Abstract: This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.
Data Set Characteristics: Multivariate, Sequential, Time-Series
Number of Instances: 541909
Area: Business
Attribute Characteristics: Integer, Real
Number of Attributes:8
Date Donated: 2015-11-06
Associated Tasks: Classification, Clustering
Missing Values?: N/A
Number of Web Hits: 846307
Source:
Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.
Data Set Information:
This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.
Citation:
Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197–208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17).
Dataset Download Link https://archive.ics.uci.edu/ml/machine-learning-databases/00352/
import pandas as pd
import matplotlib.pyplot as plt# Read the dataset (excel file) into a DataFrame
#Since there is only sheet in excel file we can use below code to read the entire excel file to dataframe
df_original = pd.read_excel('Online Retail.xlsx')
df_original
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| InvoiceNo | StockCode | Description | Quantity | InvoiceDate | UnitPrice | CustomerID | Country | |
|---|---|---|---|---|---|---|---|---|
| 0 | 536365 | 85123A | WHITE HANGING HEART T-LIGHT HOLDER | 6 | 2010-12-01 08:26:00 | 2.55 | 17850.0 | United Kingdom |
| 1 | 536365 | 71053 | WHITE METAL LANTERN | 6 | 2010-12-01 08:26:00 | 3.39 | 17850.0 | United Kingdom |
| 2 | 536365 | 84406B | CREAM CUPID HEARTS COAT HANGER | 8 | 2010-12-01 08:26:00 | 2.75 | 17850.0 | United Kingdom |
| 3 | 536365 | 84029G | KNITTED UNION FLAG HOT WATER BOTTLE | 6 | 2010-12-01 08:26:00 | 3.39 | 17850.0 | United Kingdom |
| 4 | 536365 | 84029E | RED WOOLLY HOTTIE WHITE HEART. | 6 | 2010-12-01 08:26:00 | 3.39 | 17850.0 | United Kingdom |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 541904 | 581587 | 22613 | PACK OF 20 SPACEBOY NAPKINS | 12 | 2011-12-09 12:50:00 | 0.85 | 12680.0 | France |
| 541905 | 581587 | 22899 | CHILDREN'S APRON DOLLY GIRL | 6 | 2011-12-09 12:50:00 | 2.10 | 12680.0 | France |
| 541906 | 581587 | 23254 | CHILDRENS CUTLERY DOLLY GIRL | 4 | 2011-12-09 12:50:00 | 4.15 | 12680.0 | France |
| 541907 | 581587 | 23255 | CHILDRENS CUTLERY CIRCUS PARADE | 4 | 2011-12-09 12:50:00 | 4.15 | 12680.0 | France |
| 541908 | 581587 | 22138 | BAKING SET 9 PIECE RETROSPOT | 3 | 2011-12-09 12:50:00 | 4.95 | 12680.0 | France |
541909 rows × 8 columns
df =df_original.copy()# Display information about the DataFrame
print(df.info())<class 'pandas.core.frame.DataFrame'>
RangeIndex: 541909 entries, 0 to 541908
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 InvoiceNo 541909 non-null object
1 StockCode 541909 non-null object
2 Description 540455 non-null object
3 Quantity 541909 non-null int64
4 InvoiceDate 541909 non-null datetime64[ns]
5 UnitPrice 541909 non-null float64
6 CustomerID 406829 non-null float64
7 Country 541909 non-null object
dtypes: datetime64[ns](1), float64(2), int64(1), object(4)
memory usage: 33.1+ MB
None
print(df.describe()) Quantity InvoiceDate UnitPrice
count 541909.000000 541909 541909.000000 \
mean 9.552250 2011-07-04 13:34:57.156386048 4.611114
min -80995.000000 2010-12-01 08:26:00 -11062.060000
25% 1.000000 2011-03-28 11:34:00 1.250000
50% 3.000000 2011-07-19 17:17:00 2.080000
75% 10.000000 2011-10-19 11:27:00 4.130000
max 80995.000000 2011-12-09 12:50:00 38970.000000
std 218.081158 NaN 96.759853
CustomerID
count 406829.000000
mean 15287.690570
min 12346.000000
25% 13953.000000
50% 15152.000000
75% 16791.000000
max 18287.000000
std 1713.600303
According to dataframe info there are 8 Columns and 541909 rows
It looks like 'Description' and 'CustomerID' have missing values 540455 and 406829.
So we should check the missing values first.
df.isna().sum()InvoiceNo 0
StockCode 0
Description 1454
Quantity 0
InvoiceDate 0
UnitPrice 0
CustomerID 135080
Country 0
dtype: int64
Before we drop the missing values or fill, we have to make sure that the missing data is relatively small or not compared to the total dataset.
# Get the total number of rows in the DataFrame
total_rows = len(df)
# Get the number of missing customer IDs
missing_customer_ids = df['CustomerID'].isna().sum()
# Get the percentage of missing customer IDs
missing_customer_percentage = (missing_customer_ids / total_rows) * 100
# Print the percentage of missing customer IDs
print("Percentage of Missing Customer IDs:", missing_customer_percentage)Percentage of Missing Customer IDs: 24.926694334288598
Luckly, we didn't drop anything, beacuse the percentage of Missing Customer IDs are 24.93% so it is around a quarter of our customer IDs.
We decided not to drop anything, but we have to do something about the data so, we will fill those CustomerID with 0.
df['CustomerID'].fillna(0, inplace=True)After filling with 0 we should check that again, We should go further about Missing Customer ID later, but not now. we will just keep those rows with 0 in the data.
df.isna().sum()InvoiceNo 0
StockCode 0
Description 1454
Quantity 0
InvoiceDate 0
UnitPrice 0
CustomerID 0
Country 0
dtype: int64
Now we want to invesgate Missing Descriptions
# Get the number of Missing Descriptions
missing_descriptions = df['Description'].isna().sum()
# Calculate the percentage of Missing Descriptions
missing_descriptions_percentage = (missing_descriptions / total_rows) * 100
# Print the percentage of Missing Descriptions
print("Percentage of Missing Descriptions:", missing_descriptions_percentage)Percentage of Missing Descriptions: 0.2683107311375157
We can drop the missing description rows since it is only 0.27%. Description is crucial in this dataset since it stores what is the transanction is about or cancel or return or discount info. We can't delete the Description before we check anything. So we will filled those rows with some text.
# Fill missing Description values with "Missing_Description"
df['Description'] = df['Description'].fillna('Missing_Description')
# Select rows with 'Missing_Description' in the Description column
missing_descriptions = df[df['Description'] == 'Missing_Description']
missing_descriptions
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| InvoiceNo | StockCode | Description | Quantity | InvoiceDate | UnitPrice | CustomerID | Country | |
|---|---|---|---|---|---|---|---|---|
| 622 | 536414 | 22139 | Missing_Description | 56 | 2010-12-01 11:52:00 | 0.0 | 0.0 | United Kingdom |
| 1970 | 536545 | 21134 | Missing_Description | 1 | 2010-12-01 14:32:00 | 0.0 | 0.0 | United Kingdom |
| 1971 | 536546 | 22145 | Missing_Description | 1 | 2010-12-01 14:33:00 | 0.0 | 0.0 | United Kingdom |
| 1972 | 536547 | 37509 | Missing_Description | 1 | 2010-12-01 14:33:00 | 0.0 | 0.0 | United Kingdom |
| 1987 | 536549 | 85226A | Missing_Description | 1 | 2010-12-01 14:34:00 | 0.0 | 0.0 | United Kingdom |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 535322 | 581199 | 84581 | Missing_Description | -2 | 2011-12-07 18:26:00 | 0.0 | 0.0 | United Kingdom |
| 535326 | 581203 | 23406 | Missing_Description | 15 | 2011-12-07 18:31:00 | 0.0 | 0.0 | United Kingdom |
| 535332 | 581209 | 21620 | Missing_Description | 6 | 2011-12-07 18:35:00 | 0.0 | 0.0 | United Kingdom |
| 536981 | 581234 | 72817 | Missing_Description | 27 | 2011-12-08 10:33:00 | 0.0 | 0.0 | United Kingdom |
| 538554 | 581408 | 85175 | Missing_Description | 20 | 2011-12-08 14:06:00 | 0.0 | 0.0 | United Kingdom |
1454 rows × 8 columns
Now, all the missing data are filled. we are going to check again.
df.isna().sum()InvoiceNo 0
StockCode 0
Description 0
Quantity 0
InvoiceDate 0
UnitPrice 0
CustomerID 0
Country 0
dtype: int64
Now it is time to check the data types before we touch any of the data.
#We will check the datatype
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 541909 entries, 0 to 541908
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 InvoiceNo 541909 non-null object
1 StockCode 541909 non-null object
2 Description 541909 non-null object
3 Quantity 541909 non-null int64
4 InvoiceDate 541909 non-null datetime64[ns]
5 UnitPrice 541909 non-null float64
6 CustomerID 541909 non-null float64
7 Country 541909 non-null object
dtypes: datetime64[ns](1), float64(2), int64(1), object(4)
memory usage: 33.1+ MB
DataTypes looks ok, but We should see the data first.
