{ "cells": [ { "cell_type": "markdown", "id": "0c6ca4a7", "metadata": {}, "source": [ "# Práctica 1: Limpieza y transformación de datos en Online Retail\n", "\n", "**Alumno:** Eduardo Alonso Sánchez \n", "**Boleta:** 2023630155 \n", "**Grupo:** 7CV4 \n", "**Unidad de aprendizaje:** C703 - Big Data | Big Data \n", "**Docente:** Tania Rodríguez Sarabia \n", "**Periodo escolar:** 20271\n", "\n", "La boleta termina en **0155**. La consigna pide convertir los últimos cuatro dígitos a entero, por lo que `digitos_boleta = 155` se usa para módulos y paridad. Se conserva `identificador = \"0155\"` para búsquedas, etiquetas y nombres de archivo. Así se evita perder el cero inicial en las salidas legibles.\n", "\n", "Fuentes técnicas: [UCI Online Retail](https://archive.ics.uci.edu/dataset/352/online%2Bretail), [repositorio de Databricks indicado en la consigna](https://github.com/databricks/Spark-The-Definitive-Guide/tree/master/data/retail-data/all), [duplicados en pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop_duplicates.html), [conversión de fechas](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html) y [recorte con `clip`](https://pandas.pydata.org/docs/reference/api/pandas.Series.clip.html)." ] }, { "cell_type": "code", "execution_count": 1, "id": "d661365d", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:14.844903Z", "iopub.status.busy": "2026-09-06T23:48:14.844788Z", "iopub.status.idle": "2026-09-06T23:48:15.453952Z", "shell.execute_reply": "2026-09-06T23:48:15.453354Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Alumno: Eduardo Alonso Sánchez\n", "Boleta: 2023630155\n", "Dígitos únicos (entero): 155\n", "Identificador escrito: 0155\n", "Fecha de ejecución: 2026-09-06 17:48:15.451783\n", "Python: 3.12.14 | pandas: 2.2.3 | numpy: 2.3.5\n" ] } ], "source": [ "from pathlib import Path\n", "from urllib.request import urlretrieve\n", "import hashlib\n", "import json\n", "import platform\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from IPython.display import display\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "pd.set_option(\"display.max_rows\", 160)\n", "pd.set_option(\"display.max_columns\", 30)\n", "pd.set_option(\"display.width\", 160)\n", "sns.set_theme(style=\"whitegrid\", palette=\"deep\")\n", "\n", "nombre = \"Eduardo Alonso Sánchez\"\n", "boleta_completa = \"2023630155\"\n", "identificador = boleta_completa[-4:]\n", "digitos_boleta = int(identificador)\n", "\n", "ROOT = Path.cwd()\n", "RAW_DIR = ROOT / \"datos\" / \"raw\"\n", "PROCESSED_DIR = ROOT / \"datos\" / \"processed\"\n", "ASSETS_DIR = ROOT / \"assets\"\n", "for carpeta in (RAW_DIR, PROCESSED_DIR, ASSETS_DIR):\n", " carpeta.mkdir(parents=True, exist_ok=True)\n", "\n", "DATA_URL = \"https://raw.githubusercontent.com/databricks/Spark-The-Definitive-Guide/master/data/retail-data/all/online-retail-dataset.csv\"\n", "RAW_PATH = RAW_DIR / \"online-retail-dataset.csv\"\n", "if not RAW_PATH.exists():\n", " print(\"Descargando dataset indicado por la consigna...\")\n", " urlretrieve(DATA_URL, RAW_PATH)\n", "\n", "print(f\"Alumno: {nombre}\")\n", "print(f\"Boleta: {boleta_completa}\")\n", "print(f\"Dígitos únicos (entero): {digitos_boleta}\")\n", "print(f\"Identificador escrito: {identificador}\")\n", "print(f\"Fecha de ejecución: {pd.Timestamp.now()}\")\n", "print(f\"Python: {platform.python_version()} | pandas: {pd.__version__} | numpy: {np.__version__}\")" ] }, { "cell_type": "markdown", "id": "af8e486e", "metadata": {}, "source": [ "## Punto 2. Carga y exploración inicial" ] }, { "cell_type": "code", "execution_count": 2, "id": "b9265dd1", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:15.455519Z", "iopub.status.busy": "2026-09-06T23:48:15.455252Z", "iopub.status.idle": "2026-09-06T23:48:16.200250Z", "shell.execute_reply": "2026-09-06T23:48:16.199776Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Archivo: /home/eddndev/Documents/ESCOM/BD/01-limpieza-datos-retail/datos/raw/online-retail-dataset.csv\n", "SHA-256: a2f79bbdd4463df6db8a3f5a50b9c980ae8f645a370bf5e2c0d6097f9e817b05\n", "Dimensión original: (541909, 8)\n", "\n", "Primeras 10 filas:\n" ] }, { "data": { "text/html": [ "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
053636585123AWHITE HANGING HEART T-LIGHT HOLDER612/1/2010 8:262.5517850.0United Kingdom
153636571053WHITE METAL LANTERN612/1/2010 8:263.3917850.0United Kingdom
253636584406BCREAM CUPID HEARTS COAT HANGER812/1/2010 8:262.7517850.0United Kingdom
353636584029GKNITTED UNION FLAG HOT WATER BOTTLE612/1/2010 8:263.3917850.0United Kingdom
453636584029ERED WOOLLY HOTTIE WHITE HEART.612/1/2010 8:263.3917850.0United Kingdom
553636522752SET 7 BABUSHKA NESTING BOXES212/1/2010 8:267.6517850.0United Kingdom
653636521730GLASS STAR FROSTED T-LIGHT HOLDER612/1/2010 8:264.2517850.0United Kingdom
753636622633HAND WARMER UNION JACK612/1/2010 8:281.8517850.0United Kingdom
853636622632HAND WARMER RED POLKA DOT612/1/2010 8:281.8517850.0United Kingdom
953636784879ASSORTED COLOUR BIRD ORNAMENT3212/1/2010 8:341.6913047.0United Kingdom
\n", "
" ], "text/plain": [ " InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice CustomerID Country\n", "0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 12/1/2010 8:26 2.55 17850.0 United Kingdom\n", "1 536365 71053 WHITE METAL LANTERN 6 12/1/2010 8:26 3.39 17850.0 United Kingdom\n", "2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 12/1/2010 8:26 2.75 17850.0 United Kingdom\n", "3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 12/1/2010 8:26 3.39 17850.0 United Kingdom\n", "4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 12/1/2010 8:26 3.39 17850.0 United Kingdom\n", "5 536365 22752 SET 7 BABUSHKA NESTING BOXES 2 12/1/2010 8:26 7.65 17850.0 United Kingdom\n", "6 536365 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6 12/1/2010 8:26 4.25 17850.0 United Kingdom\n", "7 536366 22633 HAND WARMER UNION JACK 6 12/1/2010 8:28 1.85 17850.0 United Kingdom\n", "8 536366 22632 HAND WARMER RED POLKA DOT 6 12/1/2010 8:28 1.85 17850.0 United Kingdom\n", "9 536367 84879 ASSORTED COLOUR BIRD ORNAMENT 32 12/1/2010 8:34 1.69 13047.0 United Kingdom" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Información del dataframe:\n", "\n", "RangeIndex: 541909 entries, 0 to 541908\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 InvoiceNo 541909 non-null string \n", " 1 StockCode 541909 non-null string \n", " 2 Description 540455 non-null object \n", " 3 Quantity 541909 non-null int64 \n", " 4 InvoiceDate 541909 non-null object \n", " 5 UnitPrice 541909 non-null float64\n", " 6 CustomerID 406829 non-null float64\n", " 7 Country 541909 non-null object \n", "dtypes: float64(2), int64(1), object(3), string(2)\n", "memory usage: 33.1+ MB\n", "\n", "Estadísticas descriptivas:\n" ] }, { "data": { "text/html": [ "
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countuniquetopfreqmeanstdmin25%50%75%max
InvoiceNo541909259005735851114NaNNaNNaNNaNNaNNaNNaN
StockCode541909407085123A2313NaNNaNNaNNaNNaNNaNNaN
Description5404554223WHITE HANGING HEART T-LIGHT HOLDER2369NaNNaNNaNNaNNaNNaNNaN
Quantity541909.0NaNNaNNaN9.55225218.081158-80995.01.03.010.080995.0
InvoiceDate5419092326010/31/2011 14:411114NaNNaNNaNNaNNaNNaNNaN
UnitPrice541909.0NaNNaNNaN4.61111496.759853-11062.061.252.084.1338970.0
CustomerID406829.0NaNNaNNaN15287.690571713.60030312346.013953.015152.016791.018287.0
Country54190938United Kingdom495478NaNNaNNaNNaNNaNNaNNaN
