market-basket-analysis-with-apriori.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":2904404,"sourceType":"datasetVersion","datasetId":1779990}],"dockerImageVersionId":30497,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"In this data, I implemented market basket analysis with apriori and specified the UK data in 2020 so that I can insight into the tendency among one year.","metadata":{}},{"cell_type":"markdown","source":"* [<span style='color:#435334'> 1. |</span> Loading and Cleaning data](#section-one)\n* [<span style='color:#435334'> 2. |</span> Exploratoty Data Analysis](#section-two)\n* [<span style='color:#435334'> 3. |</span> Market Basket Analysis](#section-three)\n* [<span style='color:#435334'> 4. |</span> Conclusion](#section-four)","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\nimport plotly.express as px\nfrom matplotlib.colors import ListedColormap\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:01.370620Z","iopub.execute_input":"2024-07-03T17:01:01.370983Z","iopub.status.idle":"2024-07-03T17:01:03.331377Z","shell.execute_reply.started":"2024-07-03T17:01:01.370952Z","shell.execute_reply":"2024-07-03T17:01:03.330135Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/market-basket-analysis/Assignment-1_Data.xlsx\n/kaggle/input/market-basket-analysis/Assignment-1_Data.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"<a id=\"section-one\"></a>\n## <b><span style='color:#f0bc22'> 1. |</span> Loading and Cleaning data","metadata":{}},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-1. |</span> Loading data","metadata":{}},{"cell_type":"code","source":"df=pd.read_csv('/kaggle/input/market-basket-analysis/Assignment-1_Data.csv',delimiter=';')\ndf.head()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:03.332992Z","iopub.execute_input":"2024-07-03T17:01:03.333315Z","iopub.status.idle":"2024-07-03T17:01:04.774345Z","shell.execute_reply.started":"2024-07-03T17:01:03.333288Z","shell.execute_reply":"2024-07-03T17:01:04.773142Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   BillNo                             Itemname  Quantity              Date  \\\n0  536365   WHITE HANGING HEART T-LIGHT HOLDER         6  01.12.2010 08:26   \n1  536365                  WHITE METAL LANTERN         6  01.12.2010 08:26   \n2  536365       CREAM CUPID HEARTS COAT HANGER         8  01.12.2010 08:26   \n3  536365  KNITTED UNION FLAG HOT WATER BOTTLE         6  01.12.2010 08:26   \n4  536365       RED WOOLLY HOTTIE WHITE HEART.         6  01.12.2010 08:26   \n\n  Price  CustomerID         Country  \n0  2,55     17850.0  United Kingdom  \n1  3,39     17850.0  United Kingdom  \n2  2,75     17850.0  United Kingdom  \n3  3,39     17850.0  United Kingdom  \n4  3,39     17850.0  United Kingdom  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BillNo</th>\n      <th>Itemname</th>\n      <th>Quantity</th>\n      <th>Date</th>\n      <th>Price</th>\n      <th>CustomerID</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>536365</td>\n      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>2,55</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>536365</td>\n      <td>WHITE METAL LANTERN</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3,39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>536365</td>\n      <td>CREAM CUPID HEARTS COAT HANGER</td>\n      <td>8</td>\n      <td>01.12.2010 08:26</td>\n      <td>2,75</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>536365</td>\n      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3,39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>536365</td>\n      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3,39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:04.775708Z","iopub.execute_input":"2024-07-03T17:01:04.776117Z","iopub.status.idle":"2024-07-03T17:01:05.271430Z","shell.execute_reply.started":"2024-07-03T17:01:04.776080Z","shell.execute_reply":"2024-07-03T17:01:05.270390Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 522064 entries, 0 to 522063\nData columns (total 7 columns):\n #   Column      Non-Null Count   Dtype  \n---  ------      --------------   -----  \n 0   BillNo      522064 non-null  object \n 1   Itemname    520609 non-null  object \n 2   Quantity    522064 non-null  int64  \n 3   Date        522064 non-null  object \n 4   Price       522064 non-null  object \n 5   CustomerID  388023 non-null  float64\n 6   Country     522064 non-null  object \ndtypes: float64(1), int64(1), object(5)\nmemory usage: 27.9+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:05.273892Z","iopub.execute_input":"2024-07-03T17:01:05.274239Z","iopub.status.idle":"2024-07-03T17:01:05.678921Z","shell.execute_reply.started":"2024-07-03T17:01:05.274210Z","shell.execute_reply":"2024-07-03T17:01:05.677892Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"BillNo             0\nItemname        1455\nQuantity           0\nDate               0\nPrice              0\nCustomerID    134041\nCountry            0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-2. |</span> Dropping data with **`negative`** or **`zero quantity`**","metadata":{}},{"cell_type":"code","source":"df.loc[df['Quantity']<=0][:5]","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:05.680104Z","iopub.execute_input":"2024-07-03T17:01:05.680463Z","iopub.status.idle":"2024-07-03T17:01:05.697376Z","shell.execute_reply.started":"2024-07-03T17:01:05.680436Z","shell.execute_reply":"2024-07-03T17:01:05.696423Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"      BillNo Itemname  Quantity              Date Price  CustomerID  \\\n2359  536589      NaN       -10  01.12.2010 16:50     0         NaN   \n4289  536764      NaN       -38  02.12.2010 14:42     0         NaN   \n6998  536996      NaN       -20  03.12.2010 15:30     0         NaN   \n6999  536997      NaN       -20  03.12.2010 15:30     0         NaN   \n7000  536998      NaN        -6  03.12.2010 15:30     0         NaN   \n\n             Country  \n2359  United Kingdom  \n4289  United Kingdom  \n6998  United Kingdom  \n6999  United Kingdom  \n7000  United Kingdom  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BillNo</th>\n      <th>Itemname</th>\n      <th>Quantity</th>\n      <th>Date</th>\n      <th>Price</th>\n      <th>CustomerID</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2359</th>\n      <td>536589</td>\n      <td>NaN</td>\n      <td>-10</td>\n      <td>01.12.2010 16:50</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>4289</th>\n      <td>536764</td>\n      <td>NaN</td>\n      <td>-38</td>\n      <td>02.12.2010 14:42</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>6998</th>\n      <td>536996</td>\n      <td>NaN</td>\n      <td>-20</td>\n      <td>03.12.2010 15:30</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>6999</th>\n      <td>536997</td>\n      <td>NaN</td>\n      <td>-20</td>\n      <td>03.12.2010 15:30</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>7000</th>\n      <td>536998</td>\n      <td>NaN</td>\n      <td>-6</td>\n      <td>03.12.2010 15:30</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df=df.loc[df['Quantity']>0]","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:05.698596Z","iopub.execute_input":"2024-07-03T17:01:05.698917Z","iopub.status.idle":"2024-07-03T17:01:05.758679Z","shell.execute_reply.started":"2024-07-03T17:01:05.698876Z","shell.execute_reply":"2024-07-03T17:01:05.757669Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-3. |</span> Dropping data with **`zero price`**","metadata":{}},{"cell_type":"code","source":"df.loc[df['Price']<='0'][:5\n                        ]","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:05.759902Z","iopub.execute_input":"2024-07-03T17:01:05.760293Z","iopub.status.idle":"2024-07-03T17:01:05.813642Z","shell.execute_reply.started":"2024-07-03T17:01:05.760255Z","shell.execute_reply":"2024-07-03T17:01:05.812690Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"      BillNo Itemname  Quantity              Date Price  CustomerID  \\\n613   536414      NaN        56  01.12.2010 11:52     0         NaN   \n1937  536545      NaN         1  01.12.2010 14:32     0         NaN   \n1938  536546      NaN         1  01.12.2010 14:33     0         NaN   \n1939  536547      NaN         1  01.12.2010 14:33     0         NaN   \n1940  536549      NaN         1  01.12.2010 14:34     0         NaN   \n\n             Country  \n613   United Kingdom  \n1937  United Kingdom  \n1938  United Kingdom  \n1939  United Kingdom  \n1940  United Kingdom  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BillNo</th>\n      <th>Itemname</th>\n      <th>Quantity</th>\n      <th>Date</th>\n      <th>Price</th>\n      <th>CustomerID</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>613</th>\n      <td>536414</td>\n      <td>NaN</td>\n      <td>56</td>\n      <td>01.12.2010 11:52</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>1937</th>\n      <td>536545</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>01.12.2010 14:32</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>1938</th>\n      <td>536546</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>01.12.2010 14:33</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>1939</th>\n      <td>536547</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>01.12.2010 14:33</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>1940</th>\n      <td>536549</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>01.12.2010 14:34</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df=df.loc[df['Price']>'0']","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:05.815006Z","iopub.execute_input":"2024-07-03T17:01:05.815878Z","iopub.status.idle":"2024-07-03T17:01:05.924358Z","shell.execute_reply.started":"2024-07-03T17:01:05.815838Z","shell.execute_reply":"2024-07-03T17:01:05.923422Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-4. |</span> Dropping **`Non-product`** data.","metadata":{}},{"cell_type":"code","source":"df.loc[(df['Itemname']=='POSTAGE')|(df['Itemname']=='DOTCOM POSTAGE')|(df['Itemname']=='Adjust bad debt')|(df['Itemname']=='Manual')].head()","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:05.925908Z","iopub.execute_input":"2024-07-03T17:01:05.926310Z","iopub.status.idle":"2024-07-03T17:01:06.085194Z","shell.execute_reply.started":"2024-07-03T17:01:05.926273Z","shell.execute_reply":"2024-07-03T17:01:06.084148Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"      BillNo        Itemname  Quantity              Date   Price  CustomerID  \\\n45    536370         POSTAGE         3  01.12.2010 08:45      18     12583.0   \n377   536403         POSTAGE         1  01.12.2010 11:27      15     12791.0   \n1113  536527         POSTAGE         1  01.12.2010 13:04      18     12662.0   \n1781  536544  DOTCOM POSTAGE         1  01.12.2010 14:32  569,77         NaN   \n2192  536569          Manual         1  01.12.2010 15:35    1,25     16274.0   \n\n             Country  \n45            France  \n377      Netherlands  \n1113         Germany  \n1781  United Kingdom  \n2192  United Kingdom  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BillNo</th>\n      <th>Itemname</th>\n      <th>Quantity</th>\n      <th>Date</th>\n      <th>Price</th>\n      <th>CustomerID</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>45</th>\n      <td>536370</td>\n      <td>POSTAGE</td>\n      <td>3</td>\n      <td>01.12.2010 08:45</td>\n      <td>18</td>\n      <td>12583.0</td>\n      <td>France</td>\n    </tr>\n    <tr>\n      <th>377</th>\n      <td>536403</td>\n      <td>POSTAGE</td>\n      <td>1</td>\n      <td>01.12.2010 11:27</td>\n      <td>15</td>\n      <td>12791.0</td>\n      <td>Netherlands</td>\n    </tr>\n    <tr>\n      <th>1113</th>\n      <td>536527</td>\n      <td>POSTAGE</td>\n      <td>1</td>\n      <td>01.12.2010 13:04</td>\n      <td>18</td>\n      <td>12662.0</td>\n      <td>Germany</td>\n    </tr>\n    <tr>\n      <th>1781</th>\n      <td>536544</td>\n      <td>DOTCOM POSTAGE</td>\n      <td>1</td>\n      <td>01.12.2010 14:32</td>\n      <td>569,77</td>\n      <td>NaN</td>\n      <td>United Kingdom</td>\n    </tr>\n    <tr>\n      <th>2192</th>\n      <td>536569</td>\n      <td>Manual</td>\n      <td>1</td>\n      <td>01.12.2010 15:35</td>\n      <td>1,25</td>\n      <td>16274.0</td>\n      <td>United Kingdom</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df=df.loc[(df['Itemname']!