df.head(10)
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| InvoiceNo | StockCode | Description | Quantity | InvoiceDate | UnitPrice | CustomerID | Country | |
|---|---|---|---|---|---|---|---|---|
| 0 | 536365 | 85123A | WHITE HANGING HEART T-LIGHT HOLDER | 6 | 2010-12-01 08:26:00 | 2.55 | 17850.0 | United Kingdom |
| 1 | 536365 | 71053 | WHITE METAL LANTERN | 6 | 2010-12-01 08:26:00 | 3.39 | 17850.0 | United Kingdom |
| 2 | 536365 | 84406B | CREAM CUPID HEARTS COAT HANGER | 8 | 2010-12-01 08:26:00 | 2.75 | 17850.0 | United Kingdom |
| 3 | 536365 | 84029G | KNITTED UNION FLAG HOT WATER BOTTLE | 6 | 2010-12-01 08:26:00 | 3.39 | 17850.0 | United Kingdom |
| 4 | 536365 | 84029E | RED WOOLLY HOTTIE WHITE HEART. | 6 | 2010-12-01 08:26:00 | 3.39 | 17850.0 | United Kingdom |
| 5 | 536365 | 22752 | SET 7 BABUSHKA NESTING BOXES | 2 | 2010-12-01 08:26:00 | 7.65 | 17850.0 | United Kingdom |
| 6 | 536365 | 21730 | GLASS STAR FROSTED T-LIGHT HOLDER | 6 | 2010-12-01 08:26:00 | 4.25 | 17850.0 | United Kingdom |
| 7 | 536366 | 22633 | HAND WARMER UNION JACK | 6 | 2010-12-01 08:28:00 | 1.85 | 17850.0 | United Kingdom |
| 8 | 536366 | 22632 | HAND WARMER RED POLKA DOT | 6 | 2010-12-01 08:28:00 | 1.85 | 17850.0 | United Kingdom |
| 9 | 536367 | 84879 | ASSORTED COLOUR BIRD ORNAMENT | 32 | 2010-12-01 08:34:00 | 1.69 | 13047.0 | United Kingdom |
df.tail(10)
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| InvoiceNo | StockCode | Description | Quantity | InvoiceDate | UnitPrice | CustomerID | Country | |
|---|---|---|---|---|---|---|---|---|
| 541899 | 581587 | 22726 | ALARM CLOCK BAKELIKE GREEN | 4 | 2011-12-09 12:50:00 | 3.75 | 12680.0 | France |
| 541900 | 581587 | 22730 | ALARM CLOCK BAKELIKE IVORY | 4 | 2011-12-09 12:50:00 | 3.75 | 12680.0 | France |
| 541901 | 581587 | 22367 | CHILDRENS APRON SPACEBOY DESIGN | 8 | 2011-12-09 12:50:00 | 1.95 | 12680.0 | France |
| 541902 | 581587 | 22629 | SPACEBOY LUNCH BOX | 12 | 2011-12-09 12:50:00 | 1.95 | 12680.0 | France |
| 541903 | 581587 | 23256 | CHILDRENS CUTLERY SPACEBOY | 4 | 2011-12-09 12:50:00 | 4.15 | 12680.0 | France |
| 541904 | 581587 | 22613 | PACK OF 20 SPACEBOY NAPKINS | 12 | 2011-12-09 12:50:00 | 0.85 | 12680.0 | France |
| 541905 | 581587 | 22899 | CHILDREN'S APRON DOLLY GIRL | 6 | 2011-12-09 12:50:00 | 2.10 | 12680.0 | France |
| 541906 | 581587 | 23254 | CHILDRENS CUTLERY DOLLY GIRL | 4 | 2011-12-09 12:50:00 | 4.15 | 12680.0 | France |
| 541907 | 581587 | 23255 | CHILDRENS CUTLERY CIRCUS PARADE | 4 | 2011-12-09 12:50:00 | 4.15 | 12680.0 | France |
| 541908 | 581587 | 22138 | BAKING SET 9 PIECE RETROSPOT | 3 | 2011-12-09 12:50:00 | 4.95 | 12680.0 | France |
The dataset has 541909 rows and 8 columns. Invoice Numbers are 536365 to 581587 and there are more than one items in one Invoice. Moreover, the StockCode is combining some string and numbers. So we want to see more rows rather than head and tail.
# Set the desired range of rows to display
pd.set_option('display.max_rows', 500)
# View the selected range of rows
print(df[:500]) InvoiceNo StockCode Description Quantity
0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \
1 536365 71053 WHITE METAL LANTERN 6
2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8
3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6
5 536365 22752 SET 7 BABUSHKA NESTING BOXES 2
6 536365 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
7 536366 22633 HAND WARMER UNION JACK 6
8 536366 22632 HAND WARMER RED POLKA DOT 6
9 536367 84879 ASSORTED COLOUR BIRD ORNAMENT 32
10 536367 22745 POPPY'S PLAYHOUSE BEDROOM 6
11 536367 22748 POPPY'S PLAYHOUSE KITCHEN 6
12 536367 22749 FELTCRAFT PRINCESS CHARLOTTE DOLL 8
13 536367 22310 IVORY KNITTED MUG COSY 6
14 536367 84969 BOX OF 6 ASSORTED COLOUR TEASPOONS 6
15 536367 22623 BOX OF VINTAGE JIGSAW BLOCKS 3
16 536367 22622 BOX OF VINTAGE ALPHABET BLOCKS 2
17 536367 21754 HOME BUILDING BLOCK WORD 3
18 536367 21755 LOVE BUILDING BLOCK WORD 3
19 536367 21777 RECIPE BOX WITH METAL HEART 4
20 536367 48187 DOORMAT NEW ENGLAND 4
21 536368 22960 JAM MAKING SET WITH JARS 6
22 536368 22913 RED COAT RACK PARIS FASHION 3
23 536368 22912 YELLOW COAT RACK PARIS FASHION 3
24 536368 22914 BLUE COAT RACK PARIS FASHION 3
25 536369 21756 BATH BUILDING BLOCK WORD 3
26 536370 22728 ALARM CLOCK BAKELIKE PINK 24
27 536370 22727 ALARM CLOCK BAKELIKE RED 24
28 536370 22726 ALARM CLOCK BAKELIKE GREEN 12
29 536370 21724 PANDA AND BUNNIES STICKER SHEET 12
30 536370 21883 STARS GIFT TAPE 24
31 536370 10002 INFLATABLE POLITICAL GLOBE 48
32 536370 21791 VINTAGE HEADS AND TAILS CARD GAME 24
33 536370 21035 SET/2 RED RETROSPOT TEA TOWELS 18
34 536370 22326 ROUND SNACK BOXES SET OF4 WOODLAND 24
35 536370 22629 SPACEBOY LUNCH BOX 24
36 536370 22659 LUNCH BOX I LOVE LONDON 24
37 536370 22631 CIRCUS PARADE LUNCH BOX 24
38 536370 22661 CHARLOTTE BAG DOLLY GIRL DESIGN 20
39 536370 21731 RED TOADSTOOL LED NIGHT LIGHT 24
40 536370 22900 SET 2 TEA TOWELS I LOVE LONDON 24
41 536370 21913 VINTAGE SEASIDE JIGSAW PUZZLES 12
42 536370 22540 MINI JIGSAW CIRCUS PARADE 24
43 536370 22544 MINI JIGSAW SPACEBOY 24
44 536370 22492 MINI PAINT SET VINTAGE 36
45 536370 POST POSTAGE 3
46 536371 22086 PAPER CHAIN KIT 50'S CHRISTMAS 80
47 536372 22632 HAND WARMER RED POLKA DOT 6
48 536372 22633 HAND WARMER UNION JACK 6
49 536373 85123A WHITE HANGING HEART T-LIGHT HOLDER 6
50 536373 71053 WHITE METAL LANTERN 6
51 536373 84406B CREAM CUPID HEARTS COAT HANGER 8
52 536373 20679 EDWARDIAN PARASOL RED 6
53 536373 37370 RETRO COFFEE MUGS ASSORTED 6
54 536373 21871 SAVE THE PLANET MUG 6
55 536373 21071 VINTAGE BILLBOARD DRINK ME MUG 6
56 536373 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6
57 536373 82483 WOOD 2 DRAWER CABINET WHITE FINISH 2
58 536373 82486 WOOD S/3 CABINET ANT WHITE FINISH 4
59 536373 82482 WOODEN PICTURE FRAME WHITE FINISH 6
60 536373 82494L WOODEN FRAME ANTIQUE WHITE 6
61 536373 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
62 536373 84029E RED WOOLLY HOTTIE WHITE HEART. 6
63 536373 22752 SET 7 BABUSHKA NESTING BOXES 2
64 536373 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
65 536374 21258 VICTORIAN SEWING BOX LARGE 32
66 536375 85123A WHITE HANGING HEART T-LIGHT HOLDER 6
67 536375 71053 WHITE METAL LANTERN 6
68 536375 84406B CREAM CUPID HEARTS COAT HANGER 8
69 536375 20679 EDWARDIAN PARASOL RED 6
70 536375 37370 RETRO COFFEE MUGS ASSORTED 6
71 536375 21871 SAVE THE PLANET MUG 6
72 536375 21071 VINTAGE BILLBOARD DRINK ME MUG 6
73 536375 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6
74 536375 82483 WOOD 2 DRAWER CABINET WHITE FINISH 2
75 536375 82486 WOOD S/3 CABINET ANT WHITE FINISH 4
76 536375 82482 WOODEN PICTURE FRAME WHITE FINISH 6
77 536375 82494L WOODEN FRAME ANTIQUE WHITE 6
78 536375 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
79 536375 84029E RED WOOLLY HOTTIE WHITE HEART. 6
80 536375 22752 SET 7 BABUSHKA NESTING BOXES 2
81 536375 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
82 536376 22114 HOT WATER BOTTLE TEA AND SYMPATHY 48
83 536376 21733 RED HANGING HEART T-LIGHT HOLDER 64
84 536377 22632 HAND WARMER RED POLKA DOT 6
85 536377 22633 HAND WARMER UNION JACK 6
86 536378 22386 JUMBO BAG PINK POLKADOT 10
87 536378 85099C JUMBO BAG BAROQUE BLACK WHITE 10
88 536378 21033 JUMBO BAG CHARLIE AND LOLA TOYS 10
89 536378 20723 STRAWBERRY CHARLOTTE BAG 10
90 536378 84997B RED 3 PIECE RETROSPOT CUTLERY SET 12
91 536378 84997C BLUE 3 PIECE POLKADOT CUTLERY SET 6
92 536378 21094 SET/6 RED SPOTTY PAPER PLATES 12
93 536378 20725 LUNCH BAG RED RETROSPOT 10
94 536378 21559 STRAWBERRY LUNCH BOX WITH CUTLERY 6
95 536378 22352 LUNCH BOX WITH CUTLERY RETROSPOT 6