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" ], "text/plain": [ " count unique top freq mean std min 25% 50% 75% max\n", "InvoiceNo 541909 25900 573585 1114 NaN NaN NaN NaN NaN NaN NaN\n", "StockCode 541909 4070 85123A 2313 NaN NaN NaN NaN NaN NaN NaN\n", "Description 540455 4223 WHITE HANGING HEART T-LIGHT HOLDER 2369 NaN NaN NaN NaN NaN NaN NaN\n", "Quantity 541909.0 NaN NaN NaN 9.55225 218.081158 -80995.0 1.0 3.0 10.0 80995.0\n", "InvoiceDate 541909 23260 10/31/2011 14:41 1114 NaN NaN NaN NaN NaN NaN NaN\n", "UnitPrice 541909.0 NaN NaN NaN 4.611114 96.759853 -11062.06 1.25 2.08 4.13 38970.0\n", "CustomerID 406829.0 NaN NaN NaN 15287.69057 1713.600303 12346.0 13953.0 15152.0 16791.0 18287.0\n", "Country 541909 38 United Kingdom 495478 NaN NaN NaN NaN NaN NaN NaN" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sha256_raw = hashlib.sha256(RAW_PATH.read_bytes()).hexdigest()\n", "df = pd.read_csv(\n", " RAW_PATH,\n", " encoding=\"utf-8\",\n", " dtype={\"InvoiceNo\": \"string\", \"StockCode\": \"string\"},\n", ")\n", "columnas_fuente = df.columns.tolist()\n", "filas_originales = len(df)\n", "\n", "print(f\"Archivo: {RAW_PATH}\")\n", "print(f\"SHA-256: {sha256_raw}\")\n", "print(f\"Dimensión original: {df.shape}\")\n", "print(\"\\nPrimeras 10 filas:\")\n", "display(df.head(10))\n", "print(\"\\nInformación del dataframe:\")\n", "df.info()\n", "print(\"\\nEstadísticas descriptivas:\")\n", "display(df.describe(include=\"all\").transpose())" ] }, { "cell_type": "code", "execution_count": 3, "id": "c8932450", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:16.201539Z", "iopub.status.busy": "2026-09-06T23:48:16.201426Z", "iopub.status.idle": "2026-09-06T23:48:16.220814Z", "shell.execute_reply": "2026-09-06T23:48:16.220405Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N = 155 % 100 + 100 = 155\n", "df_visualizacion se limita a la presentación; la limpieza continúa sobre df completo.\n" ] }, { "data": { "text/html": [ "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
053636585123AWHITE HANGING HEART T-LIGHT HOLDER612/1/2010 8:262.5517850.0United Kingdom
153636571053WHITE METAL LANTERN612/1/2010 8:263.3917850.0United Kingdom
253636584406BCREAM CUPID HEARTS COAT HANGER812/1/2010 8:262.7517850.0United Kingdom
353636584029GKNITTED UNION FLAG HOT WATER BOTTLE612/1/2010 8:263.3917850.0United Kingdom
453636584029ERED WOOLLY HOTTIE WHITE HEART.612/1/2010 8:263.3917850.0United Kingdom
553636522752SET 7 BABUSHKA NESTING BOXES212/1/2010 8:267.6517850.0United Kingdom
653636521730GLASS STAR FROSTED T-LIGHT HOLDER612/1/2010 8:264.2517850.0United Kingdom
753636622633HAND WARMER UNION JACK612/1/2010 8:281.8517850.0United Kingdom
853636622632HAND WARMER RED POLKA DOT612/1/2010 8:281.8517850.0United Kingdom
953636784879ASSORTED COLOUR BIRD ORNAMENT3212/1/2010 8:341.6913047.0United Kingdom
1053636722745POPPY'S PLAYHOUSE BEDROOM612/1/2010 8:342.1013047.0United Kingdom
1153636722748POPPY'S PLAYHOUSE KITCHEN612/1/2010 8:342.1013047.0United Kingdom
1253636722749FELTCRAFT PRINCESS CHARLOTTE DOLL812/1/2010 8:343.7513047.0United Kingdom
1353636722310IVORY KNITTED MUG COSY612/1/2010 8:341.6513047.0United Kingdom
1453636784969BOX OF 6 ASSORTED COLOUR TEASPOONS612/1/2010 8:344.2513047.0United Kingdom
1553636722623BOX OF VINTAGE JIGSAW BLOCKS312/1/2010 8:344.9513047.0United Kingdom
1653636722622BOX OF VINTAGE ALPHABET BLOCKS212/1/2010 8:349.9513047.0United Kingdom
1753636721754HOME BUILDING BLOCK WORD312/1/2010 8:345.9513047.0United Kingdom
1853636721755LOVE BUILDING BLOCK WORD312/1/2010 8:345.9513047.0United Kingdom
1953636721777RECIPE BOX WITH METAL HEART412/1/2010 8:347.9513047.0United Kingdom
2053636748187DOORMAT NEW ENGLAND412/1/2010 8:347.9513047.0United Kingdom
2153636822960JAM MAKING SET WITH JARS612/1/2010 8:344.2513047.0United Kingdom
2253636822913RED COAT RACK PARIS FASHION312/1/2010 8:344.9513047.0United Kingdom
2353636822912YELLOW COAT RACK PARIS FASHION312/1/2010 8:344.9513047.0United Kingdom
2453636822914BLUE COAT RACK PARIS FASHION312/1/2010 8:344.9513047.0United Kingdom
2553636921756BATH BUILDING BLOCK WORD312/1/2010 8:355.9513047.0United Kingdom
2653637022728ALARM CLOCK BAKELIKE PINK2412/1/2010 8:453.7512583.0France
2753637022727ALARM CLOCK BAKELIKE RED2412/1/2010 8:453.7512583.0France
2853637022726ALARM CLOCK BAKELIKE GREEN1212/1/2010 8:453.7512583.0France
2953637021724PANDA AND BUNNIES STICKER SHEET1212/1/2010 8:450.8512583.0France
3053637021883STARS GIFT TAPE2412/1/2010 8:450.6512583.0France
3153637010002INFLATABLE POLITICAL GLOBE4812/1/2010 8:450.8512583.0France
3253637021791VINTAGE HEADS AND TAILS CARD GAME2412/1/2010 8:451.2512583.0France
3353637021035SET/2 RED RETROSPOT TEA TOWELS1812/1/2010 8:452.9512583.0France
3453637022326ROUND SNACK BOXES SET OF4 WOODLAND2412/1/2010 8:452.9512583.0France
3553637022629SPACEBOY LUNCH BOX2412/1/2010 8:451.9512583.0France
3653637022659LUNCH BOX I LOVE LONDON2412/1/2010 8:451.9512583.0France
3753637022631CIRCUS PARADE LUNCH BOX2412/1/2010 8:451.9512583.0France
3853637022661CHARLOTTE BAG DOLLY GIRL DESIGN2012/1/2010 8:450.8512583.0France
3953637021731RED TOADSTOOL LED NIGHT LIGHT2412/1/2010 8:451.6512583.0France
4053637022900SET 2 TEA TOWELS I LOVE LONDON2412/1/2010 8:452.9512583.0France
4153637021913VINTAGE SEASIDE JIGSAW PUZZLES1212/1/2010 8:453.7512583.0France
4253637022540MINI JIGSAW CIRCUS PARADE2412/1/2010 8:450.4212583.0France
4353637022544MINI JIGSAW SPACEBOY2412/1/2010 8:450.4212583.0France
4453637022492MINI PAINT SET VINTAGE3612/1/2010 8:450.6512583.0France
45536370POSTPOSTAGE312/1/2010 8:4518.0012583.0France
4653637122086PAPER CHAIN KIT 50'S CHRISTMAS8012/1/2010 9:002.5513748.0United Kingdom
4753637222632HAND WARMER RED POLKA DOT612/1/2010 9:011.8517850.0United Kingdom
4853637222633HAND WARMER UNION JACK612/1/2010 9:011.8517850.0United Kingdom
4953637385123AWHITE HANGING HEART T-LIGHT HOLDER612/1/2010 9:022.5517850.0United Kingdom
5053637371053WHITE METAL LANTERN612/1/2010 9:023.3917850.0United Kingdom
5153637384406BCREAM CUPID HEARTS COAT HANGER812/1/2010 9:022.7517850.0United Kingdom
5253637320679EDWARDIAN PARASOL RED612/1/2010 9:024.9517850.0United Kingdom
5353637337370RETRO COFFEE MUGS ASSORTED612/1/2010 9:021.0617850.0United Kingdom
5453637321871SAVE THE PLANET MUG612/1/2010 9:021.0617850.0United Kingdom
5553637321071VINTAGE BILLBOARD DRINK ME MUG612/1/2010 9:021.0617850.0United Kingdom
5653637321068VINTAGE BILLBOARD LOVE/HATE MUG612/1/2010 9:021.0617850.0United Kingdom
5753637382483WOOD 2 DRAWER CABINET WHITE FINISH212/1/2010 9:024.9517850.0United Kingdom
5853637382486WOOD S/3 CABINET ANT WHITE FINISH412/1/2010 9:026.9517850.0United Kingdom
5953637382482WOODEN PICTURE FRAME WHITE FINISH612/1/2010 9:022.1017850.0United Kingdom
6053637382494LWOODEN FRAME ANTIQUE WHITE612/1/2010 9:022.5517850.0United Kingdom
6153637384029GKNITTED UNION FLAG HOT WATER BOTTLE612/1/2010 9:023.3917850.0United Kingdom
6253637384029ERED WOOLLY HOTTIE WHITE HEART.612/1/2010 9:023.3917850.0United Kingdom
6353637322752SET 7 BABUSHKA NESTING BOXES212/1/2010 9:027.6517850.0United Kingdom
6453637321730GLASS STAR FROSTED T-LIGHT HOLDER612/1/2010 9:024.2517850.0United Kingdom
6553637421258VICTORIAN SEWING BOX LARGE3212/1/2010 9:0910.9515100.0United Kingdom
6653637585123AWHITE HANGING HEART T-LIGHT HOLDER612/1/2010 9:322.5517850.0United Kingdom
6753637571053WHITE METAL LANTERN612/1/2010 9:323.3917850.0United Kingdom
6853637584406BCREAM CUPID HEARTS COAT HANGER812/1/2010 9:322.7517850.0United Kingdom
6953637520679EDWARDIAN PARASOL RED612/1/2010 9:324.9517850.0United Kingdom
7053637537370RETRO COFFEE MUGS ASSORTED612/1/2010 9:321.0617850.0United Kingdom
7153637521871SAVE THE PLANET MUG612/1/2010 9:321.0617850.0United Kingdom
7253637521071VINTAGE BILLBOARD DRINK ME MUG612/1/2010 9:321.0617850.0United Kingdom
7353637521068VINTAGE BILLBOARD LOVE/HATE MUG612/1/2010 9:321.0617850.0United Kingdom
7453637582483WOOD 2 DRAWER CABINET WHITE FINISH212/1/2010 9:324.9517850.0United Kingdom
7553637582486WOOD S/3 CABINET ANT WHITE FINISH412/1/2010 9:326.9517850.0United Kingdom
7653637582482WOODEN PICTURE FRAME WHITE FINISH612/1/2010 9:322.1017850.0United Kingdom
7753637582494LWOODEN FRAME ANTIQUE WHITE612/1/2010 9:322.5517850.0United Kingdom