='POSTAGE')&(df['Itemname']!='DOTCOM POSTAGE')&(df['Itemname']!='Adjust bad debt')&(df['Itemname']!='Manual')]","metadata":{"_kg_hide-output":false,"execution":{"iopub.status.busy":"2024-07-03T17:01:06.089569Z","iopub.execute_input":"2024-07-03T17:01:06.090090Z","iopub.status.idle":"2024-07-03T17:01:06.309987Z","shell.execute_reply.started":"2024-07-03T17:01:06.090050Z","shell.execute_reply":"2024-07-03T17:01:06.308938Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-5. |</span> Filling **`null`** data","metadata":{}},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:06.311107Z","iopub.execute_input":"2024-07-03T17:01:06.311429Z","iopub.status.idle":"2024-07-03T17:01:06.735475Z","shell.execute_reply.started":"2024-07-03T17:01:06.311403Z","shell.execute_reply":"2024-07-03T17:01:06.734219Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"BillNo             0\nItemname           0\nQuantity           0\nDate               0\nPrice              0\nCustomerID    130813\nCountry            0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df=df.fillna('-')\ndf.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:06.736662Z","iopub.execute_input":"2024-07-03T17:01:06.736971Z","iopub.status.idle":"2024-07-03T17:01:07.709824Z","shell.execute_reply.started":"2024-07-03T17:01:06.736944Z","shell.execute_reply":"2024-07-03T17:01:07.708687Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"BillNo        0\nItemname      0\nQuantity      0\nDate          0\nPrice         0\nCustomerID    0\nCountry       0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-6. |</span> Splitting data into **`year`** and **`month`**","metadata":{}},{"cell_type":"code","source":"df['Year']=df['Date'].apply(lambda x:x.split('.')[2])\ndf['Year']=df['Year'].apply(lambda x:x.split(' ')[0])\ndf['Month']=df['Date'].apply(lambda x:x.split('.')[1])\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:07.711322Z","iopub.execute_input":"2024-07-03T17:01:07.711707Z","iopub.status.idle":"2024-07-03T17:01:08.368183Z","shell.execute_reply.started":"2024-07-03T17:01:07.711673Z","shell.execute_reply":"2024-07-03T17:01:08.367212Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"   BillNo                             Itemname  Quantity              Date  \\\n0  536365   WHITE HANGING HEART T-LIGHT HOLDER         6  01.12.2010 08:26   \n1  536365                  WHITE METAL LANTERN         6  01.12.2010 08:26   \n2  536365       CREAM CUPID HEARTS COAT HANGER         8  01.12.2010 08:26   \n3  536365  KNITTED UNION FLAG HOT WATER BOTTLE         6  01.12.2010 08:26   \n4  536365       RED WOOLLY HOTTIE WHITE HEART.         6  01.12.2010 08:26   \n\n  Price CustomerID         Country  Year Month  \n0  2,55    17850.0  United Kingdom  2010    12  \n1  3,39    17850.0  United Kingdom  2010    12  \n2  2,75    17850.0  United Kingdom  2010    12  \n3  3,39    17850.0  United Kingdom  2010    12  \n4  3,39    17850.0  United Kingdom  2010    12  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BillNo</th>\n      <th>Itemname</th>\n      <th>Quantity</th>\n      <th>Date</th>\n      <th>Price</th>\n      <th>CustomerID</th>\n      <th>Country</th>\n      <th>Year</th>\n      <th>Month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>536365</td>\n      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>2,55</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>536365</td>\n      <td>WHITE METAL LANTERN</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3,39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>536365</td>\n      <td>CREAM CUPID HEARTS COAT HANGER</td>\n      <td>8</td>\n      <td>01.12.2010 08:26</td>\n      <td>2,75</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>536365</td>\n      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3,39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>536365</td>\n      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3,39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-7. |</span> Creating a **`Total price`** column","metadata":{}},{"cell_type":"code","source":"df['Price']=df['Price'].str.replace(',','.').astype('float64')\ndf['Total price']=df.Quantity*df.Price\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:08.369389Z","iopub.execute_input":"2024-07-03T17:01:08.369791Z","iopub.status.idle":"2024-07-03T17:01:08.663629Z","shell.execute_reply.started":"2024-07-03T17:01:08.369738Z","shell.execute_reply":"2024-07-03T17:01:08.662681Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"   BillNo                             Itemname  Quantity              Date  \\\n0  536365   WHITE HANGING HEART T-LIGHT HOLDER         6  01.12.2010 08:26   \n1  536365                  WHITE METAL LANTERN         6  01.12.2010 08:26   \n2  536365       CREAM CUPID HEARTS COAT HANGER         8  01.12.2010 08:26   \n3  536365  KNITTED UNION FLAG HOT WATER BOTTLE         6  01.12.2010 08:26   \n4  536365       RED WOOLLY HOTTIE WHITE HEART.         6  01.12.2010 08:26   \n\n   Price CustomerID         Country  Year Month  Total price  \n0   2.55    17850.0  United Kingdom  2010    12        15.30  \n1   3.39    17850.0  United Kingdom  2010    12        20.34  \n2   2.75    17850.0  United Kingdom  2010    12        22.00  \n3   3.39    17850.0  United Kingdom  2010    12        20.34  \n4   3.39    17850.0  United Kingdom  2010    12        20.34  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>BillNo</th>\n      <th>Itemname</th>\n      <th>Quantity</th>\n      <th>Date</th>\n      <th>Price</th>\n      <th>CustomerID</th>\n      <th>Country</th>\n      <th>Year</th>\n      <th>Month</th>\n      <th>Total price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>536365</td>\n      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>2.55</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n      <td>15.30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>536365</td>\n      <td>WHITE METAL LANTERN</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3.39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n      <td>20.34</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>536365</td>\n      <td>CREAM CUPID HEARTS COAT HANGER</td>\n      <td>8</td>\n      <td>01.12.2010 08:26</td>\n      <td>2.75</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n      <td>22.00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>536365</td>\n      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3.39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n      <td>20.34</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>536365</td>\n      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n      <td>6</td>\n      <td>01.12.2010 08:26</td>\n      <td>3.39</td>\n      <td>17850.0</td>\n      <td>United Kingdom</td>\n      <td>2010</td>\n      <td>12</td>\n      <td>20.34</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 1-8. |</span> Checking the **`Total price`** in each month.","metadata":{}},{"cell_type":"code","source":"df.groupby(['Year','Month'])['Total price'].sum()","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:08.664867Z","iopub.execute_input":"2024-07-03T17:01:08.665177Z","iopub.status.idle":"2024-07-03T17:01:08.760815Z","shell.execute_reply.started":"2024-07-03T17:01:08.665137Z","shell.execute_reply":"2024-07-03T17:01:08.759899Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"Year  Month\n2010  12        778386.780\n2011  01        648311.120\n      02        490058.230\n      03        659979.660\n      04        507366.971\n      05        721789.800\n      06        710158.020\n      07        642528.481\n      08        701411.420\n      09        981408.102\n      10       1072317.070\n      11       1421055.630\n      12        606953.650\nName: Total price, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"It is appropriate to look at 12-month increments to implement data analytics properly, so I'll drop the data for 2020 Dec.","metadata":{}},{"cell_type":"code","source":"df=df.loc[df['Year']!='2010']","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:08.761876Z","iopub.execute_input":"2024-07-03T17:01:08.762191Z","iopub.status.idle":"2024-07-03T17:01:09.021122Z","shell.execute_reply.started":"2024-07-03T17:01:08.762143Z","shell.execute_reply":"2024-07-03T17:01:09.020189Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"section-two\"></a>\n## <b><span style='color:#f0bc22'> 2. |</span> Exploratoty Data Analysis","metadata":{}},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 2-1. |</span> Sales amount and quantity","metadata":{}},{"cell_type":"code","source":"# Grouping and aggregating the data\nsales = df.groupby(['Year', 'Month'])[['Total price', 'Quantity']].sum()\nsales.to_csv('sales.csv')\n\n# Reading the data from CSV\nsales = pd.read_csv('sales.csv')\n\n# Pivot table creation\nsales = sales.pivot_table(index=['Year', 'Month'], aggfunc=np.sum, fill_value=0)\n\n# Define custom colors\ncustom_colors = ['#435334', '#9EB384']\ncmap = ListedColormap(custom_colors)\n\n# Plotting the data with custom colors\nax = sales.plot(kind='bar', color=custom_colors, width=0.7)\nax.set_ylabel('Total Price and Quantity')  # Set the y-axis label\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:09.022294Z","iopub.execute_input":"2024-07-03T17:01:09.022596Z","iopub.status.idle":"2024-07-03T17:01:09.518352Z","shell.execute_reply.started":"2024-07-03T17:01:09.022570Z","shell.execute_reply":"2024-07-03T17:01:09.517388Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Grouping and aggregating the data\nsales_country = df.groupby(['Year', 'Month', 'Country'])['Total price'].sum().reset_index()\nsales_country.to_csv('sales_country.csv', index=False)\n\n# Reading the data from CSV\nsales_country = pd.read_csv('sales_country.csv')\n\n# Define custom colors (16 colors)\ncustom_colors = [\n    '#435334', '#9EB384', '#D4E157', '#FF7043', '#26A69A', '#AB47BC', \n    '#42A5F5', '#7E57C2', '#FFCA28', '#66BB6A', '#EF5350', '#8D6E63',\n    '#EC407A', '#FFA726', '#29B6F6', '#66BB6A'\n]\n\n# Plotting the data with custom colors\nfig = px.bar(\n    sales_country,\n    x='Month',\n    y='Total price',\n    color='Country',\n    title='Monthly sales amount in each country in 2021',\n    color_discrete_sequence=custom_colors,\n)\n\nfig.update_layout(\n    xaxis_title='Month',\n    yaxis_title='Sales amount'\n)\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:09.519607Z","iopub.execute_input":"2024-07-03T17:01:09.520010Z","iopub.status.idle":"2024-07-03T17:01:11.387908Z","shell.execute_reply.started":"2024-07-03T17:01:09.519974Z","shell.execute_reply":"2024-07-03T17:01:11.386951Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-2.20.0.