{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8047690,"sourceType":"datasetVersion","datasetId":4745562}],"dockerImageVersionId":30673,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\n","metadata":{"execution":{"iopub.status.busy":"2024-04-06T11:27:42.830690Z","iopub.execute_input":"2024-04-06T11:27:42.831112Z","iopub.status.idle":"2024-04-06T11:27:42.843464Z","shell.execute_reply.started":"2024-04-06T11:27:42.831081Z","shell.execute_reply":"2024-04-06T11:27:42.841872Z"}}},{"cell_type":"markdown","source":"In data integration, **merging** and **appending** datasets are pivotal processes that streamline the consolidation of information from **disparate sources**. Merging combines datasets **horizontally** based on shared **attributes**, facilitating comprehensive analysis by enriching the data with **complementary information**. Meanwhile, appending **stacks** datasets **vertically**, enabling the aggregation of similar data **records** over time or from various sources. These operations are integral to data science, empowering analysts to create cohesive datasets essential for robust exploratory analysis, feature engineering, and predictive modeling, ultimately driving informed decision-making and actionable insights.  \n  \n  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"}}},{"cell_type":"markdown","source":"## Merging Datasets  \nMerging datasets is a foundational process in data analysis that empowers analysts to integrate and consolidate diverse sources of information into a **unified dataset**. By combining datasets based on **common attributes or keys**, analysts can uncover valuable insights and patterns that may remain hidden within individual datasets. This approach enables a **comprehensive understanding** of complex relationships and correlations across different dimensions of data. Whether merging customer data with sales records or combining demographic information with geographic data, the process of merging datasets facilitates more holistic analyses and informs data-driven decision-making across various domains and industries.","metadata":{}},{"cell_type":"markdown","source":"### DataA  \nDataA consists of records detailing **individual farmers from state X** and their pertinent characteristics. Each row corresponds to a unique farmer identified by an **ID**. The dataset encompasses attributes like:   \n* The farmer's **age**  \n* **The number of lands** they own or operate  \n* The primary agricultural **product** they cultivate  \n* Their years of **agricultural experience**.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\n# Create a dictionary with the data\ndataA= {\n    'ID': [1, 2, 3, 4, 5],\n    'Age': [56, 34, 39, 28, 70],\n    'Number of Lands': [15, 40, 23, 12, 38],\n    'Product': ['rice', 'rice', 'citrus', 'tea', 'rice'],\n    'Agricultural Experience': [30, 5, 14, 10, 52]\n}\n\n# Create DataFrame\nDataA = pd.DataFrame(dataA)\n\n# Display DataFrame\nprint(DataA)","metadata":{"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"   ID  Age  Number of Lands Product  Agricultural Experience\n0   1   56               15    rice                       30\n1   2   34               40    rice                        5\n2   3   39               23  citrus                       14\n3   4   28               12     tea                       10\n4   5   70               38    rice                       52","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>ID</th>\n      <th>Age</th>\n      <th>Number of Lands</th>\n      <th>Product</th>\n      <th>Agricultural Experience</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>56</td>\n      <td>15</td>\n      <td>rice</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>34</td>\n      <td>40</td>\n      <td>rice</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>39</td>\n      <td>23</td>\n      <td>citrus</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>28</td>\n      <td>12</td>\n      <td>tea</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>70</td>\n      <td>38</td>\n      <td>rice</td>\n      <td>52</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### DataB\nDataB presents details regarding **farmers** who have opted for **insurance coverage** for their agricultural products. Each row in the dataset represents a distinct farmer identified by an ID, alongside their   \n* Insured **product**   \n* The **duration** of insurance coverage.  \n\nThe dataset captures information about the agricultural products that farmers choose to safeguard through insurance and the duration for which they are insured.