96 536378 21212 PACK OF 72 RETROSPOT CAKE CASES 120
97 536378 21975 PACK OF 60 DINOSAUR CAKE CASES 24
98 536378 21977 PACK OF 60 PINK PAISLEY CAKE CASES 24
99 536378 84991 60 TEATIME FAIRY CAKE CASES 24
100 536378 84519A TOMATO CHARLIE+LOLA COASTER SET 6
101 536378 85183B CHARLIE & LOLA WASTEPAPER BIN FLORA 48
102 536378 85071B RED CHARLIE+LOLA PERSONAL DOORSIGN 96
103 536378 21931 JUMBO STORAGE BAG SUKI 10
104 536378 21929 JUMBO BAG PINK VINTAGE PAISLEY 10
105 536380 22961 JAM MAKING SET PRINTED 24
106 536381 22139 RETROSPOT TEA SET CERAMIC 11 PC 23
107 536381 84854 GIRLY PINK TOOL SET 5
108 536381 22411 JUMBO SHOPPER VINTAGE RED PAISLEY 10
109 536381 82567 AIRLINE LOUNGE,METAL SIGN 2
110 536381 21672 WHITE SPOT RED CERAMIC DRAWER KNOB 6
111 536381 22774 RED DRAWER KNOB ACRYLIC EDWARDIAN 24
112 536381 22771 CLEAR DRAWER KNOB ACRYLIC EDWARDIAN 24
113 536381 71270 PHOTO CLIP LINE 1
114 536381 22262 FELT EGG COSY CHICKEN 1
115 536381 22637 PIGGY BANK RETROSPOT 1
116 536381 21934 SKULL SHOULDER BAG 10
117 536381 21169 YOU'RE CONFUSING ME METAL SIGN 3
118 536381 21166 COOK WITH WINE METAL SIGN 1
119 536381 21175 GIN + TONIC DIET METAL SIGN 2
120 536381 37444A YELLOW BREAKFAST CUP AND SAUCER 1
121 536381 37444C PINK BREAKFAST CUP AND SAUCER 1
122 536381 22086 PAPER CHAIN KIT 50'S CHRISTMAS 4
123 536381 22083 PAPER CHAIN KIT RETROSPOT 1
124 536381 84971S SMALL HEART FLOWERS HOOK 6
125 536381 71270 PHOTO CLIP LINE 3
126 536381 47580 TEA TIME DES TEA COSY 2
127 536381 22261 FELT EGG COSY WHITE RABBIT 1
128 536381 84832 ZINC WILLIE WINKIE CANDLE STICK 1
129 536381 22644 CERAMIC CHERRY CAKE MONEY BANK 1
130 536381 21533 RETROSPOT LARGE MILK JUG 1
131 536381 21557 SET OF 6 FUNKY BEAKERS 2
132 536381 15056BL EDWARDIAN PARASOL BLACK 2
133 536381 15056N EDWARDIAN PARASOL NATURAL 2
134 536381 22646 CERAMIC STRAWBERRY CAKE MONEY BANK 4
135 536381 22176 BLUE OWL SOFT TOY 1
136 536381 22438 BALLOON ART MAKE YOUR OWN FLOWERS 1
137 536381 21731 RED TOADSTOOL LED NIGHT LIGHT 2
138 536381 22778 GLASS CLOCHE SMALL 3
139 536381 22719 GUMBALL MONOCHROME COAT RACK 36
140 536381 21523 DOORMAT FANCY FONT HOME SWEET HOME 10
141 C536379 D Discount -1
142 536382 10002 INFLATABLE POLITICAL GLOBE 12
143 536382 21912 VINTAGE SNAKES & LADDERS 8
144 536382 21832 CHOCOLATE CALCULATOR 12
145 536382 22411 JUMBO SHOPPER VINTAGE RED PAISLEY 10
146 536382 22379 RECYCLING BAG RETROSPOT 10
147 536382 22381 TOY TIDY PINK POLKADOT 50
148 536382 22798 ANTIQUE GLASS DRESSING TABLE POT 8
149 536382 22726 ALARM CLOCK BAKELIKE GREEN 4
150 536382 22926 IVORY GIANT GARDEN THERMOMETER 12
151 536382 22839 3 TIER CAKE TIN GREEN AND CREAM 2
152 536382 22838 3 TIER CAKE TIN RED AND CREAM 2
153 536382 22783 SET 3 WICKER OVAL BASKETS W LIDS 4
154 C536383 35004C SET OF 3 COLOURED FLYING DUCKS -1
155 536384 82484 WOOD BLACK BOARD ANT WHITE FINISH 3
156 536384 84755 COLOUR GLASS T-LIGHT HOLDER HANGING 48
157 536384 22464 HANGING METAL HEART LANTERN 12
158 536384 21324 HANGING MEDINA LANTERN SMALL 6
159 536384 22457 NATURAL SLATE HEART CHALKBOARD 12
160 536384 22469 HEART OF WICKER SMALL 40
161 536384 22470 HEART OF WICKER LARGE 40
162 536384 22224 WHITE LOVEBIRD LANTERN 6
163 536384 21340 CLASSIC METAL BIRDCAGE PLANT HOLDER 2
164 536384 22189 CREAM HEART CARD HOLDER 4
165 536384 22427 ENAMEL FLOWER JUG CREAM 3
166 536384 22428 ENAMEL FIRE BUCKET CREAM 6
167 536384 22424 ENAMEL BREAD BIN CREAM 8
168 536385 22783 SET 3 WICKER OVAL BASKETS W LIDS 1
169 536385 22961 JAM MAKING SET PRINTED 12
170 536385 22960 JAM MAKING SET WITH JARS 6
171 536385 22663 JUMBO BAG DOLLY GIRL DESIGN 10
172 536385 85049A TRADITIONAL CHRISTMAS RIBBONS 12
173 536385 22168 ORGANISER WOOD ANTIQUE WHITE 2
174 536385 22662 LUNCH BAG DOLLY GIRL DESIGN 10
175 536386 84880 WHITE WIRE EGG HOLDER 36
176 536386 85099C JUMBO BAG BAROQUE BLACK WHITE 100
177 536386 85099B JUMBO BAG RED RETROSPOT 100
178 536387 79321 CHILLI LIGHTS 192
179 536387 22780 LIGHT GARLAND BUTTERFILES PINK 192
180 536387 22779 WOODEN OWLS LIGHT GARLAND 192
181 536387 22466 FAIRY TALE COTTAGE NIGHTLIGHT 432
182 536387 21731 RED TOADSTOOL LED NIGHT LIGHT 432
183 536388 21754 HOME BUILDING BLOCK WORD 3
184 536388 21755 LOVE BUILDING BLOCK WORD 3
185 536388 21523 DOORMAT FANCY FONT HOME SWEET HOME 2
186 536388 21363 HOME SMALL WOOD LETTERS 3
187 536388 21411 GINGHAM HEART DOORSTOP RED 3
188 536388 22318 FIVE HEART HANGING DECORATION 6
189 536388 22464 HANGING METAL HEART LANTERN 12
190 536388 22915 ASSORTED BOTTLE TOP MAGNETS 12
191 536388 22922 FRIDGE MAGNETS US DINER ASSORTED 12
192 536388 22969 HOMEMADE JAM SCENTED CANDLES 12
193 536388 22923 FRIDGE MAGNETS LES ENFANTS ASSORTED 12
194 536388 21115 ROSE CARAVAN DOORSTOP 4
195 536388 22469 HEART OF WICKER SMALL 12
196 536388 22242 5 HOOK HANGER MAGIC TOADSTOOL 12
197 536389 22941 CHRISTMAS LIGHTS 10 REINDEER 6
198 536389 21622 VINTAGE UNION JACK CUSHION COVER 8
199 536389 21791 VINTAGE HEADS AND TAILS CARD GAME 12
200 536389 35004C SET OF 3 COLOURED FLYING DUCKS 6
201 536389 35004G SET OF 3 GOLD FLYING DUCKS 4
202 536389 85014B RED RETROSPOT UMBRELLA 6
203 536389 85014A BLACK/BLUE POLKADOT UMBRELLA 3
204 536389 22193 RED DINER WALL CLOCK 2
205 536389 22726 ALARM CLOCK BAKELIKE GREEN 4
206 536389 22727 ALARM CLOCK BAKELIKE RED 4
207 536389 22192 BLUE DINER WALL CLOCK 2
208 536389 22191 IVORY DINER WALL CLOCK 2
209 536389 22195 LARGE HEART MEASURING SPOONS 24
210 536389 22196 SMALL HEART MEASURING SPOONS 24
211 536390 22941 CHRISTMAS LIGHTS 10 REINDEER 2
212 536390 22960 JAM MAKING SET WITH JARS 12
213 536390 22961 JAM MAKING SET PRINTED 12
214 536390 22962 JAM JAR WITH PINK LID 48
215 536390 22963 JAM JAR WITH GREEN LID 48
216 536390 22968 ROSE COTTAGE KEEPSAKE BOX 8
217 536390 84970S HANGING HEART ZINC T-LIGHT HOLDER 144
218 536390 22910 PAPER CHAIN KIT VINTAGE CHRISTMAS 40
219 536390 20668 DISCO BALL CHRISTMAS DECORATION 288
220 536390 85123A WHITE HANGING HEART T-LIGHT HOLDER 64
221 536390 22197 SMALL POPCORN HOLDER 100
222 536390 22198 LARGE POPCORN HOLDER 50
223 536390 21533 RETROSPOT LARGE MILK JUG 12
224 536390 21080 SET/20 RED RETROSPOT PAPER NAPKINS 96
225 536390 21094 SET/6 RED SPOTTY PAPER PLATES 96
226 536390 21086 SET/6 RED SPOTTY PAPER CUPS 48
227 536390 21786 POLKADOT RAIN HAT 144
228 536390 22654 DELUXE SEWING KIT 40
229 536390 21485 RETROSPOT HEART HOT WATER BOTTLE 24
230 536390 84029G KNITTED UNION FLAG HOT WATER BOTTLE 24
231 536390 84030E ENGLISH ROSE HOT WATER BOTTLE 24
232 536390 22174 PHOTO CUBE 48
233 536390 22969 HOMEMADE JAM SCENTED CANDLES 96
234 536390 85099B JUMBO BAG RED RETROSPOT 100
235 C536391 22556 PLASTERS IN TIN CIRCUS PARADE -12
236 C536391 21984 PACK OF 12 PINK PAISLEY TISSUES -24
237 C536391 21983 PACK OF 12 BLUE PAISLEY TISSUES -24
238 C536391 21980 PACK OF 12 RED RETROSPOT TISSUES -24
239 C536391 21484 CHICK GREY HOT WATER BOTTLE -12
240 C536391 22557 PLASTERS IN TIN VINTAGE PAISLEY -12
241 C536391 22553 PLASTERS IN TIN SKULLS -24
242 536392 22150 3 STRIPEY MICE FELTCRAFT 6
243 536392 22619 SET OF 6 SOLDIER SKITTLES 4
244 536392 21891 TRADITIONAL WOODEN SKIPPING ROPE 12
245 536392 21889 WOODEN BOX OF DOMINOES 12
246 536392 22827 RUSTIC SEVENTEEN DRAWER SIDEBOARD 1
247 536392 22127 PARTY CONES CARNIVAL ASSORTED 12
248 536392 22128 PARTY CONES CANDY ASSORTED 12
249 536392 22502 PICNIC BASKET WICKER SMALL 4
250 536392 84879 ASSORTED COLOUR BIRD ORNAMENT 16
251 536392 22338 STAR DECORATION PAINTED ZINC 24
252 536393 22180 RETROSPOT LAMP 8
253 536394 21506 FANCY FONT BIRTHDAY CARD, 24
254 536394 22633 HAND WARMER UNION JACK 96
255 536394 22866 HAND WARMER SCOTTY DOG DESIGN 96
256 536394 22865 HAND WARMER OWL DESIGN 96