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"c5e73c26-652e-4007-aefe-68fead8388dd\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c5e73c26-652e-4007-aefe-68fead8388dd\")) {                    Plotly.newPlot(                        \"c5e73c26-652e-4007-aefe-68fead8388dd\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Australia<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Australia\",\"marker\":{\"color\":\"#435334\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Australia\",\"offsetgroup\":\"Australia\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11],\"xaxis\":\"x\",\"y\":[9017.71,14695.42,17223.99,421.6,13638.41,25187.77,4964.38,22489.2,5106.7300000000005,17150.53,7242.72],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Belgium<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Belgium\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Belgium\",\"offsetgroup\":\"Belgium\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[1080.2,1926.07,2994.98,1788.48,2486.4,3848.82,2205.57,3140.02,3779.02,5169.38,5619.76,1288.73],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=France<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"France\",\"marker\":{\"color\":\"#D4E157\",\"pattern\":{\"shape\":\"\"}},\"name\":\"France\",\"offsetgroup\":\"France\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[16174.12,7687.96,13689.55,3899.31,16210.88,14962.41,9171.19,12630.96,21898.04,24008.33,29216.76,6538.92],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Germany<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Germany\",\"marker\":{\"color\":\"#FF7043\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Germany\",\"offsetgroup\":\"Germany\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[15326.84,8825.05,12862.69,10994.79,23518.2,12122.1,14743.3,17636.77,16561.22,28422.92,24252.7,7024.17],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Greece<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Greece\",\"marker\":{\"color\":\"#26A69A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Greece\",\"offsetgroup\":\"Greece\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,3,4,7,12],\"xaxis\":\"x\",\"y\":[2661.24,387.31,509.74,321.24,545.99],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Hong Kong<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Hong Kong\",\"marker\":{\"color\":\"#AB47BC\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Hong Kong\",\"offsetgroup\":\"Hong Kong\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,4,5,6,8,10],\"xaxis\":\"x\",\"y\":[2439.76,1539.64,1966.16,618.96,2653.9500000000003,734.52],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Iceland<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Iceland\",\"marker\":{\"color\":\"#42A5F5\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Iceland\",\"offsetgroup\":\"Iceland\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,4,6,8,10,12],\"xaxis\":\"x\",\"y\":[475.39,636.25,382.52,584.9100000000001,1294.32,224.82],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Israel<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Israel\",\"marker\":{\"color\":\"#7E57C2\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Israel\",\"offsetgroup\":\"Israel\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,4,7,8,9,10],\"xaxis\":\"x\",\"y\":[379.84,779.1,110.0,1430.75,4873.81,423.73,138.03],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Italy<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Italy\",\"marker\":{\"color\":\"#FFCA28\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Italy\",\"offsetgroup\":\"Italy\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[2066.51,228.0,1676.81,737.8299999999999,587.45,202.07,147.1,1838.95,279.8,3329.69,3875.08,200.45],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Lebanon<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Lebanon\",\"marker\":{\"color\":\"#66BB6A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Lebanon\",\"offsetgroup\":\"Lebanon\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1],\"xaxis\":\"x\",\"y\":[1693.88],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Netherlands<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Netherlands\",\"marker\":{\"color\":\"#EF5350\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Netherlands\",\"offsetgroup\":\"Netherlands\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[26596.16,22831.91,22206.49,2886.56,28942.88,26753.09,11.02,40327.81,26832.26,40378.65,25640.01,11728.02],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Poland<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Poland\",\"marker\":{\"color\":\"#8D6E63\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Poland\",\"offsetgroup\":\"Poland\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,11],\"xaxis\":\"x\",\"y\":[613.22,604.05,317.78,705.8399999999999,1402.37,263.65000000000003,607.3,361.8,489.25,1361.23],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Portugal<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Portugal\",\"marker\":{\"color\":\"#EC407A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Portugal\",\"offsetgroup\":\"Portugal\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[3479.71,1017.9,2520.85,1687.75,602.83,800.46,2175.85,1221.4,1377.22,5330.13,2336.9,2416.19],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Singapore<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Singapore\",\"marker\":{\"color\":\"#FFA726\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Singapore\",\"offsetgroup\":\"Singapore\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,4,7,10],\"xaxis\":\"x\",\"y\":[2053.07,2118.74,3949.32,999.26],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Spain<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Spain\",\"marker\":{\"color\":\"#29B6F6\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Spain\",\"offsetgroup\":\"Spain\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[9694.09,1722.5,4775.15,1645.65,2697.6,2717.21,7120.92,2898.91,4461.24,7964.94,8034.96,288.21],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Sweden<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Sweden\",\"marker\":{\"color\":\"#66BB6A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Sweden\",\"offsetgroup\":\"Sweden\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[3286.32,469.5,4680.58,649.1,2641.98,676.0,6692.09,1160.86,4517.21,5671.16,2322.23,238.0],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Switzerland<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Switzerland\",\"marker\":{\"color\":\"#435334\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Switzerland\",\"offsetgroup\":\"Switzerland\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11],\"xaxis\":\"x\",\"y\":[4031.23,2374.92,1590.23,1756.94,3450.01,7544.15,3518.61,4889.89,7524.86,7265.18,7916.96],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=United Kingdom<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"United Kingdom\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"United Kingdom\",\"offsetgroup\":\"United Kingdom\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[1,2,3,4,5,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[547241.83,419050.62,573351.22,466318.821,619639.48,606395.97,578308.011,580150.26,878384.112,914957.97,1286504.78,572306.01],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Austria<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Austria\",\"marker\":{\"color\":\"#D4E157\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Austria\",\"offsetgroup\":\"Austria\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[2,3,4,5,7,8,10,11,12],\"xaxis\":\"x\",\"y\":[438.36,1308.12,584.78,1049.43,1111.95,1436.08,923.78,1209.78,523.2],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Japan<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Japan\",\"marker\":{\"color\":\"#FF7043\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Japan\",\"offsetgroup\":\"Japan\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[2,3,4,5,6,7,9,10,11],\"xaxis\":\"x\",\"y\":[5780.81,48.9,6548.5,1351.45,1607.04,3854.4,112.07999999999998,1948.04,8460.08],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Norway<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Norway\",\"marker\":{\"color\":\"#26A69A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Norway\",\"offsetgroup\":\"Norway\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[2,3,6,7,8,9,10,11,12],\"xaxis\":\"x\",\"y\":[590.9,345.01,5186.86,770.0699999999999,1505.81,8398.94,3316.73,5807.5,2745.7000000000003],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Saudi Arabia<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Saudi Arabia\",\"marker\":{\"color\":\"#AB47BC\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Saudi Arabia\",\"offsetgroup\":\"Saudi Arabia\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[2],\"xaxis\":\"x\",\"y\":[145.92],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=United Arab Emirates<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"United Arab Emirates\",\"marker\":{\"color\":\"#42A5F5\",\"pattern\":{\"shape\":\"\"}},\"name\":\"United Arab Emirates\",\"offsetgroup\":\"United Arab