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\n# Create a dictionary with the data\ndataB = {\n    'ID': [1, 3, 4, 5, 14, 16, 17, 21],\n    'Insured Product': ['rice', 'citrus', 'tea', 'rice', 'rice', 'tea', 'wheat', 'lentils'],\n    'Insurance Duration': [1, 2, 2, 1, 3, 2, 1, 4]\n}\n\n# Create DataFrame\nDataB = pd.DataFrame(dataB)\n\n# Display DataFrame\nprint(DataB)\n","metadata":{"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"   ID Insured Product  Insurance Duration\n0   1            rice                   1\n1   3          citrus                   2\n2   4             tea                   2\n3   5            rice                   1\n4  14            rice                   3\n5  16             tea                   2\n6  17           wheat                   1\n7  21         lentils                   4","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>ID</th>\n      <th>Insured Product</th>\n      <th>Insurance Duration</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>rice</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>citrus</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n      <td>tea</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>rice</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>14</td>\n      <td>rice</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>16</td>\n      <td>tea</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>17</td>\n      <td>wheat</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>21</td>\n      <td>lentils</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### DataC  \nDataC includes data pertaining to farmers who **purchase fertilizer** from specific stores. Each row within the dataset represents a **distinct farmer** identified by an ID and their corresponding fertilizer purchase details. The dataset includes   \n* Information on the **type of fertilizer** purchased  \n* The **quantity** bought  \n* The associated **price**.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\n# Create a dictionary with the data\ndataC = {\n    'ID': [4, 5, 14, 16, 36, 20, 22, 23],\n    'Fertilizer Type': ['a', 'b', 'a', 'a', 'a', 'c', 'b', 'a'],\n    'Amount': [3, 4, 2, 20, 2, 15, 17, 12],\n    'Price': [33400, 24500, 18000, 43600, 27000, 54000, 34000, 12000]\n}\n\n# Create DataFrame\nDataC = pd.DataFrame(dataC)\n\n# Display DataFrame\nprint(DataC)\n","metadata":{"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   ID Fertilizer Type  Amount  Price\n0   4               a       3  33400\n1   5               b       4  24500\n2  14               a       2  18000\n3  16               a      20  43600\n4  36               a       2  27000\n5  20               c      15  54000\n6  22               b      17  34000\n7  23               a      12  12000","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>ID</th>\n      <th>Fertilizer Type</th>\n      <th>Amount</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n      <td>a</td>\n      <td>3</td>\n      <td>33400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>b</td>\n      <td>4</td>\n      <td>24500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>14</td>\n      <td>a</td>\n      <td>2</td>\n      <td>18000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>16</td>\n      <td>a</td>\n      <td>20</td>\n      <td>43600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>36</td>\n      <td>a</td>\n      <td>2</td>\n      <td>27000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>20</td>\n      <td>c</td>\n      <td>15</td>\n      <td>54000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>22</td>\n      <td>b</td>\n      <td>17</td>\n      <td>34000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>23</td>\n      <td>a</td>\n      <td>12</td>\n      <td>12000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Inner Join  \n**Which farmers are from state X that have insured agricultural products and purchased fertilizers simultaneously?**  \n\nIn an inner join, the resulting DataFrame will contain only the rows where the key exists in both (or all) DataFrames being merged. This is similar to an SQL inner join, where only the matching records from both (or all) tables are retained in the result.   \n\n![image.png](attachment:83e2ceec-6f10-4b5f-9b6c-9f209115801a.png)","metadata":{},"attachments":{"83e2ceec-6f10-4b5f-9b6c-9f209115801a.png":{"image/png":"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"}}},{"cell_type":"code","source":"import pandas as pd\n\n# Perform inner joins on common identifiers\nmerged_inner = pd.merge(DataA, DataB, on=\"ID\", how=\"inner\")\nmerged_inner = pd.merge(merged_inner, DataC, on=\"ID\", how=\"inner\")\n\n#left_on right_on ... left_index right_index\n\n# Filter for farmers with agricultural experience, insured products, and purchased fertilizers\nresult = merged_inner[['ID']]\n\n# Display the resulting DataFrame containing farmer IDs\nprint(result)\n","metadata":{"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"   ID\n0   4\n1   5","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>ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Outer Join\n**Is there a way to capture all farmers' data, including those who are from state X, insured products, purchased fertilizers, or a combination of these?**    \n\nIn an outer join, the resulting DataFrame will contain all rows from both (all) DataFrames being merged. If a key exists in one DataFrame but not the other, the missing values will be filled with NaN (Not a Number). This is similar to an SQL outer join, where all records from both (all) tables are retained in the result, and missing values are filled with NULL.   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"}}},{"cell_type":"code","source":"import pandas as pd\n\n# Perform outer joins on common identifiers\nmerged_outer = pd.merge(DataA, DataB, on=\"ID\", how=\"outer\")\nmerged_outer = pd.merge(merged_outer, DataC, on=\"ID\", how=\"outer\")\n\n# Display the resulting DataFrame containing all farmers' data\nprint(merged_outer)\n","metadata":{"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"    ID   Age  Number of Lands Product  Agricultural Experience  \\\n0    1  56.0             15.0    rice                     30.0   \n1    2  34.0             40.0    rice                      5.0   \n2    3  39.0             23.0  citrus                     14.0   \n3    4  28.0             12.0     tea                     10.0   \n4    5  70.0             38.0    rice                     52.0   \n5   14   NaN              NaN     NaN                      NaN   \n6   16   NaN              NaN     NaN                      NaN   \n7   17   NaN              NaN     NaN                      NaN   \n8   20   NaN              NaN     NaN                      NaN   \n9   21   NaN              NaN     NaN                      NaN   \n10  22   NaN              NaN     NaN                      NaN   \n11  23   NaN              NaN     NaN                      NaN   \n12  36   NaN              NaN     NaN                      NaN   \n\n   Insured Product  Insurance Duration Fertilizer Type  Amount    Price  \n0             rice                 1.0             NaN     NaN      NaN  \n1              NaN                 NaN             NaN     NaN      NaN  \n2           citrus                 2.0             NaN     NaN      NaN  \n3              tea                 2.0               a     3.0  33400.0  \n4             rice                 1.0               b     4.0  24500.0  \n5             rice                 3.0               a     2.0  18000.0  \n6              tea                 2.0               a    20.0  43600.0  \n7            wheat                 1.0             NaN     NaN      NaN  \n8              NaN                 NaN               c    15.0  54000.0  \n9          lentils                 4.0             NaN     NaN      NaN  \n10             NaN                 NaN               b    17.0  34000.0  \n11             NaN                 NaN               a    12.0  12000.0  \n12             NaN                 NaN               a     2.0  27000.0  ","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>ID</th>\n      <th>Age</th>\n      <th>Number of Lands</th>\n      <th>Product</th>\n      <th>Agricultural Experience</th>\n      <th>Insured Product</th>\n      <th>Insurance Duration</th>\n      <th>Fertilizer Type</th>\n      <th>Amount</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>56.0</td>\n      <td>15.0</td>\n      <td>rice</td>\n      <td>30.0</td>\n      <td>rice</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>34.0</td>\n      <td>40.0</td>\n      <td>rice</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>39.0</td>\n      <td>23.0</td>\n      <td>citrus</td>\n      <td>14.0</td>\n      <td>citrus</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>28.0</td>\n      <td>12.0</td>\n      <td>tea</td>\n      <td>10.0</td>\n      <td>tea</td>\n      <td>2.0</td>\n      <td>a</td>\n      <td>3.0</td>\n      <td>33400.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>70.0</td>\n      <td>38.0</td>\n      <td>rice</td>\n      <td>52.0</td>\n      <td>rice</td>\n      <td>1.0</td>\n      <td>b</td>\n      <td>4.0</td>\n      <td>24500.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>14</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>rice</td>\n      <td>3.0</td>\n      <td>a</td>\n      <td>2.0</td>\n      <td>18000.