7853637584029GKNITTED UNION FLAG HOT WATER BOTTLE612/1/2010 9:323.3917850.0United Kingdom
7953637584029ERED WOOLLY HOTTIE WHITE HEART.612/1/2010 9:323.3917850.0United Kingdom
8053637522752SET 7 BABUSHKA NESTING BOXES212/1/2010 9:327.6517850.0United Kingdom
8153637521730GLASS STAR FROSTED T-LIGHT HOLDER612/1/2010 9:324.2517850.0United Kingdom
8253637622114HOT WATER BOTTLE TEA AND SYMPATHY4812/1/2010 9:323.4515291.0United Kingdom
8353637621733RED HANGING HEART T-LIGHT HOLDER6412/1/2010 9:322.5515291.0United Kingdom
8453637722632HAND WARMER RED POLKA DOT612/1/2010 9:341.8517850.0United Kingdom
8553637722633HAND WARMER UNION JACK612/1/2010 9:341.8517850.0United Kingdom
8653637822386JUMBO BAG PINK POLKADOT1012/1/2010 9:371.9514688.0United Kingdom
8753637885099CJUMBO BAG BAROQUE BLACK WHITE1012/1/2010 9:371.9514688.0United Kingdom
8853637821033JUMBO BAG CHARLIE AND LOLA TOYS1012/1/2010 9:372.9514688.0United Kingdom
8953637820723STRAWBERRY CHARLOTTE BAG1012/1/2010 9:370.8514688.0United Kingdom
9053637884997BRED 3 PIECE RETROSPOT CUTLERY SET1212/1/2010 9:373.7514688.0United Kingdom
9153637884997CBLUE 3 PIECE POLKADOT CUTLERY SET612/1/2010 9:373.7514688.0United Kingdom
9253637821094SET/6 RED SPOTTY PAPER PLATES1212/1/2010 9:370.8514688.0United Kingdom
9353637820725LUNCH BAG RED RETROSPOT1012/1/2010 9:371.6514688.0United Kingdom
9453637821559STRAWBERRY LUNCH BOX WITH CUTLERY612/1/2010 9:372.5514688.0United Kingdom
9553637822352LUNCH BOX WITH CUTLERY RETROSPOT612/1/2010 9:372.5514688.0United Kingdom
9653637821212PACK OF 72 RETROSPOT CAKE CASES12012/1/2010 9:370.4214688.0United Kingdom
9753637821975PACK OF 60 DINOSAUR CAKE CASES2412/1/2010 9:370.5514688.0United Kingdom
9853637821977PACK OF 60 PINK PAISLEY CAKE CASES2412/1/2010 9:370.5514688.0United Kingdom
995363788499160 TEATIME FAIRY CAKE CASES2412/1/2010 9:370.5514688.0United Kingdom
10053637884519ATOMATO CHARLIE+LOLA COASTER SET612/1/2010 9:372.9514688.0United Kingdom
10153637885183BCHARLIE & LOLA WASTEPAPER BIN FLORA4812/1/2010 9:371.2514688.0United Kingdom
10253637885071BRED CHARLIE+LOLA PERSONAL DOORSIGN9612/1/2010 9:370.3814688.0United Kingdom
10353637821931JUMBO STORAGE BAG SUKI1012/1/2010 9:371.9514688.0United Kingdom
10453637821929JUMBO BAG PINK VINTAGE PAISLEY1012/1/2010 9:371.9514688.0United Kingdom
10553638022961JAM MAKING SET PRINTED2412/1/2010 9:411.4517809.0United Kingdom
10653638122139RETROSPOT TEA SET CERAMIC 11 PC2312/1/2010 9:414.2515311.0United Kingdom
10753638184854GIRLY PINK TOOL SET512/1/2010 9:414.9515311.0United Kingdom
10853638122411JUMBO SHOPPER VINTAGE RED PAISLEY1012/1/2010 9:411.9515311.0United Kingdom
10953638182567AIRLINE LOUNGE,METAL SIGN212/1/2010 9:412.1015311.0United Kingdom
11053638121672WHITE SPOT RED CERAMIC DRAWER KNOB612/1/2010 9:411.2515311.0United Kingdom
11153638122774RED DRAWER KNOB ACRYLIC EDWARDIAN2412/1/2010 9:411.2515311.0United Kingdom
11253638122771CLEAR DRAWER KNOB ACRYLIC EDWARDIAN2412/1/2010 9:411.2515311.0United Kingdom
11353638171270PHOTO CLIP LINE112/1/2010 9:411.2515311.0United Kingdom
11453638122262FELT EGG COSY CHICKEN112/1/2010 9:410.8515311.0United Kingdom
11553638122637PIGGY BANK RETROSPOT112/1/2010 9:412.5515311.0United Kingdom
11653638121934SKULL SHOULDER BAG1012/1/2010 9:411.6515311.0United Kingdom
11753638121169YOU'RE CONFUSING ME METAL SIGN312/1/2010 9:411.6915311.0United Kingdom
11853638121166COOK WITH WINE METAL SIGN112/1/2010 9:411.9515311.0United Kingdom
11953638121175GIN + TONIC DIET METAL SIGN212/1/2010 9:412.1015311.0United Kingdom
12053638137444AYELLOW BREAKFAST CUP AND SAUCER112/1/2010 9:412.9515311.0United Kingdom
12153638137444CPINK BREAKFAST CUP AND SAUCER112/1/2010 9:412.9515311.0United Kingdom
12253638122086PAPER CHAIN KIT 50'S CHRISTMAS412/1/2010 9:412.9515311.0United Kingdom
12353638122083PAPER CHAIN KIT RETROSPOT112/1/2010 9:412.9515311.0United Kingdom
12453638184971SSMALL HEART FLOWERS HOOK612/1/2010 9:410.8515311.0United Kingdom
12553638171270PHOTO CLIP LINE312/1/2010 9:411.2515311.0United Kingdom
12653638147580TEA TIME DES TEA COSY212/1/2010 9:412.5515311.0United Kingdom
12753638122261FELT EGG COSY WHITE RABBIT112/1/2010 9:410.8515311.0United Kingdom
12853638184832ZINC WILLIE WINKIE CANDLE STICK112/1/2010 9:410.8515311.0United Kingdom
12953638122644CERAMIC CHERRY CAKE MONEY BANK112/1/2010 9:411.4515311.0United Kingdom
13053638121533RETROSPOT LARGE MILK JUG112/1/2010 9:414.9515311.0United Kingdom
13153638121557SET OF 6 FUNKY BEAKERS212/1/2010 9:412.9515311.0United Kingdom
13253638115056BLEDWARDIAN PARASOL BLACK212/1/2010 9:415.9515311.0United Kingdom
13353638115056NEDWARDIAN PARASOL NATURAL212/1/2010 9:415.9515311.0United Kingdom
13453638122646CERAMIC STRAWBERRY CAKE MONEY BANK412/1/2010 9:411.4515311.0United Kingdom
13553638122176BLUE OWL SOFT TOY112/1/2010 9:412.9515311.0United Kingdom
13653638122438BALLOON ART MAKE YOUR OWN FLOWERS112/1/2010 9:411.9515311.0United Kingdom
13753638121731RED TOADSTOOL LED NIGHT LIGHT212/1/2010 9:411.6515311.0United Kingdom
13853638122778GLASS CLOCHE SMALL312/1/2010 9:413.9515311.0United Kingdom
13953638122719GUMBALL MONOCHROME COAT RACK3612/1/2010 9:411.0615311.0United Kingdom
14053638121523DOORMAT FANCY FONT HOME SWEET HOME1012/1/2010 9:416.7515311.0United Kingdom
141C536379DDiscount-112/1/2010 9:4127.5014527.0United Kingdom
14253638210002INFLATABLE POLITICAL GLOBE1212/1/2010 9:450.8516098.0United Kingdom
14353638221912VINTAGE SNAKES & LADDERS812/1/2010 9:453.7516098.0United Kingdom
14453638221832CHOCOLATE CALCULATOR1212/1/2010 9:451.6516098.0United Kingdom
14553638222411JUMBO SHOPPER VINTAGE RED PAISLEY1012/1/2010 9:451.9516098.0United Kingdom
14653638222379RECYCLING BAG RETROSPOT1012/1/2010 9:452.1016098.0United Kingdom
14753638222381TOY TIDY PINK POLKADOT5012/1/2010 9:451.8516098.0United Kingdom
14853638222798ANTIQUE GLASS DRESSING TABLE POT812/1/2010 9:452.9516098.0United Kingdom
14953638222726ALARM CLOCK BAKELIKE GREEN412/1/2010 9:453.7516098.0United Kingdom
15053638222926IVORY GIANT GARDEN THERMOMETER1212/1/2010 9:455.9516098.0United Kingdom
151536382228393 TIER CAKE TIN GREEN AND CREAM212/1/2010 9:4514.9516098.0United Kingdom
152536382228383 TIER CAKE TIN RED AND CREAM212/1/2010 9:4514.9516098.0United Kingdom
15353638222783SET 3 WICKER OVAL BASKETS W LIDS412/1/2010 9:4516.9516098.0United Kingdom
154C53638335004CSET OF 3 COLOURED FLYING DUCKS-112/1/2010 9:494.6515311.0United Kingdom
\n", "
" ], "text/plain": [ " InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice CustomerID Country\n", "0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 12/1/2010 8:26 2.55 17850.0 United Kingdom\n", "1 536365 71053 WHITE METAL LANTERN 6 12/1/2010 8:26 3.39 17850.0 United Kingdom\n", "2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 12/1/2010 8:26 2.75 17850.0 United Kingdom\n", "3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 12/1/2010 8:26 3.39 17850.0 United Kingdom\n", "4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 12/1/2010 8:26 3.39 17850.0 United Kingdom\n", "5 536365 22752 SET 7 BABUSHKA NESTING BOXES 2 12/1/2010 8:26 7.65 17850.0 United Kingdom\n", "6 536365 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6 12/1/2010 8:26 4.25 17850.0 United Kingdom\n", "7 536366 22633 HAND WARMER UNION JACK 6 12/1/2010 8:28 1.85 17850.0 United Kingdom\n", "8 536366 22632 HAND WARMER RED POLKA DOT 6 12/1/2010 8:28 1.85 17850.0 United Kingdom\n", "9 536367 84879 ASSORTED COLOUR BIRD ORNAMENT 32 12/1/2010 8:34 1.69 13047.0 United Kingdom\n", "10 536367 22745 POPPY'S PLAYHOUSE BEDROOM 6 12/1/2010 8:34 2.10 13047.0 United Kingdom\n", "11 536367 22748 POPPY'S PLAYHOUSE KITCHEN 6 12/1/2010 8:34 2.10 13047.0 United Kingdom\n", "12 536367 22749 FELTCRAFT PRINCESS CHARLOTTE DOLL 8 12/1/2010 8:34 3.75 13047.0 United Kingdom\n", "13 536367 22310 IVORY KNITTED MUG COSY 6 12/1/2010 8:34 1.65 13047.0 United