Emirates\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[2,9],\"xaxis\":\"x\",\"y\":[889.24,975.54],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Brazil<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Brazil\",\"marker\":{\"color\":\"#7E57C2\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Brazil\",\"offsetgroup\":\"Brazil\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[4],\"xaxis\":\"x\",\"y\":[1143.6],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=USA<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"USA\",\"marker\":{\"color\":\"#FFCA28\",\"pattern\":{\"shape\":\"\"}},\"name\":\"USA\",\"offsetgroup\":\"USA\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[4,10,12],\"xaxis\":\"x\",\"y\":[383.95,2311.2,885.24],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Unspecified<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Unspecified\",\"marker\":{\"color\":\"#66BB6A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Unspecified\",\"offsetgroup\":\"Unspecified\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[4,5,6,7,8,9,11],\"xaxis\":\"x\",\"y\":[299.1,1055.87,185.78,1425.41,531.03,286.85,965.75],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Bahrain<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Bahrain\",\"marker\":{\"color\":\"#EF5350\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Bahrain\",\"offsetgroup\":\"Bahrain\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[5],\"xaxis\":\"x\",\"y\":[548.4],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=Malta<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Malta\",\"marker\":{\"color\":\"#8D6E63\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Malta\",\"offsetgroup\":\"Malta\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[6,8,11],\"xaxis\":\"x\",\"y\":[703.16,1079.0,288.43],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=RSA<br>Month=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"RSA\",\"marker\":{\"color\":\"#EC407A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"RSA\",\"offsetgroup\":\"RSA\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[10],\"xaxis\":\"x\",\"y\":[1002.31],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Month\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Sales amount\"}},\"legend\":{\"title\":{\"text\":\"Country\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Monthly sales amount in each country in 2021\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('c5e73c26-652e-4007-aefe-68fead8388dd');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Most of the sales amounts are occupied by the UK.","metadata":{}},{"cell_type":"code","source":"# Grouping and aggregating the data\ncountry = df.groupby('Country')['Total price'].sum().reset_index()\ncountry.to_csv('country.csv', index=False)\n\n# Reading the data from CSV\ncountry = pd.read_csv('country.csv')\n\n# Define custom colors (16 colors)\ncustom_colors = ['#9EB384']\n\n# Plotting the data with custom colors\nfig = px.bar(\n    country,\n    x='Country',\n    y='Total price',\n    title='Sales amount in each country in 2021',\n    color='Country',\n    color_discrete_sequence=custom_colors\n)\n\nfig.update_layout(\n    xaxis={'categoryorder': 'total descending'},\n    yaxis_title='Sales amount'\n)\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:11.389283Z","iopub.execute_input":"2024-07-03T17:01:11.389606Z","iopub.status.idle":"2024-07-03T17:01:11.601362Z","shell.execute_reply.started":"2024-07-03T17:01:11.389578Z","shell.execute_reply":"2024-07-03T17:01:11.600425Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"6ac81091-c7d3-4df9-80ca-a3b7396e6890\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6ac81091-c7d3-4df9-80ca-a3b7396e6890\")) {                    Plotly.newPlot(                        \"6ac81091-c7d3-4df9-80ca-a3b7396e6890\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Australia\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Australia\",\"offsetgroup\":\"Australia\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Australia\"],\"xaxis\":\"x\",\"y\":[137138.46],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Austria\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Austria\",\"offsetgroup\":\"Austria\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Austria\"],\"xaxis\":\"x\",\"y\":[8585.48],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Bahrain\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Bahrain\",\"offsetgroup\":\"Bahrain\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Bahrain\"],\"xaxis\":\"x\",\"y\":[548.4],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Belgium\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Belgium\",\"offsetgroup\":\"Belgium\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Belgium\"],\"xaxis\":\"x\",\"y\":[35327.43],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Brazil\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Brazil\",\"offsetgroup\":\"Brazil\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Brazil\"],\"xaxis\":\"x\",\"y\":[1143.6],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"France\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"France\",\"offsetgroup\":\"France\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"France\"],\"xaxis\":\"x\",\"y\":[176088.43],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Germany\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Germany\",\"offsetgroup\":\"Germany\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Germany\"],\"xaxis\":\"x\",\"y\":[192290.75],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Greece\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Greece\",\"offsetgroup\":\"Greece\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Greece\"],\"xaxis\":\"x\",\"y\":[4425.52],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Hong Kong\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Hong Kong\",\"offsetgroup\":\"Hong Kong\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Hong Kong\"],\"xaxis\":\"x\",\"y\":[9952.99],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Iceland\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Iceland\",\"offsetgroup\":\"Iceland\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Iceland\"],\"xaxis\":\"x\",\"y\":[3598.21],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Israel\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Israel\",\"offsetgroup\":\"Israel\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Israel\"],\"xaxis\":\"x\",\"y\":[8135.26],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Italy\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Italy\",\"offsetgroup\":\"Italy\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Italy\"],\"xaxis\":\"x\",\"y\":[15169.74],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Japan\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Japan\",\"offsetgroup\":\"Japan\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Japan\"],\"xaxis\":\"x\",\"y\":[29711.3],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Lebanon\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Lebanon\",\"offsetgroup\":\"Lebanon\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Lebanon\"],\"xaxis\":\"x\",\"y\":[1693.88],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Malta\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Malta\",\"offsetgroup\":\"Malta\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Malta\"],\"xaxis\":\"x\",\"y\":[2070.59],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Netherlands\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Netherlands\",\"offsetgroup\":\"Netherlands\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Netherlands\"],\"xaxis\":\"x\",\"y\":[275134.86],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Norway\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Norway\",\"offsetgroup\":\"Norway\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Norway\"],\"xaxis\":\"x\",\"y\":[28667.52],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Poland\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Poland\",\"offsetgroup\":\"Poland\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Poland\"],\"xaxis\":\"x\",\"y\":[6726.49],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Portugal\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Portugal\",\"offsetgroup\":\"Portugal\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Portugal\"],\"xaxis\":\"x\",\"y\":[24967.19],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"RSA\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"RSA\",\"offsetgroup\":\"RSA\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"RSA\"],\"xaxis\":\"x\",\"y\":[1002.31],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Saudi Arabia\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Saudi Arabia\",\"offsetgroup\":\"Saudi Arabia\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Saudi Arabia\"],\"xaxis\":\"x\",\"y\":[145.92],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Singapore\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Singapore\",\"offsetgroup\":\"Singapore\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Singapore\"],\"xaxis\":\"x\",\"y\":[9120.39],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Spain\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Spain\",\"offsetgroup\":\"Spain\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Spain\"],\"xaxis\":\"x\",\"y\":[54021.38],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Sweden\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Sweden\",\"offsetgroup\":\"Sweden\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Sweden\"],\"xaxis\":\"x\",\"y\":[33005.03],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Switzerland\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Switzerland\",\"offsetgroup\":\"Switzerland\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Switzerland\"],\"xaxis\":\"x\",\"y\":[51862.98],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"USA\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"USA\",\"offsetgroup\":\"USA\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"USA\"],\"xaxis\":\"x\",\"y\":[3580.39],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"United Arab Emirates\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"United Arab Emirates\",\"offsetgroup\":\"United Arab Emirates\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"United Arab Emirates\"],\"xaxis\":\"x\",\"y\":[1864.78],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"United Kingdom\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"United Kingdom\",\"offsetgroup\":\"United Kingdom\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"United Kingdom\"],\"xaxis\":\"x\",\"y\":[8042609.084],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"Country=%{x}<br>Total price=%{y}<extra></extra>\",\"legendgroup\":\"Unspecified\",\"marker\":{\"color\":\"#9EB384\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Unspecified\",\"offsetgroup\":\"Unspecified\",\"orientation\":\"v\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":[\"Unspecified\"],\"xaxis\":\"x\",\"y\":[4749.79],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Country\"},\"categoryorder\":\"total descending\",\"categoryarray\":[\"Australia\",\"Austria\",\"Bahrain\",\"Belgium\",\"Brazil\",\"France\",\"Germany\",\"Greece\",\"Hong Kong\",\"Iceland\",\"Israel\",\"Italy\",\"Japan\",\"Lebanon\",\"Malta\",\"Netherlands\",\"Norway\",\"Poland\",\"Portugal\",\"RSA\",\"Saudi Arabia\",\"Singapore\",\"Spain\",\"Sweden\",\"Switzerland\",\"USA\",\"United Arab Emirates\",\"United