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>16</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>tea</td>\n      <td>2.0</td>\n      <td>a</td>\n      <td>20.0</td>\n      <td>43600.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>17</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>wheat</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>20</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>c</td>\n      <td>15.0</td>\n      <td>54000.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>21</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>lentils</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>22</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>b</td>\n      <td>17.0</td>\n      <td>34000.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>23</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>a</td>\n      <td>12.0</td>\n      <td>12000.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>36</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>a</td>\n      <td>2.0</td>\n      <td>27000.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Left Join  \n**Can we return the farmers from state X and include any information about insurance and fertilizers for them**    \n\nIn a left join, the resulting DataFrame will contain all rows from the left DataFrame and matching rows from the right DataFrame based on the common key. If a key exists in the left DataFrame but not the right DataFrame, the corresponding values from the right DataFrame will be filled with NaN (Not a Number). This type of join ensures that all records from the left DataFrame are retained in the result, while only the matching records from the right DataFrame are included.  \n  \n![image.png](attachment:968b48d5-f508-4aef-9bb5-865e62ab6105.png)  \n\n \n### Right Join  \n\nIn a right join, the resulting DataFrame will contain all rows from the right DataFrame and matching rows from the left DataFrame based on the common key. If a key exists in the right DataFrame but not the left DataFrame, the corresponding values from the left DataFrame will be filled with NaN (Not a Number). This type of join ensures that all records from the right DataFrame are retained in the result, while only the matching records from the left DataFrame are included.  \n  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"},"4e1d1032-96ff-4a9b-863d-6e119038a9ad.png":{"image/png":"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"}}},{"cell_type":"code","source":"import pandas as pd\n\n# Perform left join between DataA and DataB based on the \"ID\" column to capture the farmers from state X with insured products\nfarmers_stateX = pd.merge(DataA, DataB, on=\"ID\", how=\"left\")\n\n# Perform left join between the result and DataC based on the \"ID\" column to include information about fertilizers purchased by farmers\nresult = pd.merge(farmers_stateX, DataC, on=\"ID\", how=\"left\")\n\n# Display the resulting DataFrame\nprint(result)\n","metadata":{"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"   ID  Age  Number of Lands Product  Agricultural Experience Insured Product  \\\n0   1   56               15    rice                       30            rice   \n1   2   34               40    rice                        5             NaN   \n2   3   39               23  citrus                       14          citrus   \n3   4   28               12     tea                       10             tea   \n4   5   70               38    rice                       52            rice   \n\n   Insurance Duration Fertilizer Type  Amount    Price  \n0                 1.0             NaN     NaN      NaN  \n1                 NaN             NaN     NaN      NaN  \n2                 2.0             NaN     NaN      NaN  \n3                 2.0               a     3.0  33400.0  \n4                 1.0               b     4.0  24500.0  ","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>ID</th>\n      <th>Age</th>\n      <th>Number of Lands</th>\n      <th>Product</th>\n      <th>Agricultural Experience</th>\n      <th>Insured Product</th>\n      <th>Insurance Duration</th>\n      <th>Fertilizer Type</th>\n      <th>Amount</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>56</td>\n      <td>15</td>\n      <td>rice</td>\n      <td>30</td>\n      <td>rice</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>34</td>\n      <td>40</td>\n      <td>rice</td>\n      <td>5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>39</td>\n      <td>23</td>\n      <td>citrus</td>\n      <td>14</td>\n      <td>citrus</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>28</td>\n      <td>12</td>\n      <td>tea</td>\n      <td>10</td>\n      <td>tea</td>\n      <td>2.0</td>\n      <td>a</td>\n      <td>3.0</td>\n      <td>33400.