6acb56b9b50d3fd2714baa8,utilidades_domesticas,48.0,664.0,6.0,14300.0,38.0,34.0,34.0
 cce679660c66e6fbd5c8091dfd29e9cd,cama_mesa_banho,43.0,125.0,1.0,250.0,40.0,4.0,30.0
 3dd6c9d499e7c311a29e08afe1fd8fc6,cool_stuff,60.0,396.0,4.0,250.0,19.0,12.0,12.0
 9a469eaf45dfbc43d39ba1977a3c07af,cama_mesa_banho,44.0,192.0,1.0,700.0,40.0,4.0,30.0
 cac9e5692471a0700418aa3400b9b2b1,bebes,57.0,2440.0,1.0,375.0,29.0,14.0,20.0
 5526b1ae9ab2688cf600783cece249df,informatica_acessorios,49.0,385.0,1.0,200.0,16.0,16.0,16.0
 9a78fb9862b10749a117f7fc3c31f051,moveis_escritorio,45.0,527.0,1.0,9750.0,42.0,41.0,42.0
 1deda1acffb44ed38494667d7e49a9f3,esporte_lazer,53.0,891.0,2.0,1150.0,27.0,12.0,17.0
 10adb53d8faa890ca7c2f0cbcb68d777,cama_mesa_banho,52.0,155.0,1.0,200.0,16.0,10.0,16.0
 d0b61bfb1de832b15ba9d266ca96e5b0,pet_shop,59.0,468.0,3.0,450.0,30.0,10.0,20.0
 8c591ab0ca519558779df02023177f44,ferramentas_jardim,47.0,1893.0,1.0,6050.0,20.0,20.0,20.0
 cd935d283d47f1050c505e1c39c48b67,esporte_lazer,32.0,658.0,5.0,281.0,30.0,14.0,25.0
 a01d1cbb398e386a4a8f8364401a7584,esporte_lazer,58.0,757.0,2.0,500.0,50.0,5.0,30.0
 12087840651e83b48206b82c213b76fd,esporte_lazer,27.0,521.0,1.0,1813.0,30.0,13.0,28.0
 f48eb5c2fde13ca63664f0bb05f55346,esporte_lazer,60.0,1153.0,2.0,100.0,20.0,11.0,11.0
 69d980b4120a76616d7b237d731d6156,relogios_presentes,60.0,1362.0,3.0,600.0,16.0,11.0,12.0
 39a9942865c056ed2006a5e8c11d9537,brinquedos,47.0,556.0,5.0,800.0,37.0,14.0,37.0
 f35927953ed82e19d06ad3aac2f06353,livros_interesse_geral,39.0,724.0,1.0,450.0,20.0,20.0,20.0
--- a/data/orders/sellers.csv
+++ b/data/orders/sellers.csv
@ -1,88 +0,0 @@
 seller_id,seller_zip_code_prefix,seller_city,seller_state
 669ae81880e08f269a64487cfb287169,89160,rio do sul,SC
 817245bcc3badd82bbd222e0366951a6,17056,bauru,SP
 7d13fca15225358621be4086e1eb0964,14050,ribeirao preto,SP
 a3a38f4affed601eb87a97788c949667,89204,joinville,SC
 744dac408745240a2c2528fb1b6028f3,83408,colombo,PR
 8b321bb669392f5163d04c59e235e066,1212,sao paulo,SP
 76d64c4aca3a7baf218bf93ef7fa768d,80215,curitiba,PR
 537eb890efff034a88679788b647c564,20270,rio de janeiro,RJ
 955fee9216a65b617aa5c0531780ce60,4782,sao paulo,SP
 ba5daa4041e1f15cdf34b76e3e18a450,4363,sao paulo,SP
 d3f39f05462b79a4562d35893a28f159,13730,mococa,SP
 d1ef48b38baca7e831711c4a0aeb398f,86800,apucarana,PR
 f7ba60f8c3f99e7ee4042fdef03b70c4,9628,sao bernardo do campo,SP
 f84a00e60c73a49e7e851c9bdca3a5bb,20756,rio de janeiro,RJ
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 d673a59aac7a70d8b01e6902bf090a11,14940,ibitinga,SP
 f52c2422904463fdd7741f99045fecb6,9230,santo andre/sao paulo,SP
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 5b925e1d006e9476d738aa200751b73b,4567,sao paulo,SP
 fe2032dab1a61af8794248c8196565c9,13030,campinas,SP
 2a84855fd20af891be03bc5924d2b453,30111,belo horizonte,MG
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 5656537e588803a555b8eb41f07a944b,72015,brasilia,DF
 5dceca129747e92ff8ef7a997dc4f8ca,13450,santa barbara d´oeste,SP
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 a5cba26a62b8b4d0145b68b841e62e7f,87303,campo mourao,PR
 1025f0e2d44d7041d6cf58b6550e0bfa,3204,sao paulo,SP
 a1043bafd471dff536d0c462352beb48,37175,ilicinea,MG
 f8db351d8c4c4c22c6835c19a46f01b0,13324,salto,SP
 1ca7077d890b907f89be8c954a02686a,6506,santana de parnaiba,SP
 89a51f50b8095ea78d5768f34c13a76f,71931,brasilia,DF
 d566c37fa119d5e66c4e9052e83ee4ea,4131,sao paulo,SP
 70a12e78e608ac31179aea7f8422044b,12327,jacarei,SP
 66922902710d126a0e7d26b0e3805106,31842,belo horizonte,MG
 1900267e848ceeba8fa32d80c1a5f5a8,14940,ibitinga,SP
 63b9ae557efed31d1f7687917d248a8d,13720,sao jose do rio pardo,SP
--- a/data/titanic.csv
+++ b/data/titanic.csv
@ -1,892 +0,0 @@
 PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
 57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
 58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
 59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
 60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
 61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
 62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
 63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
 64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
 65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
 66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
 67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
 68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
 69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
 70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
 71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
 72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
 73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
 74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
 75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
 76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
 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
 79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
 80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
 81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
 82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
 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
 93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
 94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
 95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
 96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
 97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
 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
 100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
 101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
 102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
 103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
 104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
 105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
 106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
 107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
 108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
 109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