Kingdom\n", "14 536367 84969 BOX OF 6 ASSORTED COLOUR TEASPOONS 6 12/1/2010 8:34 4.25 13047.0 United Kingdom\n", "15 536367 22623 BOX OF VINTAGE JIGSAW BLOCKS 3 12/1/2010 8:34 4.95 13047.0 United Kingdom\n", "16 536367 22622 BOX OF VINTAGE ALPHABET BLOCKS 2 12/1/2010 8:34 9.95 13047.0 United Kingdom\n", "17 536367 21754 HOME BUILDING BLOCK WORD 3 12/1/2010 8:34 5.95 13047.0 United Kingdom\n", "18 536367 21755 LOVE BUILDING BLOCK WORD 3 12/1/2010 8:34 5.95 13047.0 United Kingdom\n", "19 536367 21777 RECIPE BOX WITH METAL HEART 4 12/1/2010 8:34 7.95 13047.0 United Kingdom\n", "20 536367 48187 DOORMAT NEW ENGLAND 4 12/1/2010 8:34 7.95 13047.0 United Kingdom\n", "21 536368 22960 JAM MAKING SET WITH JARS 6 12/1/2010 8:34 4.25 13047.0 United Kingdom\n", "22 536368 22913 RED COAT RACK PARIS FASHION 3 12/1/2010 8:34 4.95 13047.0 United Kingdom\n", "23 536368 22912 YELLOW COAT RACK PARIS FASHION 3 12/1/2010 8:34 4.95 13047.0 United Kingdom\n", "24 536368 22914 BLUE COAT RACK PARIS FASHION 3 12/1/2010 8:34 4.95 13047.0 United Kingdom\n", "25 536369 21756 BATH BUILDING BLOCK WORD 3 12/1/2010 8:35 5.95 13047.0 United Kingdom\n", "26 536370 22728 ALARM CLOCK BAKELIKE PINK 24 12/1/2010 8:45 3.75 12583.0 France\n", "27 536370 22727 ALARM CLOCK BAKELIKE RED 24 12/1/2010 8:45 3.75 12583.0 France\n", "28 536370 22726 ALARM CLOCK BAKELIKE GREEN 12 12/1/2010 8:45 3.75 12583.0 France\n", "29 536370 21724 PANDA AND BUNNIES STICKER SHEET 12 12/1/2010 8:45 0.85 12583.0 France\n", "30 536370 21883 STARS GIFT TAPE 24 12/1/2010 8:45 0.65 12583.0 France\n", "31 536370 10002 INFLATABLE POLITICAL GLOBE 48 12/1/2010 8:45 0.85 12583.0 France\n", "32 536370 21791 VINTAGE HEADS AND TAILS CARD GAME 24 12/1/2010 8:45 1.25 12583.0 France\n", "33 536370 21035 SET/2 RED RETROSPOT TEA TOWELS 18 12/1/2010 8:45 2.95 12583.0 France\n", "34 536370 22326 ROUND SNACK BOXES SET OF4 WOODLAND 24 12/1/2010 8:45 2.95 12583.0 France\n", "35 536370 22629 SPACEBOY LUNCH BOX 24 12/1/2010 8:45 1.95 12583.0 France\n", "36 536370 22659 LUNCH BOX I LOVE LONDON 24 12/1/2010 8:45 1.95 12583.0 France\n", "37 536370 22631 CIRCUS PARADE LUNCH BOX 24 12/1/2010 8:45 1.95 12583.0 France\n", "38 536370 22661 CHARLOTTE BAG DOLLY GIRL DESIGN 20 12/1/2010 8:45 0.85 12583.0 France\n", "39 536370 21731 RED TOADSTOOL LED NIGHT LIGHT 24 12/1/2010 8:45 1.65 12583.0 France\n", "40 536370 22900 SET 2 TEA TOWELS I LOVE LONDON 24 12/1/2010 8:45 2.95 12583.0 France\n", "41 536370 21913 VINTAGE SEASIDE JIGSAW PUZZLES 12 12/1/2010 8:45 3.75 12583.0 France\n", "42 536370 22540 MINI JIGSAW CIRCUS PARADE 24 12/1/2010 8:45 0.42 12583.0 France\n", "43 536370 22544 MINI JIGSAW SPACEBOY 24 12/1/2010 8:45 0.42 12583.0 France\n", "44 536370 22492 MINI PAINT SET VINTAGE 36 12/1/2010 8:45 0.65 12583.0 France\n", "45 536370 POST POSTAGE 3 12/1/2010 8:45 18.00 12583.0 France\n", "46 536371 22086 PAPER CHAIN KIT 50'S CHRISTMAS 80 12/1/2010 9:00 2.55 13748.0 United Kingdom\n", "47 536372 22632 HAND WARMER RED POLKA DOT 6 12/1/2010 9:01 1.85 17850.0 United Kingdom\n", "48 536372 22633 HAND WARMER UNION JACK 6 12/1/2010 9:01 1.85 17850.0 United Kingdom\n", "49 536373 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 12/1/2010 9:02 2.55 17850.0 United Kingdom\n", "50 536373 71053 WHITE METAL LANTERN 6 12/1/2010 9:02 3.39 17850.0 United Kingdom\n", "51 536373 84406B CREAM CUPID HEARTS COAT HANGER 8 12/1/2010 9:02 2.75 17850.0 United Kingdom\n", "52 536373 20679 EDWARDIAN PARASOL RED 6 12/1/2010 9:02 4.95 17850.0 United Kingdom\n", "53 536373 37370 RETRO COFFEE MUGS ASSORTED 6 12/1/2010 9:02 1.06 17850.0 United Kingdom\n", "54 536373 21871 SAVE THE PLANET MUG 6 12/1/2010 9:02 1.06 17850.0 United Kingdom\n", "55 536373 21071 VINTAGE BILLBOARD DRINK ME MUG 6 12/1/2010 9:02 1.06 17850.0 United Kingdom\n", "56 536373 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6 12/1/2010 9:02 1.06 17850.0 United Kingdom\n", "57 536373 82483 WOOD 2 DRAWER CABINET WHITE FINISH 2 12/1/2010 9:02 4.95 17850.0 United Kingdom\n", "58 536373 82486 WOOD S/3 CABINET ANT WHITE FINISH 4 12/1/2010 9:02 6.95 17850.0 United Kingdom\n", "59 536373 82482 WOODEN PICTURE FRAME WHITE FINISH 6 12/1/2010 9:02 2.10 17850.0 United Kingdom\n", "60 536373 82494L WOODEN FRAME ANTIQUE WHITE 6 12/1/2010 9:02 2.55 17850.0 United Kingdom\n", "61 536373 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 12/1/2010 9:02 3.39 17850.0 United Kingdom\n", "62 536373 84029E RED WOOLLY HOTTIE WHITE HEART. 6 12/1/2010 9:02 3.39 17850.0 United Kingdom\n", "63 536373 22752 SET 7 BABUSHKA NESTING BOXES 2 12/1/2010 9:02 7.65 17850.0 United Kingdom\n", "64 536373 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6 12/1/2010 9:02 4.25 17850.0 United Kingdom\n", "65 536374 21258 VICTORIAN SEWING BOX LARGE 32 12/1/2010 9:09 10.95 15100.0 United Kingdom\n", "66 536375 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 12/1/2010 9:32 2.55 17850.0 United Kingdom\n", "67 536375 71053 WHITE METAL LANTERN 6 12/1/2010 9:32 3.39 17850.0 United Kingdom\n", "68 536375 84406B CREAM CUPID HEARTS COAT HANGER 8 12/1/2010 9:32 2.75 17850.0 United Kingdom\n", "69 536375 20679 EDWARDIAN PARASOL RED 6 12/1/2010 9:32 4.95 17850.0 United Kingdom\n", "70 536375 37370 RETRO COFFEE MUGS ASSORTED 6 12/1/2010 9:32 1.06 17850.0 United Kingdom\n", "71 536375 21871 SAVE THE PLANET MUG 6 12/1/2010 9:32 1.06 17850.0 United Kingdom\n", "72 536375 21071 VINTAGE BILLBOARD DRINK ME MUG 6 12/1/2010 9:32 1.06 17850.0 United Kingdom\n", "73 536375 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6 12/1/2010 9:32 1.06 17850.0 United Kingdom\n", "74 536375 82483 WOOD 2 DRAWER CABINET WHITE FINISH 2 12/1/2010 9:32 4.95 17850.0 United Kingdom\n", "75 536375 82486 WOOD S/3 CABINET ANT WHITE FINISH 4 12/1/2010 9:32 6.95 17850.0 United Kingdom\n", "76 536375 82482 WOODEN PICTURE FRAME WHITE FINISH 6 12/1/2010 9:32 2.10 17850.0 United Kingdom\n", "77 536375 82494L WOODEN FRAME ANTIQUE WHITE 6 12/1/2010 9:32 2.55 17850.0 United Kingdom\n", "78 536375 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 12/1/2010 9:32 3.39 17850.0 United Kingdom\n", "79 536375 84029E RED WOOLLY HOTTIE WHITE HEART. 6 12/1/2010 9:32 3.39 17850.0 United Kingdom\n", "80 536375 22752 SET 7 BABUSHKA NESTING BOXES 2 12/1/2010 9:32 7.65 17850.0 United Kingdom\n", "81 536375 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6 12/1/2010 9:32 4.25 17850.0 United Kingdom\n", "82 536376 22114 HOT WATER BOTTLE TEA AND SYMPATHY 48 12/1/2010 9:32 3.45 15291.0 United Kingdom\n", "83 536376 21733 RED HANGING HEART T-LIGHT HOLDER 64 12/1/2010 9:32 2.55 15291.0 United Kingdom\n", "84 536377 22632 HAND WARMER RED POLKA DOT 6 12/1/2010 9:34 1.85 17850.0 United Kingdom\n", "85 536377 22633 HAND WARMER UNION JACK 6 12/1/2010 9:34 1.85 17850.0 United Kingdom\n", "86 536378 22386 JUMBO BAG PINK POLKADOT 10 12/1/2010 9:37 1.95 14688.0 United Kingdom\n", "87 536378 85099C JUMBO BAG BAROQUE BLACK WHITE 10 12/1/2010 9:37 1.95 14688.0 United Kingdom\n", "88 536378 21033 JUMBO BAG CHARLIE AND LOLA TOYS 10 12/1/2010 9:37 2.95 14688.0 United Kingdom\n", "89 536378 20723 STRAWBERRY CHARLOTTE BAG 10 12/1/2010 9:37 0.85 14688.0 United Kingdom\n", "90 536378 84997B RED 3 PIECE RETROSPOT CUTLERY SET 12 12/1/2010 9:37 3.75 14688.0 United Kingdom\n", "91 536378 84997C BLUE 3 PIECE POLKADOT CUTLERY SET 6 12/1/2010 9:37 3.75 14688.0 United Kingdom\n", "92 536378 21094 SET/6 RED SPOTTY PAPER PLATES 12 12/1/2010 9:37 0.85 14688.0 United Kingdom\n", "93 536378 20725 LUNCH BAG RED RETROSPOT 10 12/1/2010 9:37 1.65 14688.0 United Kingdom\n", "94 536378 21559 STRAWBERRY LUNCH BOX WITH CUTLERY 6 12/1/2010 9:37 2.55 14688.0 United Kingdom\n", "95 536378 22352 LUNCH BOX WITH CUTLERY RETROSPOT 6 12/1/2010 9:37 2.55 14688.0 United Kingdom\n", "96 536378 21212 PACK OF 72 RETROSPOT CAKE CASES 120 12/1/2010 9:37 0.42 14688.0 United