257 536394 22632 HAND WARMER RED RETROSPOT 96
258 536394 21485 RETROSPOT HEART HOT WATER BOTTLE 12
259 536394 22349 DOG BOWL CHASING BALL DESIGN 12
260 536394 22558 CLOTHES PEGS RETROSPOT PACK 24 48
261 536394 85152 HAND OVER THE CHOCOLATE SIGN 12
262 536394 85123A WHITE HANGING HEART T-LIGHT HOLDER 32
263 536394 22652 TRAVEL SEWING KIT 20
264 536395 22188 BLACK HEART CARD HOLDER 8
265 536395 84879 ASSORTED COLOUR BIRD ORNAMENT 32
266 536395 21977 PACK OF 60 PINK PAISLEY CAKE CASES 24
267 536395 84991 60 TEATIME FAIRY CAKE CASES 24
268 536395 21212 PACK OF 72 RETROSPOT CAKE CASES 24
269 536395 21484 CHICK GREY HOT WATER BOTTLE 8
270 536395 21314 SMALL GLASS HEART TRINKET POT 8
271 536395 22730 ALARM CLOCK BAKELIKE IVORY 4
272 536395 22727 ALARM CLOCK BAKELIKE RED 8
273 536395 22729 ALARM CLOCK BAKELIKE ORANGE 8
274 536395 22726 ALARM CLOCK BAKELIKE GREEN 8
275 536395 22114 HOT WATER BOTTLE TEA AND SYMPATHY 8
276 536395 22867 HAND WARMER BIRD DESIGN 48
277 536395 22866 HAND WARMER SCOTTY DOG DESIGN 48
278 536396 85123A WHITE HANGING HEART T-LIGHT HOLDER 6
279 536396 71053 WHITE METAL LANTERN 6
280 536396 84406B CREAM CUPID HEARTS COAT HANGER 8
281 536396 15056BL EDWARDIAN PARASOL BLACK 6
282 536396 20679 EDWARDIAN PARASOL RED 6
283 536396 37370 RETRO COFFEE MUGS ASSORTED 6
284 536396 21871 SAVE THE PLANET MUG 6
285 536396 21071 VINTAGE BILLBOARD DRINK ME MUG 6
286 536396 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6
287 536396 82483 WOOD 2 DRAWER CABINET WHITE FINISH 2
288 536396 82486 WOOD S/3 CABINET ANT WHITE FINISH 4
289 536396 82482 WOODEN PICTURE FRAME WHITE FINISH 6
290 536396 82494L WOODEN FRAME ANTIQUE WHITE 12
291 536396 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
292 536396 84029E RED WOOLLY HOTTIE WHITE HEART. 6
293 536396 22752 SET 7 BABUSHKA NESTING BOXES 2
294 536396 22803 IVORY EMBROIDERED QUILT 2
295 536396 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
296 536397 35004B SET OF 3 BLACK FLYING DUCKS 12
297 536397 35004C SET OF 3 COLOURED FLYING DUCKS 48
298 536398 21980 PACK OF 12 RED RETROSPOT TISSUES 24
299 536398 21844 RED RETROSPOT MUG 6
300 536398 22468 BABUSHKA LIGHTS STRING OF 10 4
301 536398 22637 PIGGY BANK RETROSPOT 8
302 536398 22752 SET 7 BABUSHKA NESTING BOXES 6
303 536398 48185 DOORMAT FAIRY CAKE 2
304 536398 22632 HAND WARMER RED RETROSPOT 12
305 536398 22866 HAND WARMER SCOTTY DOG DESIGN 12
306 536398 22865 HAND WARMER OWL DESIGN 12
307 536398 21232 STRAWBERRY CERAMIC TRINKET BOX 12
308 536398 22064 PINK DOUGHNUT TRINKET POT 12
309 536398 22449 SILK PURSE BABUSHKA PINK 6
310 536398 22114 HOT WATER BOTTLE TEA AND SYMPATHY 4
311 536398 22835 HOT WATER BOTTLE I AM SO POORLY 8
312 536398 22112 CHOCOLATE HOT WATER BOTTLE 9
313 536398 21479 WHITE SKULL HOT WATER BOTTLE 4
314 536398 22111 SCOTTIE DOG HOT WATER BOTTLE 9
315 536399 22632 HAND WARMER RED POLKA DOT 6
316 536399 22633 HAND WARMER UNION JACK 6
317 536400 22969 HOMEMADE JAM SCENTED CANDLES 12
318 536401 22110 BIRD HOUSE HOT WATER BOTTLE 1
319 536401 22098 BOUDOIR SQUARE TISSUE BOX 1
320 536401 22100 SKULLS SQUARE TISSUE BOX 2
321 536401 22766 PHOTO FRAME CORNICE 1
322 536401 22451 SILK PURSE BABUSHKA RED 1
323 536401 22549 PICTURE DOMINOES 1
324 536401 84744 S/6 SEW ON CROCHET FLOWERS 1
325 536401 85049E SCANDINAVIAN REDS RIBBONS 2
326 536401 21328 BALLOONS WRITING SET 1
327 536401 22961 JAM MAKING SET PRINTED 4
328 536401 17091A LAVENDER INCENSE IN TIN 1
329 536401 22473 TV DINNER TRAY VINTAGE PAISLEY 1
330 536401 84509A SET OF 4 ENGLISH ROSE PLACEMATS 2
331 536401 84510A SET OF 4 ENGLISH ROSE COASTERS 2
332 536401 22767 TRIPLE PHOTO FRAME CORNICE 2
333 536401 22768 FAMILY PHOTO FRAME CORNICE 1
334 536401 21463 MIRRORED DISCO BALL 1
335 536401 21464 DISCO BALL ROTATOR BATTERY OPERATED 1
336 536401 20820 SILVER LOOKING MIRROR 3
337 536401 85150 LADIES & GENTLEMEN METAL SIGN 1
338 536401 22117 METAL SIGN HER DINNER IS SERVED 1
339 536401 21169 YOU'RE CONFUSING ME METAL SIGN 2
340 536401 48129 DOORMAT TOPIARY 1
341 536401 82580 BATHROOM METAL SIGN 1
342 536401 82578 KITCHEN METAL SIGN 1
343 536401 82581 TOILET METAL SIGN 2
344 536401 22413 METAL SIGN TAKE IT OR LEAVE IT 4
345 536401 21907 I'M ON HOLIDAY METAL SIGN 2
346 536401 22441 GROW YOUR OWN BASIL IN ENAMEL MUG 1
347 536401 21122 SET/10 PINK POLKADOT PARTY CANDLES 1
348 536401 22851 SET 20 NAPKINS FAIRY CAKES DESIGN 1
349 536401 84991 60 TEATIME FAIRY CAKE CASES 3
350 536401 22810 SET OF 6 T-LIGHTS SNOWMEN 1
351 536401 22809 SET OF 6 T-LIGHTS SANTA 1
352 536401 22435 SET OF 9 HEART SHAPED BALLOONS 2
353 536401 20966 SANDWICH BATH SPONGE 3
354 536401 20963 APPLE BATH SPONGE 1
355 536401 20961 STRAWBERRY BATH SPONGE 1
356 536401 22068 BLACK PIRATE TREASURE CHEST 2
357 536401 21743 STAR PORTABLE TABLE LIGHT 2
358 536401 21744 SNOWFLAKE PORTABLE TABLE LIGHT 2
359 536401 84709B PINK OVAL JEWELLED MIRROR 1
360 536401 21592 RETROSPOT CIGAR BOX MATCHES 1
361 536401 21587 COSY HOUR GIANT TUBE MATCHES 2
362 536401 20992 JAZZ HEARTS PURSE NOTEBOOK 9
363 536401 22662 LUNCH BAG DOLLY GIRL DESIGN 1
364 536401 85123A WHITE HANGING HEART T-LIGHT HOLDER 4
365 536401 22804 CANDLEHOLDER PINK HANGING HEART 3
366 536401 82483 WOOD 2 DRAWER CABINET WHITE FINISH 1
367 536401 20749 ASSORTED COLOUR MINI CASES 1
368 536401 20725 LUNCH BAG RED RETROSPOT 1
369 536401 22382 LUNCH BAG SPACEBOY DESIGN 2
370 536401 20726 LUNCH BAG WOODLAND 1
371 536401 22384 LUNCH BAG PINK POLKADOT 1
372 536401 22467 GUMBALL COAT RACK 5
373 536401 84625C BLUE NEW BAROQUE CANDLESTICK CANDLE 3
374 536401 84625A PINK NEW BAROQUECANDLESTICK CANDLE 3
375 536401 21108 FAIRY CAKE FLANNEL ASSORTED COLOUR 9
376 536401 22848 BREAD BIN DINER STYLE PINK 1
377 536401 21033 JUMBO BAG CHARLIE AND LOLA TOYS 4
378 536401 47570B TEA TIME TABLE CLOTH 1
379 536401 84030E ENGLISH ROSE HOT WATER BOTTLE 1
380 536401 22428 ENAMEL FIRE BUCKET CREAM 2
381 536401 22502 PICNIC BASKET WICKER SMALL 2
382 536402 22086 PAPER CHAIN KIT 50'S CHRISTMAS 40
383 536402 22910 PAPER CHAIN KIT VINTAGE CHRISTMAS 40
384 536402 22837 HOT WATER BOTTLE BABUSHKA 36
385 536403 22867 HAND WARMER BIRD DESIGN 96
386 536403 POST POSTAGE 1
387 536404 22297 HEART IVORY TRELLIS SMALL 24
388 536404 22771 CLEAR DRAWER KNOB ACRYLIC EDWARDIAN 12
389 536404 22772 PINK DRAWER KNOB ACRYLIC EDWARDIAN 12
390 536404 22773 GREEN DRAWER KNOB ACRYLIC EDWARDIAN 12
391 536404 22805 BLUE DRAWER KNOB ACRYLIC EDWARDIAN 12
392 536404 22469 HEART OF WICKER SMALL 12
393 536404 22197 SMALL POPCORN HOLDER 36
394 536404 21125 SET 6 FOOTBALL CELEBRATION CANDLES 12
395 536404 21126 SET OF 6 GIRLS CELEBRATION CANDLES 12
396 536404 85049C ROMANTIC PINKS RIBBONS 12
397 536404 85049D BRIGHT BLUES RIBBONS 12
398 536404 85049E SCANDINAVIAN REDS RIBBONS 12
399 536404 85049G CHOCOLATE BOX RIBBONS 12
400 536404 21061 PARTY INVITES FOOTBALL 12
401 536404 21063 PARTY INVITES JAZZ HEARTS 12
402 536404 21062 PARTY INVITES SPACEMAN 12
403 536404 84380 SET OF 3 BUTTERFLY COOKIE CUTTERS 12
404 536404 84378 SET OF 3 HEART COOKIE CUTTERS 12
405 536404 22964 3 PIECE SPACEBOY COOKIE CUTTER SET 12
406 536404 21213 PACK OF 72 SKULL CAKE CASES 24
407 536404 22417 PACK OF 60 SPACEBOY CAKE CASES 24
408 536404 21212 PACK OF 72 RETROSPOT CAKE CASES 24
409 536404 84992 72 SWEETHEART FAIRY CAKE CASES 24
410 536404 21975 PACK OF 60 DINOSAUR CAKE CASES 24
411 536404 22383 LUNCH BAG SUKI DESIGN 10
412 536404 20728 LUNCH BAG CARS BLUE 10
413 536404 20727 LUNCH BAG BLACK SKULL. 10
414 536404 22296 HEART IVORY TRELLIS LARGE 24
415 536405 20914 SET/5 RED RETROSPOT LID GLASS BOWLS 128
416 536406 85123A WHITE HANGING HEART T-LIGHT HOLDER 8
417 536406 71053 WHITE METAL LANTERN 8