 110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
 111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
 112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
 113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
 114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
 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
 117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
 118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
 119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
 120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
 121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
 122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
 123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
 124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
 125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
 126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
 127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
 128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
 129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
 130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
 131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
 132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
 133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
 134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
 135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
 136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
 137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
 138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
 139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
 140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
 141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
 142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
 143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
 144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
 145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
 146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
 147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
 148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
 149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
 150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
 151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
 152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
 153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
 154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
 155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
 156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
 157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
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 838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
 839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
 840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
 841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
 842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
 843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
 844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
 845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
 846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
 847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
 848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
 849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
 850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
 851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
 852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
 853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
 854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
--- a/docs/path1.png
+++ b/docs/path1.png
--- a/docs/path2.png
+++ b/docs/path2.png
--- a/docs/path3.png
+++ b/docs/path3.png
--- a/docs/path4.png
+++ b/docs/path4.png
--- a/lab1.ipynb
+++ b/lab1.ipynb
--- a/labs/lab1/healthcareDataset.csv
+++ b/labs/lab1/healthcareDataset.csv
--- a/labs/lab1/lab1.ipynb
+++ b/labs/lab1/lab1.ipynb
--- a/labs/lab1/newHealthcareDataset.csv
+++ b/labs/lab1/newHealthcareDataset.csv
--- a/labs/lab2/lab2.ipynb
+++ b/labs/lab2/lab2.ipynb
@ -1,58 +0,0 @@
 {
 "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": {
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   "display_name": ".venv",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "nbformat": 4,
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--- a/lec1.ipynb
+++ b/lec1.ipynb
--- a/lec2.ipynb
+++ b/lec2.ipynb
@ -1,959 +0,0 @@
 {
 "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": {
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       "  <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",
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       "      <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",
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       "      <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",
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       "        vertical-align: middle;\n",
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       "\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",
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       "      <th></th>\n",
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       "      <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",
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       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
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       "    <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",
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 }
--- a/lec3.ipynb
+++ b/lec3.ipynb
--- a/poetry.lock
+++ b/poetry.lock
--- a/poetry.toml
+++ b/poetry.toml
@ -1,2 +0,0 @@
 [virtualenvs]
 in-project = true
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,29 +0,0 @@
 [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"
--- a/run.py
+++ b/run.py
@ -1,16 +0,0 @@
 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()
`@ -1 +1,2 @@`
	`Лабораторные работы по МИИ`	`# ISEbd-31_Baygulov_A.A._MAI`