Kingdom\n", "97 536378 21975 PACK OF 60 DINOSAUR CAKE CASES 24 12/1/2010 9:37 0.55 14688.0 United Kingdom\n", "98 536378 21977 PACK OF 60 PINK PAISLEY CAKE CASES 24 12/1/2010 9:37 0.55 14688.0 United Kingdom\n", "99 536378 84991 60 TEATIME FAIRY CAKE CASES 24 12/1/2010 9:37 0.55 14688.0 United Kingdom\n", "100 536378 84519A TOMATO CHARLIE+LOLA COASTER SET 6 12/1/2010 9:37 2.95 14688.0 United Kingdom\n", "101 536378 85183B CHARLIE & LOLA WASTEPAPER BIN FLORA 48 12/1/2010 9:37 1.25 14688.0 United Kingdom\n", "102 536378 85071B RED CHARLIE+LOLA PERSONAL DOORSIGN 96 12/1/2010 9:37 0.38 14688.0 United Kingdom\n", "103 536378 21931 JUMBO STORAGE BAG SUKI 10 12/1/2010 9:37 1.95 14688.0 United Kingdom\n", "104 536378 21929 JUMBO BAG PINK VINTAGE PAISLEY 10 12/1/2010 9:37 1.95 14688.0 United Kingdom\n", "105 536380 22961 JAM MAKING SET PRINTED 24 12/1/2010 9:41 1.45 17809.0 United Kingdom\n", "106 536381 22139 RETROSPOT TEA SET CERAMIC 11 PC 23 12/1/2010 9:41 4.25 15311.0 United Kingdom\n", "107 536381 84854 GIRLY PINK TOOL SET 5 12/1/2010 9:41 4.95 15311.0 United Kingdom\n", "108 536381 22411 JUMBO SHOPPER VINTAGE RED PAISLEY 10 12/1/2010 9:41 1.95 15311.0 United Kingdom\n", "109 536381 82567 AIRLINE LOUNGE,METAL SIGN 2 12/1/2010 9:41 2.10 15311.0 United Kingdom\n", "110 536381 21672 WHITE SPOT RED CERAMIC DRAWER KNOB 6 12/1/2010 9:41 1.25 15311.0 United Kingdom\n", "111 536381 22774 RED DRAWER KNOB ACRYLIC EDWARDIAN 24 12/1/2010 9:41 1.25 15311.0 United Kingdom\n", "112 536381 22771 CLEAR DRAWER KNOB ACRYLIC EDWARDIAN 24 12/1/2010 9:41 1.25 15311.0 United Kingdom\n", "113 536381 71270 PHOTO CLIP LINE 1 12/1/2010 9:41 1.25 15311.0 United Kingdom\n", "114 536381 22262 FELT EGG COSY CHICKEN 1 12/1/2010 9:41 0.85 15311.0 United Kingdom\n", "115 536381 22637 PIGGY BANK RETROSPOT 1 12/1/2010 9:41 2.55 15311.0 United Kingdom\n", "116 536381 21934 SKULL SHOULDER BAG 10 12/1/2010 9:41 1.65 15311.0 United Kingdom\n", "117 536381 21169 YOU'RE CONFUSING ME METAL SIGN 3 12/1/2010 9:41 1.69 15311.0 United Kingdom\n", "118 536381 21166 COOK WITH WINE METAL SIGN 1 12/1/2010 9:41 1.95 15311.0 United Kingdom\n", "119 536381 21175 GIN + TONIC DIET METAL SIGN 2 12/1/2010 9:41 2.10 15311.0 United Kingdom\n", "120 536381 37444A YELLOW BREAKFAST CUP AND SAUCER 1 12/1/2010 9:41 2.95 15311.0 United Kingdom\n", "121 536381 37444C PINK BREAKFAST CUP AND SAUCER 1 12/1/2010 9:41 2.95 15311.0 United Kingdom\n", "122 536381 22086 PAPER CHAIN KIT 50'S CHRISTMAS 4 12/1/2010 9:41 2.95 15311.0 United Kingdom\n", "123 536381 22083 PAPER CHAIN KIT RETROSPOT 1 12/1/2010 9:41 2.95 15311.0 United Kingdom\n", "124 536381 84971S SMALL HEART FLOWERS HOOK 6 12/1/2010 9:41 0.85 15311.0 United Kingdom\n", "125 536381 71270 PHOTO CLIP LINE 3 12/1/2010 9:41 1.25 15311.0 United Kingdom\n", "126 536381 47580 TEA TIME DES TEA COSY 2 12/1/2010 9:41 2.55 15311.0 United Kingdom\n", "127 536381 22261 FELT EGG COSY WHITE RABBIT 1 12/1/2010 9:41 0.85 15311.0 United Kingdom\n", "128 536381 84832 ZINC WILLIE WINKIE CANDLE STICK 1 12/1/2010 9:41 0.85 15311.0 United Kingdom\n", "129 536381 22644 CERAMIC CHERRY CAKE MONEY BANK 1 12/1/2010 9:41 1.45 15311.0 United Kingdom\n", "130 536381 21533 RETROSPOT LARGE MILK JUG 1 12/1/2010 9:41 4.95 15311.0 United Kingdom\n", "131 536381 21557 SET OF 6 FUNKY BEAKERS 2 12/1/2010 9:41 2.95 15311.0 United Kingdom\n", "132 536381 15056BL EDWARDIAN PARASOL BLACK 2 12/1/2010 9:41 5.95 15311.0 United Kingdom\n", "133 536381 15056N EDWARDIAN PARASOL NATURAL 2 12/1/2010 9:41 5.95 15311.0 United Kingdom\n", "134 536381 22646 CERAMIC STRAWBERRY CAKE MONEY BANK 4 12/1/2010 9:41 1.45 15311.0 United Kingdom\n", "135 536381 22176 BLUE OWL SOFT TOY 1 12/1/2010 9:41 2.95 15311.0 United Kingdom\n", "136 536381 22438 BALLOON ART MAKE YOUR OWN FLOWERS 1 12/1/2010 9:41 1.95 15311.0 United Kingdom\n", "137 536381 21731 RED TOADSTOOL LED NIGHT LIGHT 2 12/1/2010 9:41 1.65 15311.0 United Kingdom\n", "138 536381 22778 GLASS CLOCHE SMALL 3 12/1/2010 9:41 3.95 15311.0 United Kingdom\n", "139 536381 22719 GUMBALL MONOCHROME COAT RACK 36 12/1/2010 9:41 1.06 15311.0 United Kingdom\n", "140 536381 21523 DOORMAT FANCY FONT HOME SWEET HOME 10 12/1/2010 9:41 6.75 15311.0 United Kingdom\n", "141 C536379 D Discount -1 12/1/2010 9:41 27.50 14527.0 United Kingdom\n", "142 536382 10002 INFLATABLE POLITICAL GLOBE 12 12/1/2010 9:45 0.85 16098.0 United Kingdom\n", "143 536382 21912 VINTAGE SNAKES & LADDERS 8 12/1/2010 9:45 3.75 16098.0 United Kingdom\n", "144 536382 21832 CHOCOLATE CALCULATOR 12 12/1/2010 9:45 1.65 16098.0 United Kingdom\n", "145 536382 22411 JUMBO SHOPPER VINTAGE RED PAISLEY 10 12/1/2010 9:45 1.95 16098.0 United Kingdom\n", "146 536382 22379 RECYCLING BAG RETROSPOT 10 12/1/2010 9:45 2.10 16098.0 United Kingdom\n", "147 536382 22381 TOY TIDY PINK POLKADOT 50 12/1/2010 9:45 1.85 16098.0 United Kingdom\n", "148 536382 22798 ANTIQUE GLASS DRESSING TABLE POT 8 12/1/2010 9:45 2.95 16098.0 United Kingdom\n", "149 536382 22726 ALARM CLOCK BAKELIKE GREEN 4 12/1/2010 9:45 3.75 16098.0 United Kingdom\n", "150 536382 22926 IVORY GIANT GARDEN THERMOMETER 12 12/1/2010 9:45 5.95 16098.0 United Kingdom\n", "151 536382 22839 3 TIER CAKE TIN GREEN AND CREAM 2 12/1/2010 9:45 14.95 16098.0 United Kingdom\n", "152 536382 22838 3 TIER CAKE TIN RED AND CREAM 2 12/1/2010 9:45 14.95 16098.0 United Kingdom\n", "153 536382 22783 SET 3 WICKER OVAL BASKETS W LIDS 4 12/1/2010 9:45 16.95 16098.0 United Kingdom\n", "154 C536383 35004C SET OF 3 COLOURED FLYING DUCKS -1 12/1/2010 9:49 4.65 15311.0 United Kingdom" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = digitos_boleta % 100 + 100\n", "df_visualizacion = df.head(N).copy()\n", "print(f\"N = {digitos_boleta} % 100 + 100 = {N}\")\n", "print(\"df_visualizacion se limita a la presentación; la limpieza continúa sobre df completo.\")\n", "display(df_visualizacion)" ] }, { "cell_type": "markdown", "id": "1a8dc4bf", "metadata": {}, "source": [ "## Punto 3. Limpieza de valores nulos" ] }, { "cell_type": "code", "execution_count": 4, "id": "b1a277db", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:16.221949Z", "iopub.status.busy": "2026-09-06T23:48:16.221845Z", "iopub.status.idle": "2026-09-06T23:48:16.486193Z", "shell.execute_reply": "2026-09-06T23:48:16.485722Z" } }, "outputs": [ { "data": { "text/html": [ "
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NulosPorcentaje
InvoiceNo00.000000
StockCode00.000000
Description14540.268311
Quantity00.000000
InvoiceDate00.000000
UnitPrice00.000000
CustomerID13508024.926694
Country00.000000
\n", "
" ], "text/plain": [ " Nulos Porcentaje\n", "InvoiceNo 0 0.000000\n", "StockCode 0 0.000000\n", "Description 1454 0.268311\n", "Quantity 0 0.000000\n", "InvoiceDate 0 0.000000\n", "UnitPrice 0 0.000000\n", "CustomerID 135080 24.926694\n", "Country 0 0.000000" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CustomerID imputados con 10155: 135,080\n", "Description imputadas con NO_DESCRIPCION_0155: 1,454\n", "Filas eliminadas por InvoiceNo nulo: 0\n", "Filas restantes: 541,909\n" ] } ], "source": [ "nulos_conteo_inicial = df.isna().sum()\n", "nulos_porcentaje_inicial = (nulos_conteo_inicial / len(df) * 100).round(6)\n", "reporte_nulos = pd.DataFrame({\"Nulos\": nulos_conteo_inicial, \"Porcentaje\": nulos_porcentaje_inicial})\n", "display(reporte_nulos)\n", "\n", "nulos_customer = int(df[\"CustomerID\"].isna().sum())\n", "nulos_description = int(df[\"Description\"].isna().sum())\n", "nulos_invoice = int(df[\"InvoiceNo\"].isna().sum())\n", "\n", "# 155 es impar: la estrategia asignada es imputar CustomerID con 10000 + 155 = 10155.