Kingdom\",\"Unspecified\"]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Sales amount\"}},\"legend\":{\"title\":{\"text\":\"Country\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Sales amount in each country in 2021\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('6ac81091-c7d3-4df9-80ca-a3b7396e6890');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 2-2. |</span> Category","metadata":{}},{"cell_type":"markdown","source":"> ### Top 10 highest sales amount items","metadata":{}},{"cell_type":"code","source":"cm=sns.light_palette(\"green\",as_cmap=True)\n\nitem_sales=df.groupby('Itemname')['Price'].sum().sort_values(ascending=False)[:10]\nitem_sales.to_csv('item_sales.csv')\nitem_sales=pd.read_csv('item_sales.csv')\nitem_sales.style.background_gradient(cmap=cm).set_precision(2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:11.602737Z","iopub.execute_input":"2024-07-03T17:01:11.603052Z","iopub.status.idle":"2024-07-03T17:01:11.748146Z","shell.execute_reply.started":"2024-07-03T17:01:11.603025Z","shell.execute_reply":"2024-07-03T17:01:11.747210Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7cd0c811b790>","text/html":"<style type=\"text/css\">\n#T_9856c_row0_col1 {\n  background-color: #008000;\n  color: #f1f1f1;\n}\n#T_9856c_row1_col1 {\n  background-color: #c0dec0;\n  color: #000000;\n}\n#T_9856c_row2_col1 {\n  background-color: #d7e9d7;\n  color: #000000;\n}\n#T_9856c_row3_col1 {\n  background-color: #e0eee0;\n  color: #000000;\n}\n#T_9856c_row4_col1 {\n  background-color: #e2efe2;\n  color: #000000;\n}\n#T_9856c_row5_col1, #T_9856c_row6_col1 {\n  background-color: #e4f0e4;\n  color: #000000;\n}\n#T_9856c_row7_col1 {\n  background-color: #e7f1e7;\n  color: #000000;\n}\n#T_9856c_row8_col1 {\n  background-color: #e8f2e8;\n  color: #000000;\n}\n#T_9856c_row9_col1 {\n  background-color: #ebf3eb;\n  color: #000000;\n}\n</style>\n<table id=\"T_9856c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_9856c_level0_col0\" class=\"col_heading level0 col0\" >Itemname</th>\n      <th id=\"T_9856c_level0_col1\" class=\"col_heading level0 col1\" >Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_9856c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n      <td id=\"T_9856c_row0_col0\" class=\"data row0 col0\" >REGENCY CAKESTAND 3 TIER</td>\n      <td id=\"T_9856c_row0_col1\" class=\"data row0 col1\" >24653.67</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n      <td id=\"T_9856c_row1_col0\" class=\"data row1 col0\" >PARTY BUNTING</td>\n      <td id=\"T_9856c_row1_col1\" class=\"data row1 col1\" >9416.13</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n      <td id=\"T_9856c_row2_col0\" class=\"data row2 col0\" >SET OF 3 CAKE TINS PANTRY DESIGN</td>\n      <td id=\"T_9856c_row2_col1\" class=\"data row2 col1\" >7621.05</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n      <td id=\"T_9856c_row3_col0\" class=\"data row3 col0\" >CREAM SWEETHEART MINI CHEST</td>\n      <td id=\"T_9856c_row3_col1\" class=\"data row3 col1\" >6836.38</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n      <td id=\"T_9856c_row4_col0\" class=\"data row4 col0\" >SET/4 WHITE RETRO STORAGE CUBES</td>\n      <td id=\"T_9856c_row4_col1\" class=\"data row4 col1\" >6714.75</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n      <td id=\"T_9856c_row5_col0\" class=\"data row5 col0\" >ENAMEL BREAD BIN CREAM</td>\n      <td id=\"T_9856c_row5_col1\" class=\"data row5 col1\" >6585.93</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n      <td id=\"T_9856c_row6_col0\" class=\"data row6 col0\" >WHITE HANGING HEART T-LIGHT HOLDER</td>\n      <td id=\"T_9856c_row6_col1\" class=\"data row6 col1\" >6563.80</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n      <td id=\"T_9856c_row7_col0\" class=\"data row7 col0\" >DOORMAT KEEP CALM AND COME IN</td>\n      <td id=\"T_9856c_row7_col1\" class=\"data row7 col1\" >6385.09</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n      <td id=\"T_9856c_row8_col0\" class=\"data row8 col0\" >SPOTTY BUNTING</td>\n      <td id=\"T_9856c_row8_col1\" class=\"data row8 col1\" >6262.40</td>\n    </tr>\n    <tr>\n      <th id=\"T_9856c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n      <td id=\"T_9856c_row9_col0\" class=\"data row9 col0\" >RED RETROSPOT CAKE STAND</td>\n      <td id=\"T_9856c_row9_col1\" class=\"data row9 col1\" >6035.29</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"> ### Top 10 most purchased items","metadata":{}},{"cell_type":"code","source":"df[['Itemname','Quantity']].sort_values(by='Quantity',ascending=False)[:10].style.background_gradient(cmap=cm).set_precision(2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:11.749420Z","iopub.execute_input":"2024-07-03T17:01:11.749727Z","iopub.status.idle":"2024-07-03T17:01:11.825094Z","shell.execute_reply.started":"2024-07-03T17:01:11.749701Z","shell.execute_reply":"2024-07-03T17:01:11.824145Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7cd0c8118130>","text/html":"<style type=\"text/css\">\n#T_8bfe1_row0_col1 {\n  background-color: #008000;\n  color: #f1f1f1;\n}\n#T_8bfe1_row1_col1 {\n  background-color: #148a14;\n  color: #f1f1f1;\n}\n#T_8bfe1_row2_col1 {\n  background-color: #e5f0e5;\n  color: #000000;\n}\n#T_8bfe1_row3_col1 {\n  background-color: #e7f1e7;\n  color: #000000;\n}\n#T_8bfe1_row4_col1 {\n  background-color: #e8f2e8;\n  color: #000000;\n}\n#T_8bfe1_row5_col1 {\n  background-color: #eaf2ea;\n  color: #000000;\n}\n#T_8bfe1_row6_col1, #T_8bfe1_row7_col1, #T_8bfe1_row8_col1, #T_8bfe1_row9_col1 {\n  background-color: #ebf3eb;\n  color: #000000;\n}\n</style>\n<table id=\"T_8bfe1\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_8bfe1_level0_col0\" class=\"col_heading level0 col0\" >Itemname</th>\n      <th id=\"T_8bfe1_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_8bfe1_level0_row0\" class=\"row_heading level0 row0\" >520583</th>\n      <td id=\"T_8bfe1_row0_col0\" class=\"data row0 col0\" >PAPER CRAFT , LITTLE BIRDIE</td>\n      <td id=\"T_8bfe1_row0_col1\" class=\"data row0 col1\" >80995</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row1\" class=\"row_heading level0 row1\" >59999</th>\n      <td id=\"T_8bfe1_row1_col0\" class=\"data row1 col0\" >MEDIUM CERAMIC TOP STORAGE JAR</td>\n      <td id=\"T_8bfe1_row1_col1\" class=\"data row1 col1\" >74215</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row2\" class=\"row_heading level0 row2\" >405138</th>\n      <td id=\"T_8bfe1_row2_col0\" class=\"data row2 col0\" >WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n      <td id=\"T_8bfe1_row2_col1\" class=\"data row2 col1\" >4800</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row3\" class=\"row_heading level0 row3\" >198929</th>\n      <td id=\"T_8bfe1_row3_col0\" class=\"data row3 col0\" >SMALL POPCORN HOLDER</td>\n      <td id=\"T_8bfe1_row3_col1\" class=\"data row3 col1\" >4300</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row4\" class=\"row_heading level0 row4\" >94245</th>\n      <td id=\"T_8bfe1_row4_col0\" class=\"data row4 col0\" >EMPIRE DESIGN ROSETTE</td>\n      <td id=\"T_8bfe1_row4_col1\" class=\"data row4 col1\" >3906</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row5\" class=\"row_heading level0 row5\" >260928</th>\n      <td id=\"T_8bfe1_row5_col0\" class=\"data row5 col0\" >ESSENTIAL BALM 3.5g TIN IN ENVELOPE</td>\n      <td id=\"T_8bfe1_row5_col1\" class=\"data row5 col1\" >3186</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row6\" class=\"row_heading level0 row6\" >51228</th>\n      <td id=\"T_8bfe1_row6_col0\" class=\"data row6 col0\" >FAIRY CAKE FLANNEL ASSORTED COLOUR</td>\n      <td id=\"T_8bfe1_row6_col1\" class=\"data row6 col1\" >3114</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row7\" class=\"row_heading level0 row7\" >154834</th>\n      <td id=\"T_8bfe1_row7_col0\" class=\"data row7 col0\" >FAIRY CAKE FLANNEL ASSORTED COLOUR</td>\n      <td id=\"T_8bfe1_row7_col1\" class=\"data row7 col1\" >3114</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row8\" class=\"row_heading level0 row8\" >416997</th>\n      <td id=\"T_8bfe1_row8_col0\" class=\"data row8 col0\" >SMALL CHINESE STYLE SCISSOR</td>\n      <td id=\"T_8bfe1_row8_col1\" class=\"data row8 col1\" >3000</td>\n    </tr>\n    <tr>\n      <th id=\"T_8bfe1_level0_row9\" class=\"row_heading level0 row9\" >280572</th>\n      <td id=\"T_8bfe1_row9_col0\" class=\"data row9 col0\" >ASSORTED COLOUR BIRD ORNAMENT</td>\n      <td id=\"T_8bfe1_row9_col1\" class=\"data row9 col1\" >2880</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"> ### Top 10 most frequently purchased items ","metadata":{}},{"cell_type":"code","source":"# Define custom colors (up to 10 colors)\ncustom_colors = [\n    '#435334', '#9EB384', '#D4E157', '#FF7043', '#26A69A', \n    '#AB47BC', '#42A5F5', '#7E57C2', '#FFCA28', '#66BB6A'\n]\n\n# Plotting the bar chart with custom colors\ndf['Itemname'].value_counts().head(10).plot.bar(color=custom_colors, figsize=(6, 3))\n\nplt.xticks(rotation=90, fontsize=8)\nplt.grid()\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:11.826469Z","iopub.execute_input":"2024-07-03T17:01:11.826794Z","iopub.status.idle":"2024-07-03T17:01:12.119543Z","shell.execute_reply.started":"2024-07-03T17:01:11.826767Z","shell.execute_reply":"2024-07-03T17:01:12.118576Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 600x300 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"<a id=\"section-three\"></a>\n## <b><span style='color:#f0bc22'> 3. |</span> Market Basket Analysis","metadata":{}},{"cell_type":"markdown","source":"Since the UK is the most purchased country, let insight into the item combination purchased in the UK.","metadata":{}},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 3-1. |</span> Implementing Apriori","metadata":{}},{"cell_type":"code","source":"from mlxtend.frequent_patterns import apriori\nfrom mlxtend.frequent_patterns import association_rules","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:12.121053Z","iopub.execute_input":"2024-07-03T17:01:12.121465Z","iopub.status.idle":"2024-07-03T17:01:12.138196Z","shell.execute_reply.started":"2024-07-03T17:01:12.121428Z","shell.execute_reply":"2024-07-03T17:01:12.137304Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"df['Itemname']=df['Itemname'].str.strip()\ndf['BillNo']=df['BillNo'].astype('str')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:12.139328Z","iopub.execute_input":"2024-07-03T17:01:12.139634Z","iopub.status.idle":"2024-07-03T17:01:12.422189Z","shell.execute_reply.started":"2024-07-03T17:01:12.139608Z","shell.execute_reply":"2024-07-03T17:01:12.421307Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"basket=(df[df['Country']=='United Kingdom']\n        .groupby(['BillNo','Itemname'])['Quantity']\n        .sum().unstack().reset_index().fillna(0)\n        .set_index('BillNo'))","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:12.423402Z","iopub.execute_input":"2024-07-03T17:01:12.423730Z","iopub.status.idle":"2024-07-03T17:01:14.166316Z","shell.execute_reply.started":"2024-07-03T17:01:12.423703Z","shell.execute_reply":"2024-07-03T17:01:14.165380Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"basket.head(3)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:14.167445Z","iopub.execute_input":"2024-07-03T17:01:14.167726Z","iopub.status.idle":"2024-07-03T17:01:14.196184Z","shell.execute_reply.started":"2024-07-03T17:01:14.167703Z","shell.execute_reply":"2024-07-03T17:01:14.195202Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Itemname  10 COLOUR SPACEBOY PEN  12 COLOURED PARTY BALLOONS  \\\nBillNo                                                         \n539993                       0.0                         0.0   \n540001                       0.0                         0.0   \n540002                       0.0                         0.0   \n\nItemname  12 DAISY PEGS IN WOOD BOX  12 EGG HOUSE PAINTED WOOD  \\\nBillNo                                                           \n539993                          0.0                        0.0   \n540001                          0.0                        0.0   \n540002                          0.0                        0.0   \n\nItemname  12 HANGING EGGS HAND PAINTED  12 IVORY ROSE PEG PLACE SETTINGS  \\\nBillNo                                                                     \n539993                             0.0                               0.0   \n540001                             0.0                               0.0   \n540002                             0.0                               0.0   \n\nItemname  12 MESSAGE CARDS WITH ENVELOPES  12 PENCIL SMALL TUBE WOODLAND  \\\nBillNo                                                                     \n539993                                0.0                            0.0   \n540001                                0.0                            0.0   \n540002                                0.0                            0.0   \n\nItemname  12 PENCILS SMALL TUBE RED RETROSPOT  12 PENCILS SMALL TUBE SKULL  \\\nBillNo                                                                       \n539993                                    0.0                          0.0   \n540001                                    0.0                          0.0   \n540002                                    0.0                          0.0   \n\nItemname  ...  ZINC STAR T-LIGHT HOLDER  ZINC SWEETHEART SOAP DISH  \\\nBillNo    ...                                                        \n539993    ...                       0.0                        0.0   \n540001    ...                       0.0                        0.0   \n540002    ...                       0.0                        0.0   \n\nItemname  ZINC SWEETHEART WIRE LETTER RACK  ZINC T-LIGHT HOLDER STAR LARGE  \\\nBillNo                                                                       \n539993                                 0.0                             0.0   \n540001                                 0.0                             0.0   \n540002                                 0.0                             0.0   \n\nItemname  ZINC T-LIGHT HOLDER STARS LARGE  ZINC T-LIGHT HOLDER STARS SMALL  \\\nBillNo                                                                       \n539993                                0.0                              0.0   \n540001                                0.0                              0.0   \n540002                                0.0                              0.0   \n\nItemname  ZINC TOP  2 DOOR WOODEN SHELF  ZINC WILLIE WINKIE  CANDLE STICK  \\\nBillNo                                                                      \n539993                              0.0                               0.0   \n540001                              0.0                               0.0   \n540002                              0.0                               0.0   \n\nItemname  ZINC WIRE KITCHEN ORGANISER  ZINC WIRE SWEETHEART LETTER TRAY  \nBillNo                                                                   \n539993                            0.0                               0.0  \n540001                            0.0                               0.0  \n540002                            0.0                               0.0  \n\n[3 rows x 3893 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Itemname</th>\n      <th>10 COLOUR SPACEBOY PEN</th>\n      <th>12 COLOURED PARTY BALLOONS</th>\n      <th>12 DAISY PEGS IN WOOD BOX</th>\n      <th>12 EGG HOUSE PAINTED WOOD</th>\n      <th>12 HANGING EGGS HAND PAINTED</th>\n      <th>12 IVORY ROSE PEG PLACE SETTINGS</th>\n      <th>12 MESSAGE CARDS WITH ENVELOPES</th>\n      <th>12 PENCIL SMALL TUBE WOODLAND</th>\n      <th>12 PENCILS SMALL TUBE RED RETROSPOT</th>\n      <th>12 PENCILS SMALL TUBE SKULL</th>\n      <th>...</th>\n      <th>ZINC STAR T-LIGHT HOLDER</th>\n      <th>ZINC SWEETHEART SOAP DISH</th>\n      <th>ZINC SWEETHEART WIRE LETTER RACK</th>\n      <th>ZINC T-LIGHT HOLDER STAR LARGE</th>\n      <th>ZINC T-LIGHT HOLDER STARS LARGE</th>\n      <th>ZINC T-LIGHT HOLDER STARS SMALL</th>\n      <th>ZINC TOP  2 DOOR WOODEN SHELF</th>\n      <th>ZINC WILLIE WINKIE  CANDLE STICK</th>\n      <th>ZINC WIRE KITCHEN ORGANISER</th>\n      <th>ZINC WIRE SWEETHEART LETTER TRAY</th>\n    </tr>\n    <tr>\n      <th>BillNo</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>539993</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>540001</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>540002</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3 rows × 3893 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"def encode_units(x):\n    if x<=0: \n        return 0\n    if x>=1:\n        return 1","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:14.197454Z","iopub.execute_input":"2024-07-03T17:01:14.197772Z","iopub.status.idle":"2024-07-03T17:01:14.205737Z","shell.execute_reply.started":"2024-07-03T17:01:14.197745Z","shell.execute_reply":"2024-07-03T17:01:14.204743Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"basket_sets=basket.applymap(encode_units)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:14.210551Z","iopub.execute_input":"2024-07-03T17:01:14.210993Z","iopub.status.idle":"2024-07-03T17:01:41.722976Z","shell.execute_reply.started":"2024-07-03T17:01:14.210957Z","shell.execute_reply":"2024-07-03T17:01:41.722080Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"frequent_itemsets=apriori(basket_sets,min_support=0.03,use_colnames=True)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:41.724102Z","iopub.execute_input":"2024-07-03T17:01:41.724422Z","iopub.status.idle":"2024-07-03T17:01:43.828651Z","shell.execute_reply.started":"2024-07-03T17:01:41.724396Z","shell.execute_reply":"2024-07-03T17:01:43.827473Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning:\n\nDataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n\n","output_type":"stream"}]},{"cell_type":"code","source":"frequent_itemsets","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:12:28.344744Z","iopub.execute_input":"2024-07-03T17:12:28.346040Z","iopub.status.idle":"2024-07-03T17:12:28.364791Z","shell.execute_reply.started":"2024-07-03T17:12:28.345988Z","shell.execute_reply":"2024-07-03T17:12:28.363437Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"      support                                           itemsets\n0    0.047772                           (6 RIBBONS RUSTIC CHARM)\n1    0.030715                  (60 CAKE CASES VINTAGE CHRISTMAS)\n2    0.042066                      (60 TEATIME FAIRY CAKE CASES)\n3    0.031079                   (72 SWEETHEART FAIRY CAKE CASES)\n4    0.047529                       (ALARM CLOCK BAKELIKE GREEN)\n..        ...                                                ...\n144  0.034964  (LUNCH BAG  BLACK SKULL., LUNCH BAG RED RETROS...\n145  0.030290   (LUNCH BAG SUKI DESIGN, LUNCH BAG  BLACK SKULL.)\n146  0.032233  (LUNCH BAG PINK POLKADOT, LUNCH BAG RED RETROS...\n147  0.031322   (LUNCH BAG SUKI DESIGN, LUNCH BAG RED RETROSPOT)\n148  0.032961  (PINK REGENCY TEACUP AND SAUCER, ROSES REGENCY...\n\n[149 rows x 2 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>support</th>\n      <th>itemsets</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.047772</td>\n      <td>(6 RIBBONS RUSTIC CHARM)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.030715</td>\n      <td>(60 CAKE CASES VINTAGE CHRISTMAS)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.042066</td>\n      <td>(60 TEATIME FAIRY CAKE CASES)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.031079</td>\n      <td>(72 SWEETHEART FAIRY CAKE CASES)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.047529</td>\n      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>0.034964</td>\n      <td>(LUNCH BAG  BLACK SKULL., LUNCH BAG RED RETROS...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>0.030290</td>\n      <td>(LUNCH BAG SUKI DESIGN, LUNCH BAG  BLACK SKULL.)</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>0.032233</td>\n      <td>(LUNCH BAG PINK POLKADOT, LUNCH BAG RED RETROS...</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>0.031322</td>\n      <td>(LUNCH BAG SUKI DESIGN, LUNCH BAG RED RETROSPOT)</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>0.032961</td>\n      <td>(PINK REGENCY TEACUP AND SAUCER, ROSES REGENCY...</td>\n    </tr>\n  </tbody>\n</table>\n<p>149 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"rules=round(association_rules(frequent_itemsets,metric='lift',min_threshold=1),2)\nrules.head(5)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:43.830199Z","iopub.execute_input":"2024-07-03T17:01:43.830690Z","iopub.status.idle":"2024-07-03T17:01:43.867045Z","shell.execute_reply.started":"2024-07-03T17:01:43.830653Z","shell.execute_reply":"2024-07-03T17:01:43.865695Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"                           antecedents                          consequents  \\\n0         (ALARM CLOCK BAKELIKE GREEN)           (ALARM CLOCK BAKELIKE RED)   \n1           (ALARM CLOCK BAKELIKE RED)         (ALARM CLOCK BAKELIKE GREEN)   \n2  (GARDENERS KNEELING PAD CUP OF TEA)   (GARDENERS KNEELING PAD KEEP CALM)   \n3   (GARDENERS KNEELING PAD KEEP CALM)  (GARDENERS KNEELING PAD CUP OF TEA)   \n4    (GREEN REGENCY TEACUP AND SAUCER)     (PINK REGENCY TEACUP AND SAUCER)   \n\n   antecedent support  consequent support  support  confidence   lift  \\\n0                0.05                0.05     0.03        0.64  12.41   \n1                0.05                0.05     0.03        0.59  12.41   \n2                0.05                0.05     0.03        0.72  13.23   \n3                0.05                0.05     0.03        0.60  13.23   \n4                0.05                0.04     0.03        0.66  15.50   \n\n   leverage  conviction  zhangs_metric  \n0      0.03        2.64           0.97  \n1      0.03        2.32           0.97  \n2      0.03        3.39           0.97  \n3      0.03        2.40           0.98  \n4      0.03        