418 536406 84406B CREAM CUPID HEARTS COAT HANGER 8
419 536406 20679 EDWARDIAN PARASOL RED 6
420 536406 37370 RETRO COFFEE MUGS ASSORTED 6
421 536406 21871 SAVE THE PLANET MUG 6
422 536406 21071 VINTAGE BILLBOARD DRINK ME MUG 6
423 536406 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6
424 536406 82483 WOOD 2 DRAWER CABINET WHITE FINISH 4
425 536406 82486 WOOD S/3 CABINET ANT WHITE FINISH 4
426 536406 82482 WOODEN PICTURE FRAME WHITE FINISH 6
427 536406 82494L WOODEN FRAME ANTIQUE WHITE 6
428 536406 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
429 536406 84029E RED WOOLLY HOTTIE WHITE HEART. 6
430 536406 22752 SET 7 BABUSHKA NESTING BOXES 2
431 536406 22803 IVORY EMBROIDERED QUILT 2
432 536406 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
433 536407 22632 HAND WARMER RED POLKA DOT 6
434 536407 22633 HAND WARMER UNION JACK 6
435 536408 22537 MAGIC DRAWING SLATE DINOSAUR 24
436 536408 22533 MAGIC DRAWING SLATE BAKE A CAKE 24
437 536408 20982 12 PENCILS TALL TUBE SKULLS 12
438 536408 21832 CHOCOLATE CALCULATOR 12
439 536408 21915 RED HARMONICA IN BOX 12
440 536408 21914 BLUE HARMONICA IN BOX 12
441 536408 21544 SKULLS WATER TRANSFER TATTOOS 12
442 536408 22813 PACK 3 BOXES BIRD PANNETONE 12
443 536408 22114 HOT WATER BOTTLE TEA AND SYMPATHY 4
444 536408 84029E RED WOOLLY HOTTIE WHITE HEART. 4
445 536408 21479 WHITE SKULL HOT WATER BOTTLE 4
446 536408 22964 3 PIECE SPACEBOY COOKIE CUTTER SET 6
447 536408 84375 SET OF 20 KIDS COOKIE CUTTERS 12
448 536408 22418 10 COLOUR SPACEBOY PEN 24
449 536408 22178 VICTORIAN GLASS HANGING T-LIGHT 12
450 536408 84970L SINGLE HEART ZINC T-LIGHT HOLDER 12
451 536408 21733 RED HANGING HEART T-LIGHT HOLDER 6
452 536408 22465 HANGING METAL STAR LANTERN 12
453 536408 84949 SILVER HANGING T-LIGHT HOLDER 6
454 536408 20685 DOORMAT RED RETROSPOT 2
455 536408 48194 DOORMAT HEARTS 2
456 536408 22488 NATURAL SLATE RECTANGLE CHALKBOARD 12
457 536408 22219 LOVEBIRD HANGING DECORATION WHITE 12
458 536408 84879 ASSORTED COLOUR BIRD ORNAMENT 8
459 536408 21754 HOME BUILDING BLOCK WORD 3
460 536408 21755 LOVE BUILDING BLOCK WORD 3
461 536408 22766 PHOTO FRAME CORNICE 8
462 536408 22610 PENS ASSORTED FUNNY FACE 36
463 536408 22716 CARD CIRCUS PARADE 12
464 536408 22706 WRAP COWBOYS 25
465 536408 22371 AIRLINE BAG VINTAGE TOKYO 78 4
466 536408 85014B RED RETROSPOT UMBRELLA 3
467 536408 85014A BLACK/BLUE POLKADOT UMBRELLA 3
468 536408 84997B RED 3 PIECE RETROSPOT CUTLERY SET 6
469 536408 21212 PACK OF 72 RETROSPOT CAKE CASES 24
470 536408 21210 SET OF 72 RETROSPOT PAPER DOILIES 12
471 536408 22914 BLUE COAT RACK PARIS FASHION 3
472 536408 22553 PLASTERS IN TIN SKULLS 12
473 536408 16237 SLEEPING CAT ERASERS 30
474 536408 22714 CARD BIRTHDAY COWBOY 12
475 536408 22812 PACK 3 BOXES CHRISTMAS PANNETONE 12
476 536408 84347 ROTATING SILVER ANGELS T-LIGHT HLDR 6
477 536408 21587 COSY HOUR GIANT TUBE MATCHES 12
478 536408 22736 RIBBON REEL MAKING SNOWMEN 10
479 536408 22492 MINI PAINT SET VINTAGE 36
480 536408 22620 4 TRADITIONAL SPINNING TOPS 12
481 536408 22619 SET OF 6 SOLDIER SKITTLES 4
482 536408 21705 BAG 500g SWIRLY MARBLES 12
483 536409 90199C 5 STRAND GLASS NECKLACE CRYSTAL 3
484 536409 21479 WHITE SKULL HOT WATER BOTTLE 1
485 536409 22111 SCOTTIE DOG HOT WATER BOTTLE 1
486 536409 22785 SQUARECUSHION COVER PINK UNION FLAG 1
487 536409 22975 SPACEBOY CHILDRENS EGG CUP 1
488 536409 22972 CHILDREN'S SPACEBOY MUG 1
489 536409 22866 HAND WARMER SCOTTY DOG DESIGN 1
490 536409 22568 FELTCRAFT CUSHION OWL 1
491 536409 85116 BLACK CANDELABRA T-LIGHT HOLDER 1
492 536409 22664 TOY TIDY DOLLY GIRL DESIGN 1
493 536409 21609 SET 12 LAVENDER BOTANICAL T-LIGHTS 1
494 536409 21866 UNION JACK FLAG LUGGAGE TAG 1
495 536409 20669 RED HEART LUGGAGE TAG 1
496 536409 90129F RED GLASS TASSLE BAG CHARM 1
497 536409 90210B CLEAR ACRYLIC FACETED BANGLE 1
498 536409 90199C 5 STRAND GLASS NECKLACE CRYSTAL 1
499 536409 21955 DOORMAT UNION JACK GUNS AND ROSES 1
InvoiceDate UnitPrice CustomerID Country
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1 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
2 2010-12-01 08:26:00 2.75 17850.0 United Kingdom
3 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
4 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
5 2010-12-01 08:26:00 7.65 17850.0 United Kingdom
6 2010-12-01 08:26:00 4.25 17850.0 United Kingdom
7 2010-12-01 08:28:00 1.85 17850.0 United Kingdom
8 2010-12-01 08:28:00 1.85 17850.0 United Kingdom
9 2010-12-01 08:34:00 1.69 13047.0 United Kingdom
10 2010-12-01 08:34:00 2.10 13047.0 United Kingdom
11 2010-12-01 08:34:00 2.10 13047.0 United Kingdom
12 2010-12-01 08:34:00 3.75 13047.0 United Kingdom
13 2010-12-01 08:34:00 1.65 13047.0 United Kingdom
14 2010-12-01 08:34:00 4.25 13047.0 United Kingdom
15 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
16 2010-12-01 08:34:00 9.95 13047.0 United Kingdom
17 2010-12-01 08:34:00 5.95 13047.0 United Kingdom
18 2010-12-01 08:34:00 5.95 13047.0 United Kingdom
19 2010-12-01 08:34:00 7.95 13047.0 United Kingdom
20 2010-12-01 08:34:00 7.95 13047.0 United Kingdom
21 2010-12-01 08:34:00 4.25 13047.0 United Kingdom
22 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
23 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
24 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
25 2010-12-01 08:35:00 5.95 13047.0 United Kingdom
26 2010-12-01 08:45:00 3.75 12583.0 France
27 2010-12-01 08:45:00 3.75 12583.0 France
28 2010-12-01 08:45:00 3.75 12583.0 France
29 2010-12-01 08:45:00 0.85 12583.0 France
30 2010-12-01 08:45:00 0.65 12583.0 France
31 2010-12-01 08:45:00 0.85 12583.0 France
32 2010-12-01 08:45:00 1.25 12583.0 France
33 2010-12-01 08:45:00 2.95 12583.0 France
34 2010-12-01 08:45:00 2.95 12583.0 France
35 2010-12-01 08:45:00 1.95 12583.0 France
36 2010-12-01 08:45:00 1.95 12583.0 France
37 2010-12-01 08:45:00 1.95 12583.0 France
38 2010-12-01 08:45:00 0.85 12583.0 France
39 2010-12-01 08:45:00 1.65 12583.0 France
40 2010-12-01 08:45:00 2.95 12583.0 France
41 2010-12-01 08:45:00 3.75 12583.0 France
42 2010-12-01 08:45:00 0.42 12583.0 France
43 2010-12-01 08:45:00 0.42 12583.0 France
44 2010-12-01 08:45:00 0.65 12583.0 France
45 2010-12-01 08:45:00 18.00 12583.0 France
46 2010-12-01 09:00:00 2.55 13748.0 United Kingdom
47 2010-12-01 09:01:00 1.85 17850.0 United Kingdom
48 2010-12-01 09:01:00 1.85 17850.0 United Kingdom
49 2010-12-01 09:02:00 2.55 17850.0 United Kingdom
50 2010-12-01 09:02:00 3.39 17850.0 United Kingdom
51 2010-12-01 09:02:00 2.75 17850.0 United Kingdom
52 2010-12-01 09:02:00 4.95 17850.0 United Kingdom
53 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
54 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
55 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
56 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
57 2010-12-01 09:02:00 4.95 17850.0 United Kingdom
58 2010-12-01 09:02:00 6.95 17850.0 United Kingdom
59 2010-12-01 09:02:00 2.10 17850.0 United Kingdom
60 2010-12-01 09:02:00 2.55 17850.0 United Kingdom
61 2010-12-01 09:02:00 3.39 17850.0 United Kingdom
62 2010-12-01 09:02:00 3.39 17850.0 United Kingdom
63 2010-12-01 09:02:00 7.65 17850.0 United Kingdom
64 2010-12-01 09:02:00 4.25 17850.0 United Kingdom
65 2010-12-01 09:09:00 10.95 15100.0 United Kingdom
66 2010-12-01 09:32:00 2.55 17850.0 United Kingdom
67 2010-12-01 09:32:00 3.39 17850.0 United Kingdom
68 2010-12-01 09:32:00 2.75 17850.0 United Kingdom
69 2010-12-01 09:32:00 4.95 17850.0 United Kingdom
70 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
71 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
72 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