\n", "customer_id_imputado = 10000 + digitos_boleta\n", "df[\"CustomerID\"] = df[\"CustomerID\"].fillna(customer_id_imputado).astype(\"Int64\")\n", "df[\"Description\"] = df[\"Description\"].fillna(f\"NO_DESCRIPCION_{identificador}\")\n", "df = df.dropna(subset=[\"InvoiceNo\"]).copy()\n", "\n", "print(f\"CustomerID imputados con {customer_id_imputado}: {nulos_customer:,}\")\n", "print(f\"Description imputadas con NO_DESCRIPCION_{identificador}: {nulos_description:,}\")\n", "print(f\"Filas eliminadas por InvoiceNo nulo: {nulos_invoice:,}\")\n", "print(f\"Filas restantes: {len(df):,}\")" ] }, { "cell_type": "markdown", "id": "3a4b73c7", "metadata": {}, "source": [ "## Punto 4. Duplicados y redundancia" ] }, { "cell_type": "code", "execution_count": 5, "id": "6728cae7", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:16.487647Z", "iopub.status.busy": "2026-09-06T23:48:16.487494Z", "iopub.status.idle": "2026-09-06T23:48:16.819017Z", "shell.execute_reply": "2026-09-06T23:48:16.818445Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Duplicados exactos detectados: 5,268\n", "Antes: 541,909 | Después: 531,225\n", "Eliminados por clave ['InvoiceNo', 'StockCode', 'CustomerID']: 10,684 (1.971549%)\n" ] } ], "source": [ "# La columna de auditoría se agrega sin alterar la definición de duplicado exacto.\n", "df[\"Fila_Original\"] = df.index.astype(\"int64\") + 1 # posición ordinal del registro en el CSV\n", "duplicados_exactos = int(df.duplicated(subset=columnas_fuente, keep=\"first\").sum())\n", "filas_antes_duplicados = len(df)\n", "clave_negocio = [\"InvoiceNo\", \"StockCode\", \"CustomerID\"]\n", "\n", "# 155 es impar: se conserva la primera fila por la clave crítica indicada.\n", "df = df.drop_duplicates(subset=clave_negocio, keep=\"first\").copy()\n", "filas_despues_duplicados = len(df)\n", "eliminadas_redundancia = filas_antes_duplicados - filas_despues_duplicados\n", "porcentaje_redundancia = eliminadas_redundancia / filas_antes_duplicados * 100\n", "\n", "print(f\"Duplicados exactos detectados: {duplicados_exactos:,}\")\n", "print(f\"Antes: {filas_antes_duplicados:,} | Después: {filas_despues_duplicados:,}\")\n", "print(f\"Eliminados por clave {clave_negocio}: {eliminadas_redundancia:,} ({porcentaje_redundancia:.6f}%)\")" ] }, { "cell_type": "markdown", "id": "44f80a6b", "metadata": {}, "source": [ "## Punto 5. Validación y corrección de tipos" ] }, { "cell_type": "code", "execution_count": 6, "id": "5cc10ec1", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:16.820140Z", "iopub.status.busy": "2026-09-06T23:48:16.820034Z", "iopub.status.idle": "2026-09-06T23:48:17.013467Z", "shell.execute_reply": "2026-09-06T23:48:17.012956Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "InvoiceDate datetime64[ns]\n", "Quantity int64\n", "UnitPrice float64\n", "Transaccion_Valida bool\n", "Es_Cancelacion boolean\n", "dtype: object\n", "Fechas no convertibles: 0\n", "Transacciones válidas (criterio original): 519,582\n", "Registros de cancelación: 9,139\n" ] } ], "source": [ "df[\"InvoiceDate\"] = pd.to_datetime(df[\"InvoiceDate\"], format=\"%m/%d/%Y %H:%M\", errors=\"coerce\")\n", "df[\"Quantity\"] = pd.to_numeric(df[\"Quantity\"], errors=\"coerce\")\n", "df[\"UnitPrice\"] = pd.to_numeric(df[\"UnitPrice\"], errors=\"coerce\")\n", "\n", "# Se preservan los valores de origen antes del capping para auditar devoluciones y ventas.\n", "df[\"Quantity_Original\"] = df[\"Quantity\"]\n", "df[\"UnitPrice_Original\"] = df[\"UnitPrice\"]\n", "df[\"Total_Transaccion_Original\"] = df[\"Quantity_Original\"] * df[\"UnitPrice_Original\"]\n", "df[\"Transaccion_Valida\"] = (df[\"Quantity_Original\"] > 0) & (df[\"UnitPrice_Original\"] > 0)\n", "df[\"Es_Cancelacion\"] = df[\"InvoiceNo\"].str.upper().str.startswith(\"C\", na=False)\n", "\n", "print(df[[\"InvoiceDate\", \"Quantity\", \"UnitPrice\", \"Transaccion_Valida\", \"Es_Cancelacion\"]].dtypes)\n", "print(f\"Fechas no convertibles: {df['InvoiceDate'].isna().sum():,}\")\n", "print(f\"Transacciones válidas (criterio original): {df['Transaccion_Valida'].sum():,}\")\n", "print(f\"Registros de cancelación: {df['Es_Cancelacion'].sum():,}\")" ] }, { "cell_type": "markdown", "id": "fd991712", "metadata": {}, "source": [ "## Punto 6. Detección y manejo de outliers\n", "\n", "El IQR se usa para **detectar** valores atípicos. Como el último dígito es 5, la corrección asignada consiste en limitar `Quantity` a los percentiles 5 y 95. Esta transformación es útil para comparar distribuciones, pero altera el significado de cantidades negativas. Por eso se conservan `Quantity_Original`, `Total_Transaccion_Original`, `Transaccion_Valida` y `Es_Cancelacion`." ] }, { "cell_type": "code", "execution_count": 7, "id": "1b4c0c80", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:17.014645Z", "iopub.status.busy": "2026-09-06T23:48:17.014531Z", "iopub.status.idle": "2026-09-06T23:48:17.048277Z", "shell.execute_reply": "2026-09-06T23:48:17.047782Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q1=1, Q3=10, IQR=9; límites IQR=[-12.5, 23.5]\n", "Outliers detectados por IQR: 58,337\n", "Capping p5=1, p95=30; filas modificadas: 36,222\n", "Cancelaciones afectadas por capping: 9,139\n", "Cantidades negativas convertidas a positivas: 10,475 de 10,475\n" ] } ], "source": [ "Q1 = df[\"Quantity_Original\"].quantile(0.25)\n", "Q3 = df[\"Quantity_Original\"].quantile(0.75)\n", "IQR = Q3 - Q1\n", "limite_inferior_iqr = Q1 - 1.5 * IQR\n", "limite_superior_iqr = Q3 + 1.5 * IQR\n", "mascara_outlier_iqr = ~df[\"Quantity_Original\"].between(limite_inferior_iqr, limite_superior_iqr)\n", "outliers_iqr = int(mascara_outlier_iqr.sum())\n", "\n", "p5 = df[\"Quantity_Original\"].quantile(0.05)\n", "p95 = df[\"Quantity_Original\"].quantile(0.95)\n", "df[\"Quantity\"] = df[\"Quantity_Original\"].clip(lower=p5, upper=p95)\n", "df[\"Cantidad_Capeada\"] = df[\"Quantity\"].ne(df[\"Quantity_Original\"])\n", "df[\"Total_Transaccion\"] = df[\"Quantity\"] * df[\"UnitPrice\"]\n", "df[\"Transaccion_Valida_Ajustada\"] = (df[\"Quantity\"] > 0) & (df[\"UnitPrice\"] > 0)\n", "\n", "filas_capeadas = int(df[\"Cantidad_Capeada\"].sum())\n", "cancelaciones_capeadas = int((df[\"Cantidad_Capeada\"] & df[\"Es_Cancelacion\"]).sum())\n", "negativas_originales = int((df[\"Quantity_Original\"] < 0).sum())\n", "negativas_convertidas_positivas = int(((df[\"Quantity_Original\"] < 0) & (df[\"Quantity\"] > 0)).sum())\n", "\n", "print(f\"Q1={Q1:g}, Q3={Q3:g}, IQR={IQR:g}; límites IQR=[{limite_inferior_iqr:g}, {limite_superior_iqr:g}]\")\n", "print(f\"Outliers detectados por IQR: {outliers_iqr:,}\")\n", "print(f\"Capping p5={p5:g}, p95={p95:g}; filas modificadas: {filas_capeadas:,}\")\n", "print(f\"Cancelaciones afectadas por capping: {cancelaciones_capeadas:,}\")\n", "print(f\"Cantidades negativas convertidas a positivas: {negativas_convertidas_positivas:,} de {negativas_originales:,}\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "ef995063", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:17.049623Z", "iopub.status.busy": "2026-09-06T23:48:17.049505Z", "iopub.status.idle": "2026-09-06T23:48:18.466271Z", "shell.execute_reply": "2026-09-06T23:48:18.465761Z" } }, "outputs": [ { "data": { "image/png": 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22268055644422502PICNIC BASKET WICKER 60 PIECES30649.5019485.00False
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43703C540118AMAZONFEEAMAZON FEE116453.7116453.71True
