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59 lines
4.8 KiB
JSON
59 lines
4.8 KiB
JSON
{
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"data": {
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"question": {
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"questionId": "3071",
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"questionFrontendId": "2882",
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"boundTopicId": null,
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"title": "Drop Duplicate Rows",
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"titleSlug": "drop-duplicate-rows",
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"content": "<pre>\nDataFrame customers\n+-------------+--------+\n| Column Name | Type |\n+-------------+--------+\n| customer_id | int |\n| name | object |\n| email | object |\n+-------------+--------+\n</pre>\n\n<p>There are some duplicate rows in the DataFrame based on the <code>email</code> column.</p>\n\n<p>Write a solution to remove these duplicate rows and keep only the <strong>first</strong> occurrence.</p>\n\n<p>The result format is in the following example.</p>\n\n<p> </p>\n<pre>\n<strong class=\"example\">Example 1:</strong>\n<strong>Input:</strong>\n+-------------+---------+---------------------+\n| customer_id | name | email |\n+-------------+---------+---------------------+\n| 1 | Ella | emily@example.com |\n| 2 | David | michael@example.com |\n| 3 | Zachary | sarah@example.com |\n| 4 | Alice | john@example.com |\n| 5 | Finn | john@example.com |\n| 6 | Violet | alice@example.com |\n+-------------+---------+---------------------+\n<strong>Output: </strong> \n+-------------+---------+---------------------+\n| customer_id | name | email |\n+-------------+---------+---------------------+\n| 1 | Ella | emily@example.com |\n| 2 | David | michael@example.com |\n| 3 | Zachary | sarah@example.com |\n| 4 | Alice | john@example.com |\n| 6 | Violet | alice@example.com |\n+-------------+---------+---------------------+\n<strong>Explanation:</strong>\nAlic (customer_id = 4) and Finn (customer_id = 5) both use john@example.com, so only the first occurrence of this email is retained.\n</pre>\n",
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"translatedTitle": null,
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"difficulty": "Easy",
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"likes": 34,
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"dislikes": 3,
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"similarQuestions": "[]",
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"exampleTestcases": "{\"headers\":{\"customers\":[\"customer_id\",\"name\",\"email\"]},\"rows\":{\"customers\":[[1,\"Ella\",\"emily@example.com\"],[2,\"David\",\"michael@example.com\"],[3,\"Zachary\",\"sarah@example.com\"],[4,\"Alice\",\"john@example.com\"],[5,\"Finn\",\"john@example.com\"],[6,\"Violet\",\"alice@example.com\"]]}}",
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"categoryTitle": "pandas",
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"codeSnippets": [
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{
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"lang": "Pandas",
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"langSlug": "pythondata",
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"code": "import pandas as pd\n\ndef dropDuplicateEmails(customers: pd.DataFrame) -> pd.DataFrame:\n ",
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}
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"stats": "{\"totalAccepted\": \"10.4K\", \"totalSubmission\": \"12.6K\", \"totalAcceptedRaw\": 10416, \"totalSubmissionRaw\": 12563, \"acRate\": \"82.9%\"}",
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"hints": [
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"Consider using a build-in function in pandas library to remove the duplicate rows based on specified data."
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],
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"solution": {
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"id": "2114",
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"sampleTestCase": "{\"headers\":{\"customers\":[\"customer_id\",\"name\",\"email\"]},\"rows\":{\"customers\":[[1,\"Ella\",\"emily@example.com\"],[2,\"David\",\"michael@example.com\"],[3,\"Zachary\",\"sarah@example.com\"],[4,\"Alice\",\"john@example.com\"],[5,\"Finn\",\"john@example.com\"],[6,\"Violet\",\"alice@example.com\"]]}}",
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"metaData": "{\n \"pythondata\": [\n \"customers = pd.DataFrame([], columns=['customer_id', 'name', 'email']).astype({'customer_id':'Int64', 'name':'object', 'email':'object'})\"\n ],\n \"database\": true,\n \"name\": \"dropDuplicateEmails\",\n \"languages\": [\n \"pythondata\"\n ]\n}",
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"envInfo": "{\"pythondata\": [\"Pandas\", \"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0</p>\"]}",
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