73 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
74 2010-12-01 09:32:00 4.95 17850.0 United Kingdom
75 2010-12-01 09:32:00 6.95 17850.0 United Kingdom
76 2010-12-01 09:32:00 2.10 17850.0 United Kingdom
77 2010-12-01 09:32:00 2.55 17850.0 United Kingdom
78 2010-12-01 09:32:00 3.39 17850.0 United Kingdom
79 2010-12-01 09:32:00 3.39 17850.0 United Kingdom
80 2010-12-01 09:32:00 7.65 17850.0 United Kingdom
81 2010-12-01 09:32:00 4.25 17850.0 United Kingdom
82 2010-12-01 09:32:00 3.45 15291.0 United Kingdom
83 2010-12-01 09:32:00 2.55 15291.0 United Kingdom
84 2010-12-01 09:34:00 1.85 17850.0 United Kingdom
85 2010-12-01 09:34:00 1.85 17850.0 United Kingdom
86 2010-12-01 09:37:00 1.95 14688.0 United Kingdom
87 2010-12-01 09:37:00 1.95 14688.0 United Kingdom
88 2010-12-01 09:37:00 2.95 14688.0 United Kingdom
89 2010-12-01 09:37:00 0.85 14688.0 United Kingdom
90 2010-12-01 09:37:00 3.75 14688.0 United Kingdom
91 2010-12-01 09:37:00 3.75 14688.0 United Kingdom
92 2010-12-01 09:37:00 0.85 14688.0 United Kingdom
93 2010-12-01 09:37:00 1.65 14688.0 United Kingdom
94 2010-12-01 09:37:00 2.55 14688.0 United Kingdom
95 2010-12-01 09:37:00 2.55 14688.0 United Kingdom
96 2010-12-01 09:37:00 0.42 14688.0 United Kingdom
97 2010-12-01 09:37:00 0.55 14688.0 United Kingdom
98 2010-12-01 09:37:00 0.55 14688.0 United Kingdom
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470 2010-12-01 11:41:00 1.45 14307.0 United Kingdom
471 2010-12-01 11:41:00 4.95 14307.0 United Kingdom
472 2010-12-01 11:41:00 1.65 14307.0 United Kingdom
473 2010-12-01 11:41:00 0.21 14307.0 United Kingdom
474 2010-12-01 11:41:00 0.42 14307.0 United Kingdom
475 2010-12-01 11:41:00 1.95 14307.0 United Kingdom
476 2010-12-01 11:41:00 2.55 14307.0 United Kingdom
477 2010-12-01 11:41:00 2.55 14307.0 United Kingdom
478 2010-12-01 11:41:00 1.65 14307.0 United Kingdom
479 2010-12-01 11:41:00 0.65 14307.0 United Kingdom
480 2010-12-01 11:41:00 1.25 14307.0 United Kingdom
481 2010-12-01 11:41:00 3.75 14307.0 United Kingdom
482 2010-12-01 11:41:00 1.65 14307.0 United Kingdom
483 2010-12-01 11:45:00 6.35 17908.0 United Kingdom
484 2010-12-01 11:45:00 3.75 17908.0 United Kingdom
485 2010-12-01 11:45:00 4.95 17908.0 United Kingdom
486 2010-12-01 11:45:00 6.75 17908.0 United Kingdom
487 2010-12-01 11:45:00 1.25 17908.0 United Kingdom
488 2010-12-01 11:45:00 1.65 17908.0 United Kingdom
489 2010-12-01 11:45:00 2.10 17908.0 United Kingdom
490 2010-12-01 11:45:00 3.75 17908.0 United Kingdom
491 2010-12-01 11:45:00 2.10 17908.0 United Kingdom
492 2010-12-01 11:45:00 2.10 17908.0 United Kingdom
493 2010-12-01 11:45:00 2.95 17908.0 United Kingdom
494 2010-12-01 11:45:00 1.25 17908.0 United Kingdom
495 2010-12-01 11:45:00 1.25 17908.0 United Kingdom
496 2010-12-01 11:45:00 2.95 17908.0 United Kingdom
497 2010-12-01 11:45:00 2.95 17908.0 United Kingdom
498 2010-12-01 11:45:00 6.35 17908.0 United Kingdom
499 2010-12-01 11:45:00 7.95 17908.0 United Kingdom
We can see the irregularities in the data. First, InvoiceNos and Stockcodes are not only number, it has some letters. There are negative quanities too.
First, we need to change Invoice Column Datatype first. then we will create a column then we will seperate the data with letters from main dataframe.
# Convert InvoiceNo to string data type
df.loc[:, 'InvoiceNo'] = df['InvoiceNo'].astype(str)We noticed the negative number in quanitity column. So, how many unique letter groups and their respective count numbers are in the dataframe.
group_counts = df['InvoiceNo'].str.extract(r'([a-zA-Z]+)').squeeze().value_counts()
print(group_counts)0
C 9288
A 3
Name: count, dtype: int64
There are only 2 group in the invoice column. We should check those first.
# Create the DataFrames A_rows and rest_rows
A_rows = df[df['InvoiceNo'].str.contains(r'A')]
C_rows = df[df['InvoiceNo'].str.contains(r'C')]
# Print the DataFrames A_rows and rest_rows using f-strings
print(f"A_rows:\n{A_rows}\n")
print(f"Rest_rows:\n{C_rows}")A_rows:
InvoiceNo StockCode Description Quantity InvoiceDate
299982 A563185 B Adjust bad debt 1 2011-08-12 14:50:00 \
299983 A563186 B Adjust bad debt 1 2011-08-12 14:51:00
299984 A563187 B Adjust bad debt 1 2011-08-12 14:52:00
UnitPrice CustomerID Country
299982 11062.06 0.0 United Kingdom
299983 -11062.06 0.0 United Kingdom
299984 -11062.06 0.0 United Kingdom
Rest_rows:
InvoiceNo StockCode Description Quantity
141 C536379 D Discount -1 \
154 C536383 35004C SET OF 3 COLOURED FLYING DUCKS -1
235 C536391 22556 PLASTERS IN TIN CIRCUS PARADE -12
236 C536391 21984 PACK OF 12 PINK PAISLEY TISSUES -24
237 C536391 21983 PACK OF 12 BLUE PAISLEY TISSUES -24
... ... ... ... ...
540449 C581490 23144 ZINC T-LIGHT HOLDER STARS SMALL -11
541541 C581499 M Manual -1
541715 C581568 21258 VICTORIAN SEWING BOX LARGE -5
541716 C581569 84978 HANGING HEART JAR T-LIGHT HOLDER -1
541717 C581569 20979 36 PENCILS TUBE RED RETROSPOT -5
InvoiceDate UnitPrice CustomerID Country
141 2010-12-01 09:41:00 27.50 14527.0 United Kingdom
154 2010-12-01 09:49:00 4.65 15311.0 United Kingdom
235 2010-12-01 10:24:00 1.65 17548.0 United Kingdom
236 2010-12-01 10:24:00 0.29 17548.0 United Kingdom
237 2010-12-01 10:24:00 0.29 17548.0 United Kingdom
... ... ... ... ...
540449 2011-12-09 09:57:00 0.83 14397.0 United Kingdom
541541 2011-12-09 10:28:00 224.69 15498.0 United Kingdom
541715 2011-12-09 11:57:00 10.95 15311.0 United Kingdom
541716 2011-12-09 11:58:00 1.25 17315.0 United Kingdom
541717 2011-12-09 11:58:00 1.25 17315.0 United Kingdom
[9288 rows x 8 columns]
It turns out that A are just adjustments and not related to our dataframe. So we will drop these rows.
# Drop rows with 'A' in the letter group from the main df
df = df[~df['InvoiceNo'].str.contains(r'A')].reset_index(drop=True)Now, let's get back to 'C' invoices. A was Adjustment so since it start wih C , it should be cancelled invoices. but we have to check and make sure that the InvoiceNo with the letters and the negative quantity are related or not.
# Check if there are any rows in the subset with missing unit prices, customer IDs, and quantities
if len(df['InvoiceNo'].str.contains(r'C')) and len(df['Quantity'] <= 0):
print("The Invoice_with_C and the negative quantities are related")
else:
print("The Invoice_with_C and the negative quantities are not related")The Invoice_with_C and the negative quantities are related
Since these two are related, the C columns are return invoices.So, we need to seperate these into another dataframe and we will check the cancelled items later.
# Filter the DataFrame for cancelled invoices
cancelled_df = df[df['InvoiceNo'].str.contains(r'C')]
# Export cancelled_df to a CSV file with all columns
cancelled_df.to_csv('cancelled_invoices.csv', index=False, columns=cancelled_df.columns)We also export cancelled_df to csv file as a backup. Now we will delete those rows from our main data frame and reset the index.
# Drop rows with 'C' in the letter group from the main df
df = df[~df['InvoiceNo'].str.contains(r'C')].reset_index(drop=True)df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 532618 entries, 0 to 532617
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 InvoiceNo 532618 non-null object
1 StockCode 532618 non-null object
2 Description 532618 non-null object
3 Quantity 532618 non-null int64
4 InvoiceDate 532618 non-null datetime64[ns]
5 UnitPrice 532618 non-null float64
6 CustomerID 532618 non-null float64
7 Country 532618 non-null object
dtypes: datetime64[ns](1), float64(2), int64(1), object(4)
memory usage: 32.5+ MB
Now we have only 532618 rows and 8 columns. We should look through our data again.