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" ], "text/plain": [ " InvoiceNo StockCode Description Quantity UnitPrice Total_Transaccion Es_Cancelacion\n", "222681 C556445 M Manual 1 38970.00 38970.00 True\n", "222680 556444 22502 PICNIC BASKET WICKER 60 PIECES 30 649.50 19485.00 False\n", "524602 C580605 AMAZONFEE AMAZON FEE 1 17836.46 17836.46 True\n", "43702 C540117 AMAZONFEE AMAZON FEE 1 16888.02 16888.02 True\n", "43703 C540118 AMAZONFEE AMAZON FEE 1 16453.71 16453.71 True" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(11, 4.2))\n", "sns.boxplot(x=df[\"Quantity_Original\"], ax=axes[0], color=\"#2C8BB3\", showfliers=True)\n", "axes[0].set_title(\"Antes: Quantity original\")\n", "axes[0].set_xlabel(\"Cantidad\")\n", "sns.boxplot(x=df[\"Quantity\"], ax=axes[1], color=\"#762042\", showfliers=True)\n", "axes[1].set_title(f\"Después: capping p5={p5:g}, p95={p95:g}\")\n", "axes[1].set_xlabel(\"Cantidad ajustada\")\n", "fig.suptitle(\"Distribución de cantidades antes y después del capping\")\n", "fig.tight_layout()\n", "boxplot_path = ASSETS_DIR / f\"boxplot_cantidad_{identificador}.png\"\n", "fig.savefig(boxplot_path, dpi=180, bbox_inches=\"tight\")\n", "plt.show()\n", "\n", "columnas_top = [\"InvoiceNo\", \"StockCode\", \"Description\", \"Quantity_Original\", \"UnitPrice_Original\", \"Total_Transaccion_Original\", \"Es_Cancelacion\"]\n", "top5_original = df.nlargest(5, \"Total_Transaccion_Original\")[columnas_top]\n", "print(\"Cinco registros con mayor total original (sin capping):\")\n", "display(top5_original)\n", "\n", "columnas_top_ajustado = [\"InvoiceNo\", \"StockCode\", \"Description\", \"Quantity\", \"UnitPrice\", \"Total_Transaccion\", \"Es_Cancelacion\"]\n", "top5_ajustado = df.nlargest(5, \"Total_Transaccion\")[columnas_top_ajustado]\n", "print(\"Cinco registros con mayor total ajustado (dataframe transformado):\")\n", "display(top5_ajustado)" ] }, { "cell_type": "markdown", "id": "b1cc3ff5", "metadata": {}, "source": [ "## Punto 7. Normalización y estandarización" ] }, { "cell_type": "code", "execution_count": 9, "id": "977816bd", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:18.467531Z", "iopub.status.busy": "2026-09-06T23:48:18.467417Z", "iopub.status.idle": "2026-09-06T23:48:19.205219Z", "shell.execute_reply": "2026-09-06T23:48:19.204747Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Productos con múltiples descripciones antes de unificar: 1,324\n", "StockCode que contiene 0155: 1 productos, 7 filas\n" ] }, { "data": { "text/html": [ "
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Filas
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" ], "text/plain": [ " Filas\n", "Categoría \n", "PRODUCTO_NORMAL 477266\n", "PRODUCTO_ESPECIAL 53925\n", "REGALO_0155 34" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"Country\"] = (\n", " df[\"Country\"].astype(\"string\").str.replace(r\"\\s+\", \" \", regex=True).str.strip().str.upper()\n", ")\n", "\n", "descripciones_por_producto = df.groupby(\"StockCode\")[\"Description\"].nunique(dropna=True)\n", "productos_descripcion_multiple = int((descripciones_por_producto > 1).sum())\n", "\n", "mascara_identificador_stock = df[\"StockCode\"].str.contains(identificador, regex=False, na=False)\n", "filas_stock_identificador = int(mascara_identificador_stock.sum())\n", "productos_stock_identificador = int(df.loc[mascara_identificador_stock, \"StockCode\"].nunique())\n", "\n", "if mascara_identificador_stock.any():\n", " moda_por_stock = (\n", " df.loc[mascara_identificador_stock]\n", " .groupby(\"StockCode\")[\"Description\"]\n", " .agg(lambda serie: serie.mode().iloc[0] if not serie.mode().empty else serie.iloc[0])\n", " )\n", " df.loc[mascara_identificador_stock, \"Description\"] = (\n", " df.loc[mascara_identificador_stock, \"StockCode\"].map(moda_por_stock)\n", " + f\" [UNIFICADO_{identificador}]\"\n", " )\n", "\n", "stock_minuscula = df[\"StockCode\"].str.lower()\n", "contiene_gift = stock_minuscula.str.contains(\"gift\", regex=False, na=False)\n", "contiene_letras = df[\"StockCode\"].str.contains(r\"[A-Za-z]\", regex=True, na=False)\n", "df[\"Categoria_Producto\"] = np.select(\n", " [contiene_gift, contiene_letras],\n", " [f\"REGALO_{identificador}\", \"PRODUCTO_ESPECIAL\"],\n", " default=\"PRODUCTO_NORMAL\",\n", ")\n", "\n", "print(f\"Productos con múltiples descripciones antes de unificar: {productos_descripcion_multiple:,}\")\n", "print(f\"StockCode que contiene {identificador}: {productos_stock_identificador:,} productos, {filas_stock_identificador:,} filas\")\n", "display(df[\"Categoria_Producto\"].value_counts().rename_axis(\"Categoría\").to_frame(\"Filas\"))" ] }, { "cell_type": "markdown", "id": "b841f739", "metadata": {}, "source": [ "## Punto 8. Segmentación de clientes y gasto" ] }, { "cell_type": "code", "execution_count": 10, "id": "9dd09f16", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:19.206567Z", "iopub.status.busy": "2026-09-06T23:48:19.206458Z", "iopub.status.idle": "2026-09-06T23:48:19.479549Z", "shell.execute_reply": "2026-09-06T23:48:19.479092Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Umbral = 2023630155 % 50 + 10 = 15\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Transacciones
Categoria_Cliente
BAJO_VALOR341842
ALTO_VALOR189383
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" ], "text/plain": [ " Transacciones\n", "Categoria_Cliente \n", "BAJO_VALOR 341842\n", "ALTO_VALOR 189383" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[\"Total_Venta_Original\"] = df[\"Total_Transaccion_Original\"]\n", "df[\"Total_Venta\"] = df[\"Total_Transaccion\"] # total ajustado por la estrategia de capping asignada\n", "umbral = int(boleta_completa) % 50 + 10\n", "df[\"Categoria_Cliente\"] = np.where(df[\"Total_Venta\"] < umbral, \"BAJO_VALOR\", \"ALTO_VALOR\")\n", "conteo_categorias_cliente = df[\"Categoria_Cliente\"].value_counts().reindex([\"BAJO_VALOR\", \"ALTO_VALOR\"])\n", "\n", "ax = conteo_categorias_cliente.plot(kind=\"bar\", figsize=(7.5, 4.5), color=[\"#2C8BB3\", \"#762042\"])\n", "ax.set_title(f\"Transacciones por categoría de cliente (umbral ajustado = {umbral})\")\n", "ax.set_xlabel(\"Categoría\")\n", "ax.set_ylabel(\"Número de registros\")\n", "ax.tick_params(axis=\"x\", rotation=0)\n", "for contenedor in ax.containers:\n", " ax.bar_label(contenedor, fmt=\"{:,.0f}\")\n", "plt.tight_layout()\n", "categorias_path = ASSETS_DIR / f\"categorias_cliente_{identificador}.png\"\n", "plt.savefig(categorias_path, dpi=180, bbox_inches=\"tight\")\n", "plt.show()\n", "\n", "print(f\"Umbral = {int(boleta_completa)} % 50 + 10 = {umbral}\")\n", "display(conteo_categorias_cliente.to_frame(\"Transacciones\"))" ] }, { "cell_type": "markdown", "id": "fb3b2fc6", "metadata": {}, "source": [ "## Punto 9. Exportación y calidad\n", "\n", "El CSV conserva las columnas originales y columnas de auditoría. `Total_Venta` refleja la cantidad capeada porque la consigna ordena continuar con el dataframe transformado. `Total_Venta_Original` conserva el importe observado en el archivo. Las cancelaciones siguen identificadas por `Es_Cancelacion`; no se reinterpretan como ventas reales aunque el p5 convierta su cantidad ajustada en positiva." ] }, { "cell_type": "code", "execution_count": 11, "id": "b7469841", "metadata": { "execution": { "iopub.execute_input": "2026-09-06T23:48:19.480760Z", "iopub.status.busy": "2026-09-06T23:48:19.480654Z", "iopub.status.idle": "2026-09-06T23:48:23.958670Z", "shell.execute_reply": "2026-09-06T23:48:23.958141Z" } }, "outputs": [ { "data": { "text/html": [ "
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Valor
Filas_originales541909
Filas_finales531225
Transacciones_validas519582
Transacciones_validas_ajustadas528721
Cancelaciones9139
Clientes_unicos_incluye_imputado4373
Clientes_reales_sin_imputado4372
Productos_unicos4070
Total_ventas_positivas_originales10587015.733
Total_ventas_positivas_ajustadas8446757.503
Total_neto_original9683331.923
Total_neto_ajustado8424633.383
Filas_con_identificador_0155134566
Completitud_porcentaje100.0
Consistencia_clave_porcentaje100.0
Consistencia_cancelacion_porcentaje100.0
Consistencia_rango_capeado_porcentaje100.0
Consistencia_paises_porcentaje100.0
Duplicados_exactos_detectados5268
Filas_eliminadas_por_clave10684
Outliers_IQR58337
Filas_capeadas36222
Cancelaciones_capeadas9139
p5_Quantity1.0
p95_Quantity30.0
Umbral_cliente15
SHA256_dataset_originala2f79bbdd4463df6db8a3f5a50b9c980ae8f645a370bf5...