2.80           0.99  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>antecedents</th>\n      <th>consequents</th>\n      <th>antecedent support</th>\n      <th>consequent support</th>\n      <th>support</th>\n      <th>confidence</th>\n      <th>lift</th>\n      <th>leverage</th>\n      <th>conviction</th>\n      <th>zhangs_metric</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n      <td>(ALARM CLOCK BAKELIKE RED)</td>\n      <td>0.05</td>\n      <td>0.05</td>\n      <td>0.03</td>\n      <td>0.64</td>\n      <td>12.41</td>\n      <td>0.03</td>\n      <td>2.64</td>\n      <td>0.97</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(ALARM CLOCK BAKELIKE RED)</td>\n      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n      <td>0.05</td>\n      <td>0.05</td>\n      <td>0.03</td>\n      <td>0.59</td>\n      <td>12.41</td>\n      <td>0.03</td>\n      <td>2.32</td>\n      <td>0.97</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(GARDENERS KNEELING PAD CUP OF TEA)</td>\n      <td>(GARDENERS KNEELING PAD KEEP CALM)</td>\n      <td>0.05</td>\n      <td>0.05</td>\n      <td>0.03</td>\n      <td>0.72</td>\n      <td>13.23</td>\n      <td>0.03</td>\n      <td>3.39</td>\n      <td>0.97</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(GARDENERS KNEELING PAD KEEP CALM)</td>\n      <td>(GARDENERS KNEELING PAD CUP OF TEA)</td>\n      <td>0.05</td>\n      <td>0.05</td>\n      <td>0.03</td>\n      <td>0.60</td>\n      <td>13.23</td>\n      <td>0.03</td>\n      <td>2.40</td>\n      <td>0.98</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>(GREEN REGENCY TEACUP AND SAUCER)</td>\n      <td>(PINK REGENCY TEACUP AND SAUCER)</td>\n      <td>0.05</td>\n      <td>0.04</td>\n      <td>0.03</td>\n      <td>0.66</td>\n      <td>15.50</td>\n      <td>0.03</td>\n      <td>2.80</td>\n      <td>0.99</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(6,6))\nplt.subplot(221)\nsns.scatterplot(x=\"support\",y=\"confidence\",data=rules,hue=\"lift\",palette=\"viridis\")\nplt.subplot(222)\nsns.scatterplot(x=\"support\",y=\"lift\",data=rules,hue=\"confidence\",palette=\"viridis\")\nplt.subplot(223)\nsns.scatterplot(x=\"confidence\",y=\"lift\",data=rules,hue='support',palette=\"viridis\")\nplt.subplot(224)\nsns.scatterplot(x=\"antecedent support\",y=\"consequent support\",data=rules,hue='confidence',palette=\"viridis\")\nplt.tight_layout()\nplt.show()","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:43.870388Z","iopub.execute_input":"2024-07-03T17:01:43.871317Z","iopub.status.idle":"2024-07-03T17:01:45.204256Z","shell.execute_reply.started":"2024-07-03T17:01:43.871279Z","shell.execute_reply":"2024-07-03T17:01:45.203198Z"},"trusted":true},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 600x600 with 4 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 3-2. |</span> The top 5 of the highest **`support`** value of items(antecedents)","metadata":{}},{"cell_type":"markdown","source":"> #### *Support(item) = Transactions comprising the item / Total transactions*","metadata":{}},{"cell_type":"code","source":"rules[['antecedents','consequents','support']].sort_values('support',ascending=False)[:5].style.background_gradient(cmap=cm).set_precision(2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:45.205510Z","iopub.execute_input":"2024-07-03T17:01:45.205823Z","iopub.status.idle":"2024-07-03T17:01:45.222201Z","shell.execute_reply.started":"2024-07-03T17:01:45.205794Z","shell.execute_reply":"2024-07-03T17:01:45.221223Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7cd0c7e3f2e0>","text/html":"<style type=\"text/css\">\n#T_33127_row0_col2, #T_33127_row1_col2 {\n  background-color: #008000;\n  color: #f1f1f1;\n}\n#T_33127_row2_col2, #T_33127_row3_col2, #T_33127_row4_col2 {\n  background-color: #ebf3eb;\n  color: #000000;\n}\n</style>\n<table id=\"T_33127\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_33127_level0_col0\" class=\"col_heading level0 col0\" >antecedents</th>\n      <th id=\"T_33127_level0_col1\" class=\"col_heading level0 col1\" >consequents</th>\n      <th id=\"T_33127_level0_col2\" class=\"col_heading level0 col2\" >support</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_33127_level0_row0\" class=\"row_heading level0 row0\" >13</th>\n      <td id=\"T_33127_row0_col0\" class=\"data row0 col0\" >frozenset({'JUMBO BAG PINK POLKADOT'})</td>\n      <td id=\"T_33127_row0_col1\" class=\"data row0 col1\" >frozenset({'JUMBO BAG RED RETROSPOT'})</td>\n      <td id=\"T_33127_row0_col2\" class=\"data row0 col2\" >0.05</td>\n    </tr>\n    <tr>\n      <th id=\"T_33127_level0_row1\" class=\"row_heading level0 row1\" >12</th>\n      <td id=\"T_33127_row1_col0\" class=\"data row1 col0\" >frozenset({'JUMBO BAG RED RETROSPOT'})</td>\n      <td id=\"T_33127_row1_col1\" class=\"data row1 col1\" >frozenset({'JUMBO BAG PINK POLKADOT'})</td>\n      <td id=\"T_33127_row1_col2\" class=\"data row1 col2\" >0.05</td>\n    </tr>\n    <tr>\n      <th id=\"T_33127_level0_row2\" class=\"row_heading level0 row2\" >16</th>\n      <td id=\"T_33127_row2_col0\" class=\"data row2 col0\" >frozenset({'JUMBO BAG RED RETROSPOT'})</td>\n      <td id=\"T_33127_row2_col1\" class=\"data row2 col1\" >frozenset({'JUMBO STORAGE BAG SUKI'})</td>\n      <td id=\"T_33127_row2_col2\" class=\"data row2 col2\" >0.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_33127_level0_row3\" class=\"row_heading level0 row3\" >17</th>\n      <td id=\"T_33127_row3_col0\" class=\"data row3 col0\" >frozenset({'JUMBO STORAGE BAG SUKI'})</td>\n      <td id=\"T_33127_row3_col1\" class=\"data row3 col1\" >frozenset({'JUMBO BAG RED RETROSPOT'})</td>\n      <td id=\"T_33127_row3_col2\" class=\"data row3 col2\" >0.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_33127_level0_row4\" class=\"row_heading level0 row4\" >15</th>\n      <td id=\"T_33127_row4_col0\" class=\"data row4 col0\" >frozenset({'JUMBO SHOPPER VINTAGE RED PAISLEY'})</td>\n      <td id=\"T_33127_row4_col1\" class=\"data row4 col1\" >frozenset({'JUMBO BAG RED RETROSPOT'})</td>\n      <td id=\"T_33127_row4_col2\" class=\"data row4 col2\" >0.04</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"In the top support value of purchase, it means that \"JUMBO BAG PINK RETROSPOT\" is present in 5% of all purchases.","metadata":{}},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 3-3. |</span> The top 5 of the highest **`confidence`** value of items","metadata":{}},{"cell_type":"markdown","source":"> #### *Confidence = Transactions comprising antecedent and consequent / Transactions comprising antecedent*","metadata":{}},{"cell_type":"code","source":"rules[['antecedents','consequents','confidence']].sort_values('confidence',ascending=False)[:5].style.background_gradient(cmap=cm).set_precision(2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:45.223527Z","iopub.execute_input":"2024-07-03T17:01:45.223924Z","iopub.status.idle":"2024-07-03T17:01:45.240089Z","shell.execute_reply.started":"2024-07-03T17:01:45.223888Z","shell.execute_reply":"2024-07-03T17:01:45.238942Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7cd0c7e3f400>","text/html":"<style type=\"text/css\">\n#T_547e0_row0_col2 {\n  background-color: #008000;\n  color: #f1f1f1;\n}\n#T_547e0_row1_col2 {\n  background-color: #5eae5e;\n  color: #f1f1f1;\n}\n#T_547e0_row2_col2 {\n  background-color: #a5d1a5;\n  color: #000000;\n}\n#T_547e0_row3_col2 {\n  background-color: #d4e8d4;\n  color: #000000;\n}\n#T_547e0_row4_col2 {\n  background-color: #ebf3eb;\n  color: #000000;\n}\n</style>\n<table id=\"T_547e0\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_547e0_level0_col0\" class=\"col_heading level0 col0\" >antecedents</th>\n      <th id=\"T_547e0_level0_col1\" class=\"col_heading level0 col1\" >consequents</th>\n      <th id=\"T_547e0_level0_col2\" class=\"col_heading level0 col2\" >confidence</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_547e0_level0_row0\" class=\"row_heading level0 row0\" >5</th>\n      <td id=\"T_547e0_row0_col0\" class=\"data row0 col0\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row0_col1\" class=\"data row0 col1\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row0_col2\" class=\"data row0 col2\" >0.82</td>\n    </tr>\n    <tr>\n      <th id=\"T_547e0_level0_row1\" class=\"row_heading level0 row1\" >30</th>\n      <td id=\"T_547e0_row1_col0\" class=\"data row1 col0\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row1_col1\" class=\"data row1 col1\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row1_col2\" class=\"data row1 col2\" >0.78</td>\n    </tr>\n    <tr>\n      <th id=\"T_547e0_level0_row2\" class=\"row_heading level0 row2\" >6</th>\n      <td id=\"T_547e0_row2_col0\" class=\"data row2 col0\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row2_col1\" class=\"data row2 col1\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row2_col2\" class=\"data row2 col2\" >0.75</td>\n    </tr>\n    <tr>\n      <th id=\"T_547e0_level0_row3\" class=\"row_heading level0 row3\" >7</th>\n      <td id=\"T_547e0_row3_col0\" class=\"data row3 col0\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row3_col1\" class=\"data row3 col1\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_547e0_row3_col2\" class=\"data row3 col2\" >0.73</td>\n    </tr>\n    <tr>\n      <th id=\"T_547e0_level0_row4\" class=\"row_heading level0 row4\" >2</th>\n      <td id=\"T_547e0_row4_col0\" class=\"data row4 col0\" >frozenset({'GARDENERS KNEELING PAD CUP OF TEA'})</td>\n      <td id=\"T_547e0_row4_col1\" class=\"data row4 col1\" >frozenset({'GARDENERS KNEELING PAD KEEP CALM'})</td>\n      <td id=\"T_547e0_row4_col2\" class=\"data row4 col2\" >0.72</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"In the top confidence value of the purchase, it means that 82% of the customers who bought \"PINK REGENCY TEACUP AND SAUCER\" also bought \"GREEN REGENCY TEACUP AND SAUCER\".","metadata":{}},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 3-4. |</span> The top 5 of the highest **`lift`** value of items","metadata":{}},{"cell_type":"markdown","source":"> #### *Lift = Confidence (antecedent -> consequent) / Support(antecedent)*","metadata":{}},{"cell_type":"code","source":"rules[['antecedents','consequents','lift']].sort_values('lift',ascending=False)[:5].style.background_gradient(cmap=cm).set_precision(2)","metadata":{"execution":{"iopub.status.busy":"2024-07-03T17:01:45.241384Z","iopub.execute_input":"2024-07-03T17:01:45.241714Z","iopub.status.idle":"2024-07-03T17:01:45.259479Z","shell.execute_reply.started":"2024-07-03T17:01:45.241687Z","shell.execute_reply":"2024-07-03T17:01:45.258348Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7cd0c7e3c460>","text/html":"<style type=\"text/css\">\n#T_3244d_row0_col2, #T_3244d_row1_col2 {\n  background-color: #008000;\n  color: #f1f1f1;\n}\n#T_3244d_row2_col2, #T_3244d_row3_col2 {\n  background-color: #a3d0a3;\n  color: #000000;\n}\n#T_3244d_row4_col2 {\n  background-color: #ebf3eb;\n  color: #000000;\n}\n</style>\n<table