# View the selected range of rows
print(df[:100]) InvoiceNo StockCode Description Quantity
0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \
1 536365 71053 WHITE METAL LANTERN 6
2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8
3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6
5 536365 22752 SET 7 BABUSHKA NESTING BOXES 2
6 536365 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
7 536366 22633 HAND WARMER UNION JACK 6
8 536366 22632 HAND WARMER RED POLKA DOT 6
9 536367 84879 ASSORTED COLOUR BIRD ORNAMENT 32
10 536367 22745 POPPY'S PLAYHOUSE BEDROOM 6
11 536367 22748 POPPY'S PLAYHOUSE KITCHEN 6
12 536367 22749 FELTCRAFT PRINCESS CHARLOTTE DOLL 8
13 536367 22310 IVORY KNITTED MUG COSY 6
14 536367 84969 BOX OF 6 ASSORTED COLOUR TEASPOONS 6
15 536367 22623 BOX OF VINTAGE JIGSAW BLOCKS 3
16 536367 22622 BOX OF VINTAGE ALPHABET BLOCKS 2
17 536367 21754 HOME BUILDING BLOCK WORD 3
18 536367 21755 LOVE BUILDING BLOCK WORD 3
19 536367 21777 RECIPE BOX WITH METAL HEART 4
20 536367 48187 DOORMAT NEW ENGLAND 4
21 536368 22960 JAM MAKING SET WITH JARS 6
22 536368 22913 RED COAT RACK PARIS FASHION 3
23 536368 22912 YELLOW COAT RACK PARIS FASHION 3
24 536368 22914 BLUE COAT RACK PARIS FASHION 3
25 536369 21756 BATH BUILDING BLOCK WORD 3
26 536370 22728 ALARM CLOCK BAKELIKE PINK 24
27 536370 22727 ALARM CLOCK BAKELIKE RED 24
28 536370 22726 ALARM CLOCK BAKELIKE GREEN 12
29 536370 21724 PANDA AND BUNNIES STICKER SHEET 12
30 536370 21883 STARS GIFT TAPE 24
31 536370 10002 INFLATABLE POLITICAL GLOBE 48
32 536370 21791 VINTAGE HEADS AND TAILS CARD GAME 24
33 536370 21035 SET/2 RED RETROSPOT TEA TOWELS 18
34 536370 22326 ROUND SNACK BOXES SET OF4 WOODLAND 24
35 536370 22629 SPACEBOY LUNCH BOX 24
36 536370 22659 LUNCH BOX I LOVE LONDON 24
37 536370 22631 CIRCUS PARADE LUNCH BOX 24
38 536370 22661 CHARLOTTE BAG DOLLY GIRL DESIGN 20
39 536370 21731 RED TOADSTOOL LED NIGHT LIGHT 24
40 536370 22900 SET 2 TEA TOWELS I LOVE LONDON 24
41 536370 21913 VINTAGE SEASIDE JIGSAW PUZZLES 12
42 536370 22540 MINI JIGSAW CIRCUS PARADE 24
43 536370 22544 MINI JIGSAW SPACEBOY 24
44 536370 22492 MINI PAINT SET VINTAGE 36
45 536370 POST POSTAGE 3
46 536371 22086 PAPER CHAIN KIT 50'S CHRISTMAS 80
47 536372 22632 HAND WARMER RED POLKA DOT 6
48 536372 22633 HAND WARMER UNION JACK 6
49 536373 85123A WHITE HANGING HEART T-LIGHT HOLDER 6
50 536373 71053 WHITE METAL LANTERN 6
51 536373 84406B CREAM CUPID HEARTS COAT HANGER 8
52 536373 20679 EDWARDIAN PARASOL RED 6
53 536373 37370 RETRO COFFEE MUGS ASSORTED 6
54 536373 21871 SAVE THE PLANET MUG 6
55 536373 21071 VINTAGE BILLBOARD DRINK ME MUG 6
56 536373 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6
57 536373 82483 WOOD 2 DRAWER CABINET WHITE FINISH 2
58 536373 82486 WOOD S/3 CABINET ANT WHITE FINISH 4
59 536373 82482 WOODEN PICTURE FRAME WHITE FINISH 6
60 536373 82494L WOODEN FRAME ANTIQUE WHITE 6
61 536373 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
62 536373 84029E RED WOOLLY HOTTIE WHITE HEART. 6
63 536373 22752 SET 7 BABUSHKA NESTING BOXES 2
64 536373 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
65 536374 21258 VICTORIAN SEWING BOX LARGE 32
66 536375 85123A WHITE HANGING HEART T-LIGHT HOLDER 6
67 536375 71053 WHITE METAL LANTERN 6
68 536375 84406B CREAM CUPID HEARTS COAT HANGER 8
69 536375 20679 EDWARDIAN PARASOL RED 6
70 536375 37370 RETRO COFFEE MUGS ASSORTED 6
71 536375 21871 SAVE THE PLANET MUG 6
72 536375 21071 VINTAGE BILLBOARD DRINK ME MUG 6
73 536375 21068 VINTAGE BILLBOARD LOVE/HATE MUG 6
74 536375 82483 WOOD 2 DRAWER CABINET WHITE FINISH 2
75 536375 82486 WOOD S/3 CABINET ANT WHITE FINISH 4
76 536375 82482 WOODEN PICTURE FRAME WHITE FINISH 6
77 536375 82494L WOODEN FRAME ANTIQUE WHITE 6
78 536375 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
79 536375 84029E RED WOOLLY HOTTIE WHITE HEART. 6
80 536375 22752 SET 7 BABUSHKA NESTING BOXES 2
81 536375 21730 GLASS STAR FROSTED T-LIGHT HOLDER 6
82 536376 22114 HOT WATER BOTTLE TEA AND SYMPATHY 48
83 536376 21733 RED HANGING HEART T-LIGHT HOLDER 64
84 536377 22632 HAND WARMER RED POLKA DOT 6
85 536377 22633 HAND WARMER UNION JACK 6
86 536378 22386 JUMBO BAG PINK POLKADOT 10
87 536378 85099C JUMBO BAG BAROQUE BLACK WHITE 10
88 536378 21033 JUMBO BAG CHARLIE AND LOLA TOYS 10
89 536378 20723 STRAWBERRY CHARLOTTE BAG 10
90 536378 84997B RED 3 PIECE RETROSPOT CUTLERY SET 12
91 536378 84997C BLUE 3 PIECE POLKADOT CUTLERY SET 6
92 536378 21094 SET/6 RED SPOTTY PAPER PLATES 12
93 536378 20725 LUNCH BAG RED RETROSPOT 10
94 536378 21559 STRAWBERRY LUNCH BOX WITH CUTLERY 6
95 536378 22352 LUNCH BOX WITH CUTLERY RETROSPOT 6
96 536378 21212 PACK OF 72 RETROSPOT CAKE CASES 120
97 536378 21975 PACK OF 60 DINOSAUR CAKE CASES 24
98 536378 21977 PACK OF 60 PINK PAISLEY CAKE CASES 24
99 536378 84991 60 TEATIME FAIRY CAKE CASES 24
InvoiceDate UnitPrice CustomerID Country
0 2010-12-01 08:26:00 2.55 17850.0 United Kingdom
1 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
2 2010-12-01 08:26:00 2.75 17850.0 United Kingdom
3 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
4 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
5 2010-12-01 08:26:00 7.65 17850.0 United Kingdom
6 2010-12-01 08:26:00 4.25 17850.0 United Kingdom
7 2010-12-01 08:28:00 1.85 17850.0 United Kingdom
8 2010-12-01 08:28:00 1.85 17850.0 United Kingdom
9 2010-12-01 08:34:00 1.69 13047.0 United Kingdom
10 2010-12-01 08:34:00 2.10 13047.0 United Kingdom
11 2010-12-01 08:34:00 2.10 13047.0 United Kingdom
12 2010-12-01 08:34:00 3.75 13047.0 United Kingdom
13 2010-12-01 08:34:00 1.65 13047.0 United Kingdom
14 2010-12-01 08:34:00 4.25 13047.0 United Kingdom
15 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
16 2010-12-01 08:34:00 9.95 13047.0 United Kingdom
17 2010-12-01 08:34:00 5.95 13047.0 United Kingdom
18 2010-12-01 08:34:00 5.95 13047.0 United Kingdom
19 2010-12-01 08:34:00 7.95 13047.0 United Kingdom
20 2010-12-01 08:34:00 7.95 13047.0 United Kingdom
21 2010-12-01 08:34:00 4.25 13047.0 United Kingdom
22 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
23 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
24 2010-12-01 08:34:00 4.95 13047.0 United Kingdom
25 2010-12-01 08:35:00 5.95 13047.0 United Kingdom
26 2010-12-01 08:45:00 3.75 12583.0 France
27 2010-12-01 08:45:00 3.75 12583.0 France
28 2010-12-01 08:45:00 3.75 12583.0 France
29 2010-12-01 08:45:00 0.85 12583.0 France
30 2010-12-01 08:45:00 0.65 12583.0 France
31 2010-12-01 08:45:00 0.85 12583.0 France
32 2010-12-01 08:45:00 1.25 12583.0 France
33 2010-12-01 08:45:00 2.95 12583.0 France
34 2010-12-01 08:45:00 2.95 12583.0 France
35 2010-12-01 08:45:00 1.95 12583.0 France
36 2010-12-01 08:45:00 1.95 12583.0 France
37 2010-12-01 08:45:00 1.95 12583.0 France
38 2010-12-01 08:45:00 0.85 12583.0 France
39 2010-12-01 08:45:00 1.65 12583.0 France
40 2010-12-01 08:45:00 2.95 12583.0 France
41 2010-12-01 08:45:00 3.75 12583.0 France
42 2010-12-01 08:45:00 0.42 12583.0 France
43 2010-12-01 08:45:00 0.42 12583.0 France
44 2010-12-01 08:45:00 0.65 12583.0 France
45 2010-12-01 08:45:00 18.00 12583.0 France
46 2010-12-01 09:00:00 2.55 13748.0 United Kingdom
47 2010-12-01 09:01:00 1.85 17850.0 United Kingdom
48 2010-12-01 09:01:00 1.85 17850.0 United Kingdom
49 2010-12-01 09:02:00 2.55 17850.0 United Kingdom
50 2010-12-01 09:02:00 3.39 17850.0 United Kingdom
51 2010-12-01 09:02:00 2.75 17850.0 United Kingdom
52 2010-12-01 09:02:00 4.95 17850.0 United Kingdom
53 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
54 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
55 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
56 2010-12-01 09:02:00 1.06 17850.0 United Kingdom
57 2010-12-01 09:02:00 4.95 17850.0 United Kingdom
58 2010-12-01 09:02:00 6.95 17850.0 United Kingdom
59 2010-12-01 09:02:00 2.10 17850.0 United Kingdom
60 2010-12-01 09:02:00 2.55 17850.0 United Kingdom
61 2010-12-01 09:02:00 3.39 17850.0 United Kingdom
62 2010-12-01 09:02:00 3.39 17850.0 United Kingdom
63 2010-12-01 09:02:00 7.65 17850.0 United Kingdom
64 2010-12-01 09:02:00 4.25 17850.0 United Kingdom
65 2010-12-01 09:09:00 10.95 15100.0 United Kingdom
66 2010-12-01 09:32:00 2.55 17850.0 United Kingdom
67 2010-12-01 09:32:00 3.39 17850.0 United Kingdom
68 2010-12-01 09:32:00 2.75 17850.0 United Kingdom
69 2010-12-01 09:32:00 4.95 17850.0 United Kingdom
70 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
71 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
72 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
73 2010-12-01 09:32:00 1.06 17850.0 United Kingdom
74 2010-12-01 09:32:00 4.95 17850.0 United Kingdom
75 2010-12-01 09:32:00 6.95 17850.0 United Kingdom
76 2010-12-01 09:32:00 2.10 17850.0 United Kingdom
77 2010-12-01 09:32:00 2.55 17850.0 United Kingdom
78 2010-12-01 09:32:00 3.39 17850.0 United Kingdom
79 2010-12-01 09:32:00 3.39 17850.0 United Kingdom
80 2010-12-01 09:32:00 7.65 17850.0 United Kingdom
81 2010-12-01 09:32:00 4.25 17850.0 United Kingdom
82 2010-12-01 09:32:00 3.45 15291.0 United Kingdom
83 2010-12-01 09:32:00 2.55 15291.0 United Kingdom
84 2010-12-01 09:34:00 1.85 17850.0 United Kingdom
85 2010-12-01 09:34:00 1.85 17850.0 United Kingdom
86 2010-12-01 09:37:00 1.95 14688.0 United Kingdom
87 2010-12-01 09:37:00 1.95 14688.0 United Kingdom
88 2010-12-01 09:37:00 2.95 14688.0 United Kingdom
89 2010-12-01 09:37:00 0.85 14688.0 United Kingdom
90 2010-12-01 09:37:00 3.75 14688.0 United Kingdom
91 2010-12-01 09:37:00 3.75 14688.0 United Kingdom
92 2010-12-01 09:37:00 0.85 14688.0 United Kingdom
93 2010-12-01 09:37:00 1.65 14688.0 United Kingdom
94 2010-12-01 09:37:00 2.55 14688.0 United Kingdom
95 2010-12-01 09:37:00 2.55 14688.0 United Kingdom
96 2010-12-01 09:37:00 0.42 14688.0 United Kingdom
97 2010-12-01 09:37:00 0.55 14688.0 United Kingdom
98 2010-12-01 09:37:00 0.55 14688.0 United Kingdom
99 2010-12-01 09:37:00 0.55 14688.0 United Kingdom
df.describe()
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| Quantity | InvoiceDate | UnitPrice | CustomerID | |
|---|---|---|---|---|
| count | 532618.000000 | 532618 | 532618.000000 | 532618.000000 |
| mean | 10.240024 | 2011-07-04 17:05:51.870532864 | 3.868412 | 11426.529088 |
| min | -9600.000000 | 2010-12-01 08:26:00 | 0.000000 | 0.000000 |
| 25% | 1.000000 | 2011-03-28 12:13:00 | 1.250000 | 0.000000 |
| 50% | 3.000000 | 2011-07-20 11:54:00 | 2.080000 | 14362.000000 |
| 75% | 10.000000 | 2011-10-19 12:21:00 | 4.130000 | 16255.000000 |
| max | 80995.000000 | 2011-12-09 12:50:00 | 13541.330000 | 18287.000000 |
| std | 159.593999 | NaN | 32.470442 | 6810.887001 |
Although we drop our negative rows, there seems to be negative quanitites left. we should check that.