\n", "
" ], "text/plain": [ " Valor\n", "Filas_originales 541909\n", "Filas_finales 531225\n", "Transacciones_validas 519582\n", "Transacciones_validas_ajustadas 528721\n", "Cancelaciones 9139\n", "Clientes_unicos_incluye_imputado 4373\n", "Clientes_reales_sin_imputado 4372\n", "Productos_unicos 4070\n", "Total_ventas_positivas_originales 10587015.733\n", "Total_ventas_positivas_ajustadas 8446757.503\n", "Total_neto_original 9683331.923\n", "Total_neto_ajustado 8424633.383\n", "Filas_con_identificador_0155 134566\n", "Completitud_porcentaje 100.0\n", "Consistencia_clave_porcentaje 100.0\n", "Consistencia_cancelacion_porcentaje 100.0\n", "Consistencia_rango_capeado_porcentaje 100.0\n", "Consistencia_paises_porcentaje 100.0\n", "Duplicados_exactos_detectados 5268\n", "Filas_eliminadas_por_clave 10684\n", "Outliers_IQR 58337\n", "Filas_capeadas 36222\n", "Cancelaciones_capeadas 9139\n", "p5_Quantity 1.0\n", "p95_Quantity 30.0\n", "Umbral_cliente 15\n", "SHA256_dataset_original a2f79bbdd4463df6db8a3f5a50b9c980ae8f645a370bf5..." ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CSV limpio: /home/eddndev/Documents/ESCOM/BD/01-limpieza-datos-retail/datos/processed/online_retail_limpio_Eduardo_Alonso_Sanchez_0155.csv (91.28 MiB)\n", "Reporte JSON: /home/eddndev/Documents/ESCOM/BD/01-limpieza-datos-retail/datos/processed/reporte_estadistico_0155.json\n", "Filas con el identificador escrito 0155: 134,566\n" ] } ], "source": [ "columnas_busqueda = [\"InvoiceNo\", \"StockCode\", \"Description\", \"CustomerID\", \"Country\"]\n", "contiene_identificador = pd.Series(False, index=df.index)\n", "for columna in columnas_busqueda:\n", " contiene_identificador |= df[columna].astype(\"string\").str.contains(identificador, regex=False, na=False)\n", "filas_con_identificador = int(contiene_identificador.sum())\n", "\n", "completitud = float((1 - df.isna().sum().sum() / df.size) * 100)\n", "consistencia_clave = float((1 - df.duplicated(subset=clave_negocio).mean()) * 100)\n", "consistencia_cancelacion = float((df[\"Es_Cancelacion\"] == df[\"InvoiceNo\"].str.upper().str.startswith(\"C\", na=False)).mean() * 100)\n", "consistencia_rango_capeado = float(df[\"Quantity\"].between(p5, p95).mean() * 100)\n", "consistencia_paises = float((df[\"Country\"] == df[\"Country\"].str.strip().str.upper()).mean() * 100)\n", "\n", "ventas_positivas_originales = float(df.loc[df[\"Total_Transaccion_Original\"] > 0, \"Total_Transaccion_Original\"].sum())\n", "ventas_positivas_ajustadas = float(df.loc[df[\"Total_Transaccion\"] > 0, \"Total_Transaccion\"].sum())\n", "ventas_netas_originales = float(df[\"Total_Transaccion_Original\"].sum())\n", "ventas_netas_ajustadas = float(df[\"Total_Transaccion\"].sum())\n", "\n", "reporte = {\n", " \"Filas_originales\": filas_originales,\n", " \"Filas_finales\": len(df),\n", " \"Transacciones_validas\": int(df[\"Transaccion_Valida\"].sum()),\n", " \"Transacciones_validas_ajustadas\": int(df[\"Transaccion_Valida_Ajustada\"].sum()),\n", " \"Cancelaciones\": int(df[\"Es_Cancelacion\"].sum()),\n", " \"Clientes_unicos_incluye_imputado\": int(df[\"CustomerID\"].nunique()),\n", " \"Clientes_reales_sin_imputado\": int(df.loc[df[\"CustomerID\"] != customer_id_imputado, \"CustomerID\"].nunique()),\n", " \"Productos_unicos\": int(df[\"StockCode\"].nunique()),\n", " \"Total_ventas_positivas_originales\": ventas_positivas_originales,\n", " \"Total_ventas_positivas_ajustadas\": ventas_positivas_ajustadas,\n", " \"Total_neto_original\": ventas_netas_originales,\n", " \"Total_neto_ajustado\": ventas_netas_ajustadas,\n", " \"Filas_con_identificador_0155\": filas_con_identificador,\n", " \"Completitud_porcentaje\": completitud,\n", " \"Consistencia_clave_porcentaje\": consistencia_clave,\n", " \"Consistencia_cancelacion_porcentaje\": consistencia_cancelacion,\n", " \"Consistencia_rango_capeado_porcentaje\": consistencia_rango_capeado,\n", " \"Consistencia_paises_porcentaje\": consistencia_paises,\n", " \"Duplicados_exactos_detectados\": duplicados_exactos,\n", " \"Filas_eliminadas_por_clave\": eliminadas_redundancia,\n", " \"Outliers_IQR\": outliers_iqr,\n", " \"Filas_capeadas\": filas_capeadas,\n", " \"Cancelaciones_capeadas\": cancelaciones_capeadas,\n", " \"p5_Quantity\": float(p5),\n", " \"p95_Quantity\": float(p95),\n", " \"Umbral_cliente\": umbral,\n", " \"SHA256_dataset_original\": sha256_raw,\n", "}\n", "\n", "csv_path = PROCESSED_DIR / f\"online_retail_limpio_Eduardo_Alonso_Sanchez_{identificador}.csv\"\n", "json_path = PROCESSED_DIR / f\"reporte_estadistico_{identificador}.json\"\n", "metricas_csv_path = PROCESSED_DIR / f\"reporte_estadistico_{identificador}.csv\"\n", "top5_path = PROCESSED_DIR / f\"top5_transacciones_originales_{identificador}.csv\"\n", "top5_ajustado_path = PROCESSED_DIR / f\"top5_transacciones_ajustadas_{identificador}.csv\"\n", "\n", "df.to_csv(csv_path, index=False, date_format=\"%Y-%m-%d %H:%M:%S\")\n", "json_path.write_text(json.dumps(reporte, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n", "pd.DataFrame([reporte]).to_csv(metricas_csv_path, index=False)\n", "top5_original.to_csv(top5_path, index=False)\n", "top5_ajustado.to_csv(top5_ajustado_path, index=False)\n", "\n", "display(pd.DataFrame.from_dict(reporte, orient=\"index\", columns=[\"Valor\"]))\n", "print(f\"CSV limpio: {csv_path} ({csv_path.stat().st_size / 1024**2:.2f} MiB)\")\n", "print(f\"Reporte JSON: {json_path}\")\n", "print(f\"Filas con el identificador escrito {identificador}: {filas_con_identificador:,}\")" ] }, { "cell_type": "markdown", "id": "91d7d450", "metadata": {}, "source": [ "## Conclusiones\n", "\n", "La limpieza deja una clave de negocio sin repeticiones y elimina los nulos mediante la estrategia asignada. La principal limitación es el capping en el percentil 5: como `p5 = 1`, las cantidades negativas se vuelven positivas. Esto puede servir para estabilizar una distribución destinada a ciertos modelos, pero sesga el ingreso y no debe presentarse como venta real. El archivo exportado conserva los valores originales y las banderas necesarias para separar ventas, devoluciones y cancelaciones durante la revisión." ] } ], "metadata": { "colab": { "name": "practica_01_limpieza_datos_0155.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.14" } }, "nbformat": 4, "nbformat_minor": 5 }