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>70</td>\n      <td>38</td>\n      <td>rice</td>\n      <td>52</td>\n      <td>rice</td>\n      <td>1.0</td>\n      <td>b</td>\n      <td>4.0</td>\n      <td>24500.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"##  Appending datasets \nAppending data is a critical procedure in data management that allows analysts to vertically stack datasets with **similar structures**, enhancing the depth and breadth of available information. By appending datasets, analysts can accumulate data collected **over time** or from **multiple sources** into a single cohesive dataset. This consolidation facilitates longitudinal analysis, enabling insights into trends, patterns, and changes over time. Additionally, appending data is invaluable for expanding datasets with new records, enabling **scalability** and **adaptability** to evolving data requirements. Whether appending monthly sales reports into a yearly dataset or aggregating sensor readings from various devices, this process is essential for maintaining comprehensive and up-to-date data repositories, supporting informed decision-making and actionable insights in various domains and industries.","metadata":{}},{"cell_type":"code","source":"df1 = pd.read_csv('/kaggle/input/append-data/Append-Jan.txt')\ndf2 = pd.read_csv('/kaggle/input/append-data/Append-Feb.txt')\ndf2.columns","metadata":{"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"Index(['regioN', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n       'employ', 'retire', 'gender', 'reside', 'tollfree', 'equip', 'callcard',\n       'wireless', 'longmon', 'tollmon', 'equipmon', 'cardmon', 'wiremon',\n       'longten', 'tollten', 'equipten', 'cardten', 'wireten', 'multline',\n       'voice', 'pager', 'internet', 'callid', 'callwait', 'forward', 'confer',\n       'ebill', 'loglong', 'logtoll', 'logequi', 'logcard', 'logwire', 'lninc',\n       'custcat', 'churn'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"appended = pd.concat([df1,df2], axis = 0)  # axis = 0 or 1 (rows or columns)\n\nappended = pd.concat([df1,df2], keys=[\"Jan\", \"Feb\"])\n\nappended.loc['Feb']","metadata":{"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"         region  tenure  age  marital  address  income  ed  employ  retire  \\\nJan 0       2.0      13   44        1        9    64.0   4       5     0.0   \n    1       3.0      68   52        1       24   116.0   1      29     0.0   \n    2       2.0      23   30        1        9    30.0   1       2     0.0   \n    3       3.0      45   22        1        2    19.0   2       4     0.0   \n    4       3.0      45   59        1        7   166.0   4      31     0.0   \n...         ...     ...  ...      ...      ...     ...  ..     ...     ...   \nFeb 495     NaN      61   50        1       16   190.0   2      22     0.0   \n    496     NaN      26   54        1       30    26.0   3       1     0.0   \n    497     NaN      10   39        0        0    27.0   3       0     0.0   \n    498     NaN      67   59        0       40   944.0   5      33     0.0   \n    499     NaN      50   36        1        7    39.0   3       3     0.0   \n\n         gender  ...  ebill   loglong   logtoll   logequi   logcard   logwire  \\\nJan 0         0  ...      0  1.308333       NaN       NaN  2.014903       NaN   \n    1         1  ...      0  2.898671  2.890372       NaN  3.409496       NaN   \n    2         0  ...      0  1.840550       NaN       NaN       NaN       NaN   \n    3         1  ...      1  2.388763       NaN       NaN  2.169054       NaN   \n    4         0  ...      0  2.277267  3.349904       NaN  2.484907       NaN   \n...         ...  ...    ...       ...       ...       ...       ...       ...   \nFeb 495       1  ...      1  2.824351       NaN  3.750680  3.277145  3.786460   \n    496       1  ...      1  1.648659  3.157000       NaN  3.167583  3.669951   \n    497       1  ...      1  1.098612       NaN  3.369018       NaN       NaN   \n    498       1  ...      1  3.286534  3.465736  3.999118  3.576550  4.186620   \n    499       1  ...      1  2.576422       NaN  3.514526  2.788093       NaN   \n\n            lninc  custcat  churn  regioN  \nJan 0    4.158883        1      1     NaN  \n    1    4.753590        3      0     NaN  \n    2    3.401197        3      0     NaN  \n    3    2.944439        2      1     NaN  \n    4    5.111988        3      0     NaN  \n...           ...      ...    ...     ...  \nFeb 495  5.247024        2      0     2.0  \n    496  3.258097        4      1     3.0  \n    497  3.295837        1      0     3.0  \n    498  6.850126        4      0     3.0  \n    499  3.663562        2      1     3.0  \n\n[1000 rows x 43 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></th>\n      <th>region</th>\n      <th>tenure</th>\n      <th>age</th>\n      <th>marital</th>\n      <th>address</th>\n      <th>income</th>\n      <th>ed</th>\n      <th>employ</th>\n      <th>retire</th>\n      <th>gender</th>\n      <th>...</th>\n      <th>ebill</th>\n      <th>loglong</th>\n      <th>logtoll</th>\n      <th>logequi</th>\n      <th>logcard</th>\n      <th>logwire</th>\n      <th>lninc</th>\n      <th>custcat</th>\n      <th>churn</th>\n      <th>regioN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">Jan</th>\n      <th>0</th>\n      <td>2.0</td>\n      <td>13</td>\n      <td>44</td>\n      <td>1</td>\n      <td>9</td>\n      <td>64.0</td>\n      <td>4</td>\n      <td>5</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1.308333</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.014903</td>\n      <td>NaN</td>\n      <td>4.158883</td>\n      <td>1</td>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3.0</td>\n      <td>68</td>\n      <td>52</td>\n      <td>1</td>\n      <td>24</td>\n      <td>116.0</td>\n      <td>1</td>\n      <td>29</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2.898671</td>\n      <td>2.890372</td>\n      <td>NaN</td>\n      <td>3.409496</td>\n      <td>NaN</td>\n      <td>4.753590</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>23</td>\n      <td>30</td>\n      <td>1</td>\n      <td>9</td>\n      <td>30.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1.840550</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.401197</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.0</td>\n      <td>45</td>\n      <td>22</td>\n      <td>1</td>\n      <td>2</td>\n      <td>19.0</td>\n      <td>2</td>\n      <td>4</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>2.388763</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.169054</td>\n      <td>NaN</td>\n      <td>2.944439</td>\n      <td>2</td>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.0</td>\n      <td>45</td>\n      <td>59</td>\n      <td>1</td>\n      <td>7</td>\n      <td>166.0</td>\n      <td>4</td>\n      <td>31</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>2.277267</td>\n      <td>3.349904</td>\n      <td>NaN</td>\n      <td>2.484907</td>\n      <td>NaN</td>\n      <td>5.111988</td>\n      <td>3</td>\n      <td>0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">Feb</th>\n      <th>495</th>\n      <td>NaN</td>\n      <td>61</td>\n      <td>50</td>\n      <td>1</td>\n      <td>16</td>\n      <td>190.0</td>\n      <td>2</td>\n      <td>22</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>2.824351</td>\n      <td>NaN</td>\n      <td>3.750680</td>\n      <td>3.277145</td>\n      <td>3.786460</td>\n      <td>5.247024</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>NaN</td>\n      <td>26</td>\n      <td>54</td>\n      <td>1</td>\n      <td>30</td>\n      <td>26.0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1.648659</td>\n      <td>3.157000</td>\n      <td>NaN</td>\n      <td>3.167583</td>\n      <td>3.669951</td>\n      <td>3.258097</td>\n      <td>4</td>\n      <td>1</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>NaN</td>\n      <td>10</td>\n      <td>39</td>\n      <td>0</td>\n      <td>0</td>\n      <td>27.0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1.098612</td>\n      <td>NaN</td>\n      <td>3.369018</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.295837</td>\n      <td>1</td>\n      <td>0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>NaN</td>\n      <td>67</td>\n      <td>59</td>\n      <td>0</td>\n      <td>40</td>\n      <td>944.0</td>\n      <td>5</td>\n      <td>33</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>3.286534</td>\n      <td>3.465736</td>\n      <td>3.999118</td>\n      <td>3.576550</td>\n      <td>4.186620</td>\n      <td>6.850126</td>\n      <td>4</td>\n      <td>0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>NaN</td>\n      <td>50</td>\n      <td>36</td>\n      <td>1</td>\n      <td>7</td>\n      <td>39.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>2.576422</td>\n      <td>NaN</td>\n      <td>3.514526</td>\n      <td>2.788093</td>\n      <td>NaN</td>\n      <td>3.663562</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 43 columns</p>\n</div>"},"metadata":{}}]}]}