negative_quantity_rows = df[df['Quantity'] < 0]
negative_quantity_rows['Description'].value_counts()Description
Missing_Description 862
check 120
damages 45
damaged 42
? 41
sold as set on dotcom 20
Damaged 14
thrown away 9
Unsaleable, destroyed. 9
?? 7
wet damaged 5
damages? 5
ebay 5
smashed 4
missing 3
wet pallet 3
CHECK 3
mixed up 2
incorrect stock entry. 2
crushed 2
adjustment 2
wet/rusty 2
reverse 21/5/10 adjustment 2
?missing 2
damages wax 2
Dotcom sales 2
sold as 1 2
counted 2
stock check 2
???missing 2
printing smudges/thrown away 2
rusty throw away 2
dotcom 2
wet rusty 2
test 1
incorrectly put back into stock 1
Incorrect stock entry. 1
historic computer difference?....se 1
Dagamed 1
OOPS ! adjustment 1
Damages/samples 1
?display? 1
????damages???? 1
temp adjustment 1
Crushed 1
crushed ctn 1
taig adjust no stock 1
wrongly coded-23343 1
code mix up? 84930 1
Sold as 1 on dotcom 1
lost?? 1
crushed boxes 1
??? 1
lost in space 1
????missing 1
?? missing 1
rusty thrown away 1
sold with wrong barcode 1
???lost 1
missing? 1
wrongly marked carton 22804 1
Wrongly mrked had 85123a in box 1
water damaged 1
dotcom sales 1
Damages 1
WET/MOULDY 1
wet 1
wrongly coded 20713 1
20713 1
Breakages 1
stock creditted wrongly 1
20713 wrongly marked 1
wet boxes 1
Wet pallet-thrown away 1
mouldy 1
wet? 1
can't find 1
re-adjustment 1
water damage 1
wrongly marked. 23343 in box 1
mouldy, unsaleable. 1
thrown away-can't sell 1
thrown away-can't sell. 1
?lost 1
barcode problem 1
wrong barcode 1
wrong barcode (22467) 1
throw away 1
broken 1
damaged stock 1
damages/display 1
Thrown away. 1
?sold as sets? 1
? sold as sets? 1
wrongly sold sets 1
dotcom sold sets 1
Amazon sold sets 1
wrongly sold as sets 1
Dotcom set 1
MIA 1
showroom 1
incorrectly made-thrown away. 1
samples/damages 1
label mix up 1
Dotcom 1
Given away 1
mouldy, thrown away. 1
faulty 1
re dotcom quick fix. 1
Dotcom sold in 6's 1
sold in set? 1
Not rcvd in 10/11/2010 delivery 1
mix up with c 1
Show Samples 1
found some more on shelf 1
Printing smudges/thrown away 1
sold as set by dotcom 1
sold as set on dotcom and amazon 1
Water damaged 1
incorrectly credited C550456 see 47 1
reverse previous adjustment 1
damages/dotcom? 1
sold as set/6 by dotcom 1
Thrown away-rusty 1
damages/credits from ASOS. 1
cracked 1
samples 1
damages/showroom etc 1
adjust 1
wrong code 1
wrong code? 1
Missing 1
Display 1
DAMAGED 1
POSSIBLE DAMAGES OR LOST? 1
MERCHANT CHANDLER CREDIT ERROR, STO 1
mystery! Only ever imported 1800 1
sold as 22467 1
lost 1
Name: count, dtype: int64
As we can see, all description are damanged, checked, lost, smashed, and the missing_descriptions we marked. So we will seperate those rows as damaged_df and export csv as a backup.
# Filter the DataFrame for cancelled invoices
damaged_df = negative_quantity_rows
# Export damaged_df to a CSV file with all columns
damaged_df.to_csv('damaged_df.csv', index=False, columns=damaged_df.columns)# Drop the damaged rows from the main DataFrame
df = df[df['Quantity'] >= 0]
# Reset the index of the updated DataFrame
df.reset_index(drop=True, inplace=True)df.describe()
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| Quantity | InvoiceDate | UnitPrice | CustomerID | |
|---|---|---|---|---|
| count | 531282.000000 | 531282 | 531282.000000 | 531282.000000 |
| mean | 10.655317 | 2011-07-04 18:15:26.858729728 | 3.878140 | 11455.263062 |
| min | 1.000000 | 2010-12-01 08:26:00 | 0.000000 | 0.000000 |
| 25% | 1.000000 | 2011-03-28 11:59:00 | 1.250000 | 0.000000 |
| 50% | 3.000000 | 2011-07-20 12:01:00 | 2.080000 | 14375.000000 |
| 75% | 10.000000 | 2011-10-19 12:35:00 | 4.130000 | 16261.000000 |
| max | 80995.000000 | 2011-12-09 12:50:00 | 13541.330000 | 18287.000000 |
| std | 156.830764 | NaN | 32.510663 | 6795.268735 |
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 531282 entries, 0 to 531281
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 InvoiceNo 531282 non-null object
1 StockCode 531282 non-null object
2 Description 531282 non-null object
3 Quantity 531282 non-null int64
4 InvoiceDate 531282 non-null datetime64[ns]
5 UnitPrice 531282 non-null float64
6 CustomerID 531282 non-null float64
7 Country 531282 non-null object
dtypes: datetime64[ns](1), float64(2), int64(1), object(4)
memory usage: 32.4+ MB
The summary provide insights for the dataset.
Our DataFrame now has 531,282 rows with 8 columns:
1 StockCode 531282 non-null object
2 Description 531282 non-null object
3 Quantity 531282 non-null int64
4 InvoiceDate 531282 non-null datetime64[ns]
5 UnitPrice 531282 non-null float64
6 CustomerID 531282 non-null float64
7 Country 531282 non-null object
Quantity: The average quantity is approximately 10.66, with a minimum of 1 and a maximum of 80,995. The standard deviation is 156.83.
InvoiceDate: The earliest invoice date is on December 1, 2010, and the latest is on December 9, 2011.
UnitPrice: The average unit price is approximately 3.88, with a minimum of 0 and a maximum of 13,541.33. The standard deviation is 32.51.
CustomerID: The average customer ID is approximately 11,455.26, with a minimum of 0 and a maximum of 18,287. The standard deviation is 6,795.27.
# Reset the index
df = df.reset_index(drop=True)
print(f"Cleaned_Data:\n{df}")Cleaned_Data:
InvoiceNo StockCode Description Quantity
0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \
1 536365 71053 WHITE METAL LANTERN 6
2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8
3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6
4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6
... ... ... ... ...
531277 581587 22613 PACK OF 20 SPACEBOY NAPKINS 12
531278 581587 22899 CHILDREN'S APRON DOLLY GIRL 6
531279 581587 23254 CHILDRENS CUTLERY DOLLY GIRL 4
531280 581587 23255 CHILDRENS CUTLERY CIRCUS PARADE 4
531281 581587 22138 BAKING SET 9 PIECE RETROSPOT 3
InvoiceDate UnitPrice CustomerID Country
0 2010-12-01 08:26:00 2.55 17850.0 United Kingdom
1 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
2 2010-12-01 08:26:00 2.75 17850.0 United Kingdom
3 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
4 2010-12-01 08:26:00 3.39 17850.0 United Kingdom
... ... ... ... ...
531277 2011-12-09 12:50:00 0.85 12680.0 France
531278 2011-12-09 12:50:00 2.10 12680.0 France
531279 2011-12-09 12:50:00 4.15 12680.0 France
531280 2011-12-09 12:50:00 4.15 12680.0 France
531281 2011-12-09 12:50:00 4.95 12680.0 France
[531282 rows x 8 columns]
Title: UCI Online Retail Dataset - Cleaned Version
Description:
This dataset contains the final cleaned version of the UCI Online Retail Dataset, which has been processed by #EaintKyawtHmu. Aspiring to be a part of the AI/ML field, I have undertaken the task of cleaning the data to make it suitable for analysis and modeling purposes.
The UCI Online Retail Dataset is widely used in the data science community for various research and analysis tasks. However, it is important to note that the copyright for this dataset is owned by UCI, and I do not claim any copyright over it. My role was to assist my coworkers, ML engineers, in ensuring that the data is as clean and reliable as possible.
This cleaned version of the dataset is made available for anyone to use and analyze. I believe in the power of open data and collaboration within the community. Therefore, please feel free to utilize this dataset in your projects, research, or any other endeavors.
By sharing this cleaned dataset, I aim to contribute to the collective effort of improving data quality and fostering advancements in the field of AI/ML. I hope this dataset proves to be a valuable resource for researchers, data scientists, and enthusiasts alike.
Thank you for your interest, and please don't hesitate to reach out if you have any questions or need further assistance.