id=\"T_3244d\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_3244d_level0_col0\" class=\"col_heading level0 col0\" >antecedents</th>\n      <th id=\"T_3244d_level0_col1\" class=\"col_heading level0 col1\" >consequents</th>\n      <th id=\"T_3244d_level0_col2\" class=\"col_heading level0 col2\" >lift</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_3244d_level0_row0\" class=\"row_heading level0 row0\" >4</th>\n      <td id=\"T_3244d_row0_col0\" class=\"data row0 col0\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row0_col1\" class=\"data row0 col1\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row0_col2\" class=\"data row0 col2\" >15.50</td>\n    </tr>\n    <tr>\n      <th id=\"T_3244d_level0_row1\" class=\"row_heading level0 row1\" >5</th>\n      <td id=\"T_3244d_row1_col0\" class=\"data row1 col0\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row1_col1\" class=\"data row1 col1\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row1_col2\" class=\"data row1 col2\" >15.50</td>\n    </tr>\n    <tr>\n      <th id=\"T_3244d_level0_row2\" class=\"row_heading level0 row2\" >31</th>\n      <td id=\"T_3244d_row2_col0\" class=\"data row2 col0\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row2_col1\" class=\"data row2 col1\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row2_col2\" class=\"data row2 col2\" >14.36</td>\n    </tr>\n    <tr>\n      <th id=\"T_3244d_level0_row3\" class=\"row_heading level0 row3\" >30</th>\n      <td id=\"T_3244d_row3_col0\" class=\"data row3 col0\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row3_col1\" class=\"data row3 col1\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row3_col2\" class=\"data row3 col2\" >14.36</td>\n    </tr>\n    <tr>\n      <th id=\"T_3244d_level0_row4\" class=\"row_heading level0 row4\" >6</th>\n      <td id=\"T_3244d_row4_col0\" class=\"data row4 col0\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row4_col1\" class=\"data row4 col1\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_3244d_row4_col2\" class=\"data row4 col2\" >13.86</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"In the top list value of the purchase, it means that customers are 15.5 times more likely to buy \"GREEN REGENCY TEACUP AND SAUCER\" if you sell \"PINK REGENCY TEACUP AND SAUCER\".","metadata":{}},{"cell_type":"markdown","source":"### <b><span style='color:#f0bc22'> 3-5. |</span> The best combination of the items","metadata":{}},{"cell_type":"code","source":"rules[(rules['lift']>=13)&(rules['confidence']>=0.7)].sort_values('lift',ascending=False).style.background_gradient(cmap=cm).set_precision(2)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-07-03T17:01:45.260797Z","iopub.execute_input":"2024-07-03T17:01:45.261148Z","iopub.status.idle":"2024-07-03T17:01:45.283151Z","shell.execute_reply.started":"2024-07-03T17:01:45.261121Z","shell.execute_reply":"2024-07-03T17:01:45.282178Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"<pandas.io.formats.style.Styler at 0x7cd0c7e3c040>","text/html":"<style type=\"text/css\">\n#T_01f82_row0_col2, #T_01f82_row0_col3, #T_01f82_row0_col4, #T_01f82_row0_col7, #T_01f82_row1_col2, #T_01f82_row1_col3, #T_01f82_row1_col4, #T_01f82_row1_col7, #T_01f82_row1_col9, #T_01f82_row2_col3, #T_01f82_row3_col3, #T_01f82_row4_col3, #T_01f82_row4_col4, #T_01f82_row4_col5, #T_01f82_row4_col6, #T_01f82_row4_col7, #T_01f82_row4_col8, #T_01f82_row4_col9 {\n  background-color: #ebf3eb;\n  color: #000000;\n}\n#T_01f82_row0_col5, #T_01f82_row0_col6, #T_01f82_row0_col8, #T_01f82_row0_col9, #T_01f82_row2_col2, #T_01f82_row2_col4, #T_01f82_row2_col7, #T_01f82_row2_col9, #T_01f82_row3_col2, #T_01f82_row3_col4, #T_01f82_row3_col7, #T_01f82_row3_col9, #T_01f82_row4_col2 {\n  background-color: #008000;\n  color: #f1f1f1;\n}\n#T_01f82_row1_col5 {\n  background-color: #5eae5e;\n  color: #f1f1f1;\n}\n#T_01f82_row1_col6 {\n  background-color: #76ba76;\n  color: #f1f1f1;\n}\n#T_01f82_row1_col8 {\n  background-color: #80bf80;\n  color: #000000;\n}\n#T_01f82_row2_col5 {\n  background-color: #a5d1a5;\n  color: #000000;\n}\n#T_01f82_row2_col6, #T_01f82_row3_col6 {\n  background-color: #a9d3a9;\n  color: #000000;\n}\n#T_01f82_row2_col8 {\n  background-color: #badbba;\n  color: #000000;\n}\n#T_01f82_row3_col5 {\n  background-color: #d4e8d4;\n  color: #000000;\n}\n#T_01f82_row3_col8 {\n  background-color: #d6e9d6;\n  color: #000000;\n}\n</style>\n<table id=\"T_01f82\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_01f82_level0_col0\" class=\"col_heading level0 col0\" >antecedents</th>\n      <th id=\"T_01f82_level0_col1\" class=\"col_heading level0 col1\" >consequents</th>\n      <th id=\"T_01f82_level0_col2\" class=\"col_heading level0 col2\" >antecedent support</th>\n      <th id=\"T_01f82_level0_col3\" class=\"col_heading level0 col3\" >consequent support</th>\n      <th id=\"T_01f82_level0_col4\" class=\"col_heading level0 col4\" >support</th>\n      <th id=\"T_01f82_level0_col5\" class=\"col_heading level0 col5\" >confidence</th>\n      <th id=\"T_01f82_level0_col6\" class=\"col_heading level0 col6\" >lift</th>\n      <th id=\"T_01f82_level0_col7\" class=\"col_heading level0 col7\" >leverage</th>\n      <th id=\"T_01f82_level0_col8\" class=\"col_heading level0 col8\" >conviction</th>\n      <th id=\"T_01f82_level0_col9\" class=\"col_heading level0 col9\" >zhangs_metric</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_01f82_level0_row0\" class=\"row_heading level0 row0\" >5</th>\n      <td id=\"T_01f82_row0_col0\" class=\"data row0 col0\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row0_col1\" class=\"data row0 col1\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row0_col2\" class=\"data row0 col2\" >0.04</td>\n      <td id=\"T_01f82_row0_col3\" class=\"data row0 col3\" >0.05</td>\n      <td id=\"T_01f82_row0_col4\" class=\"data row0 col4\" >0.03</td>\n      <td id=\"T_01f82_row0_col5\" class=\"data row0 col5\" >0.82</td>\n      <td id=\"T_01f82_row0_col6\" class=\"data row0 col6\" >15.50</td>\n      <td id=\"T_01f82_row0_col7\" class=\"data row0 col7\" >0.03</td>\n      <td id=\"T_01f82_row0_col8\" class=\"data row0 col8\" >5.25</td>\n      <td id=\"T_01f82_row0_col9\" class=\"data row0 col9\" >0.98</td>\n    </tr>\n    <tr>\n      <th id=\"T_01f82_level0_row1\" class=\"row_heading level0 row1\" >30</th>\n      <td id=\"T_01f82_row1_col0\" class=\"data row1 col0\" >frozenset({'PINK REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row1_col1\" class=\"data row1 col1\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row1_col2\" class=\"data row1 col2\" >0.04</td>\n      <td id=\"T_01f82_row1_col3\" class=\"data row1 col3\" >0.05</td>\n      <td id=\"T_01f82_row1_col4\" class=\"data row1 col4\" >0.03</td>\n      <td id=\"T_01f82_row1_col5\" class=\"data row1 col5\" >0.78</td>\n      <td id=\"T_01f82_row1_col6\" class=\"data row1 col6\" >14.36</td>\n      <td id=\"T_01f82_row1_col7\" class=\"data row1 col7\" >0.03</td>\n      <td id=\"T_01f82_row1_col8\" class=\"data row1 col8\" >4.24</td>\n      <td id=\"T_01f82_row1_col9\" class=\"data row1 col9\" >0.97</td>\n    </tr>\n    <tr>\n      <th id=\"T_01f82_level0_row2\" class=\"row_heading level0 row2\" >6</th>\n      <td id=\"T_01f82_row2_col0\" class=\"data row2 col0\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row2_col1\" class=\"data row2 col1\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row2_col2\" class=\"data row2 col2\" >0.05</td>\n      <td id=\"T_01f82_row2_col3\" class=\"data row2 col3\" >0.05</td>\n      <td id=\"T_01f82_row2_col4\" class=\"data row2 col4\" >0.04</td>\n      <td id=\"T_01f82_row2_col5\" class=\"data row2 col5\" >0.75</td>\n      <td id=\"T_01f82_row2_col6\" class=\"data row2 col6\" >13.86</td>\n      <td id=\"T_01f82_row2_col7\" class=\"data row2 col7\" >0.04</td>\n      <td id=\"T_01f82_row2_col8\" class=\"data row2 col8\" >3.78</td>\n      <td id=\"T_01f82_row2_col9\" class=\"data row2 col9\" >0.98</td>\n    </tr>\n    <tr>\n      <th id=\"T_01f82_level0_row3\" class=\"row_heading level0 row3\" >7</th>\n      <td id=\"T_01f82_row3_col0\" class=\"data row3 col0\" >frozenset({'ROSES REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row3_col1\" class=\"data row3 col1\" >frozenset({'GREEN REGENCY TEACUP AND SAUCER'})</td>\n      <td id=\"T_01f82_row3_col2\" class=\"data row3 col2\" >0.05</td>\n      <td id=\"T_01f82_row3_col3\" class=\"data row3 col3\" >0.05</td>\n      <td id=\"T_01f82_row3_col4\" class=\"data row3 col4\" >0.04</td>\n      <td id=\"T_01f82_row3_col5\" class=\"data row3 col5\" >0.73</td>\n      <td id=\"T_01f82_row3_col6\" class=\"data row3 col6\" >13.86</td>\n      <td id=\"T_01f82_row3_col7\" class=\"data row3 col7\" >0.04</td>\n      <td id=\"T_01f82_row3_col8\" class=\"data row3 col8\" >3.55</td>\n      <td id=\"T_01f82_row3_col9\" class=\"data row3 col9\" >0.98</td>\n    </tr>\n    <tr>\n      <th id=\"T_01f82_level0_row4\" class=\"row_heading level0 row4\" >2</th>\n      <td id=\"T_01f82_row4_col0\" class=\"data row4 col0\" >frozenset({'GARDENERS KNEELING PAD CUP OF TEA'})</td>\n      <td id=\"T_01f82_row4_col1\" class=\"data row4 col1\" >frozenset({'GARDENERS KNEELING PAD KEEP CALM'})</td>\n      <td id=\"T_01f82_row4_col2\" class=\"data row4 col2\" >0.05</td>\n      <td id=\"T_01f82_row4_col3\" class=\"data row4 col3\" >0.05</td>\n      <td id=\"T_01f82_row4_col4\" class=\"data row4 col4\" >0.03</td>\n      <td id=\"T_01f82_row4_col5\" class=\"data row4 col5\" >0.72</td>\n      <td id=\"T_01f82_row4_col6\" class=\"data row4 col6\" >13.23</td>\n      <td id=\"T_01f82_row4_col7\" class=\"data row4 col7\" >0.03</td>\n      <td id=\"T_01f82_row4_col8\" class=\"data row4 col8\" >3.39</td>\n      <td id=\"T_01f82_row4_col9\" class=\"data row4 col9\" >0.97</td>\n    </tr>\n  </tbody>\n</table>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"As you can see above, \"REGENCY TEACUP AND SAUCER\" have the best combination of the same items with different colors.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-four\"></a>\n## <b><span style='color:#f0bc22'> 4. |</span> Conclusion","metadata":{}},{"cell_type":"markdown","source":"Here's what we learned from this analysis:\n- The most purchased item is **`PAPER CRAFT, LITTLE BIRDIE`**.\n- The most frequently purchased item is **`WHITE HANGING HEART T-LIGHT HOLDER`**.\n- The best combination items are **`PINK REGENCY TEACUP AND SAUCER`** and **`GREEN REGENCY TEACUP AND SAUCER`**.","metadata":{}},{"cell_type":"markdown","source":"Hence, if you want to let customers purchase more, you can put an advertisement in `REGENCY TEACUP AND SAUCER` or put those items on the top of the page.","metadata":{}},{"cell_type":"markdown","source":"### <center> 📑Thanks for checking my notebook📒 </center>","metadata":{"execution":{"iopub.status.busy":"2023-06-15T11:47:44.287933Z","iopub.execute_input":"2023-06-15T11:47:44.288384Z","iopub.status.idle":"2023-06-15T11:47:44.293615Z","shell.execute_reply.started":"2023-06-15T11:47:44.288353Z","shell.execute_reply":"2023-06-15T11:47:44.292492Z"}}}]}