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select-data | 3 Best One-liner Solution | 3-best-one-liner-solution-by-shivamkhato-2bce | Intuition\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \n\n# Complexity\n- Time | ShivamKhator | NORMAL | 2024-05-09T12:29:31.784553+00:00 | 2024-05-09T12:29:31.784586+00:00 | 11,597 | false | # Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101, ["name", "age"]]\n #OR\n return students.loc[students["student_id"] == 101, "name" :]\n #OR\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n```\n\n | 140 | 0 | ['Python', 'Python3', 'Pandas'] | 3 |
select-data | 3 diff Way || 1 line code | 3-diff-way-1-line-code-by-vvivekyadav-y1yl | If you got help from this,... Plz Upvote .. it encourage me\n# Code\n\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n retur | vvivekyadav | NORMAL | 2023-10-06T02:03:05.057653+00:00 | 2023-10-06T02:03:05.057694+00:00 | 10,416 | false | **If you got help from this,... Plz Upvote .. it encourage me**\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101, ["name", "age"]]\n # OR\n return students.loc[students["student_id"] == 101, "name" :]\n # OR\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n\n\n\n\n#\n``` | 108 | 0 | ['Python', 'Python3', 'Pandas'] | 3 |
select-data | Easy Solution Beginner Friendly || Pandas || Beats 98% | easy-solution-beginner-friendly-pandas-b-7cxt | Intuition\nThe problem suggests that we need to create a solution to select and return specific columns (name and age) from a given Pandas DataFrame named \'stu | vaish_1929 | NORMAL | 2023-10-07T08:27:34.458655+00:00 | 2023-10-07T08:41:28.816171+00:00 | 3,190 | false | # Intuition\nThe problem suggests that we need to create a solution to select and return specific columns (name and age) from a given Pandas DataFrame named \'students\' where the \'student_id\' is equal to 101.\n\n# Approach\nTo address this problem, we utilize the Pandas library in Python. We employ DataFrame indexing and selection techniques to filter the rows where the \'student_id\' is equal to 101. Then, we select only the "name" and "age" columns from the filtered DataFrame.\n\n# Complexity\n- Time complexity:\n - The time complexity depends on the size of the \'students\' DataFrame but can be considered as O(n), where n is the number of rows in the DataFrame.\n\n- Space complexity:\n - The space complexity is also dependent on the size of the \'students\' DataFrame but can be considered as O(n), where n is the number of rows in the DataFrame.\n\n# Code\n```python\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students[students.student_id == 101][["name", "age"]]\n | 19 | 0 | ['Pandas'] | 1 |
select-data | Easy Solution with explanation. | easy-solution-with-explanation-by-swayam-rpjv | Intuition\nThe intuition for this problem is to filter the DataFrame to select the row where student_id is 101, and then select only the \'name\' and \'age\' co | Swayam248 | NORMAL | 2024-09-27T15:47:46.635304+00:00 | 2024-09-27T15:47:46.635340+00:00 | 1,836 | false | # Intuition\nThe intuition for this problem is to filter the DataFrame to select the row where student_id is 101, and then select only the \'name\' and \'age\' columns from that row.\n\n# Approach\nUse boolean indexing to filter the DataFrame for the row where student_id is 101.\nSelect only the \'name\' and \'age\' columns from the filtered result.\nReturn the resulting DataFrame.\n\n# Complexity\nTime complexity: O(n)\n\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\nThe time complexity is O(n), where n is the number of rows in the DataFrame. This is because the boolean indexing operation needs to check each row to find where student_id is 101.\n\nSpace complexity: O(1)\n\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\nThe space complexity is O(1) because we\'re returning a new DataFrame with only one row and two columns, regardless of the size of the input DataFrame. The space required is constant and doesn\'t grow with the input size.\nThis solution efficiently selects the required data from the DataFrame using pandas\' built-in indexing and column selection capabilities. It\'s a concise and readable solution that directly addresses the problem requirements.\n\n# Code\n```pythondata []\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students[\'student_id\']== 101, [\'name\', \'age\']]\n\n\'\'\'\nRow Filtering: students[\'student_id\'] == 101\n\nstudents[\'student_id\'] accesses the student_id column of the students DataFrame.\n\nstudents[\'student_id\'] == 101 creates a Boolean Series where each entry is True if the corresponding student_id is 101, and False otherwise. This is called a Boolean mask.\n\n.loc Indexer: students.loc[...]\n\n.loc is a label-based indexer used to select rows and columns by labels.\nstudents.loc[students[\'student_id\'] == 101] uses the Boolean mask created in step 2 to select only the rows where the student_id is 101.\nColumn Selection: [\'name\', \'age\']\n\nAfter selecting the rows where student_id is 101, students.loc\n[students[\'student_id\'] == 101, [\'name\', \'age\']] specifies the columns to be selected, which are name and age.\nPutting It All Together\nThe entire code students.loc[students[\'student_id\'] == 101, [\'name\', \'age\']] performs the following operations:\n\nFilter Rows: Selects rows in the students DataFrame where the student_id is 101.\nSelect Columns: From these filtered rows, selects only the name and age columns.\n\'\'\'\n``` | 9 | 0 | ['Pandas'] | 5 |
select-data | ✅Easy And Simple Solution With EXPLANATION✅ | easy-and-simple-solution-with-explanatio-w3br | \n\n### Explanation\n\nThis code defines a function named selectData that takes a pandas DataFrame as input and returns a new DataFrame containing the name and | deleted_user | NORMAL | 2024-06-07T07:31:48.813993+00:00 | 2024-06-07T07:33:24.848962+00:00 | 1,463 | false | \n\n### Explanation\n\nThis code defines a function named `selectData` that takes a pandas DataFrame as input and returns a new DataFrame containing the `name` and `age` columns for the rows where `student_id` is equal to 101.\n\n#### Step-by-step Explanation:\n\n1. **Import pandas**:\n ```python\n import pandas as pd\n ```\n - This line imports the pandas library, which is used for data manipulation and analysis in Python.\n\n2. **Define the function**:\n ```python\n def selectData(students: pd.DataFrame) -> pd.DataFrame:\n ```\n - The function `selectData` is defined with one parameter `students`. The type hint suggests that `students` should be a pandas DataFrame.\n - The function returns a pandas DataFrame, as indicated by the type hint `-> pd.DataFrame`.\n\n3. **Filter and Select Data**:\n ```python\n return students.loc[students["student_id"] == 101, ["name", "age"]]\n ```\n - The `loc` accessor is used to filter and select data based on label indexing.\n - `students["student_id"] == 101` creates a boolean mask where the condition is true for rows where `student_id` is 101.\n - `students.loc[students["student_id"] == 101, ["name", "age"]]` filters the DataFrame to include only the rows where `student_id` is 101 and selects only the `name` and `age` columns.\n\n\n#### Summary:\n\n- The function takes a pandas DataFrame and returns a new DataFrame filtered by `student_id` equal to 101.\n- It selects only the `name` and `age` columns for the filtered rows.\n- The `loc` accessor is used for label-based indexing to filter and select the data.\n\n---\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101, ["name", "age"]]\n``` | 8 | 0 | ['Pandas'] | 0 |
select-data | Pandas 1-Line | Elegant & Short | And more Pandas solutions ✅ | pandas-1-line-elegant-short-and-more-pan-2e00 | Complexity\n- Time complexity: O(n)\n- Space complexity: O(n)\n\n# Code\nPython []\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students | Kyrylo-Ktl | NORMAL | 2023-10-06T09:11:00.138310+00:00 | 2023-10-06T09:11:12.368983+00:00 | 696 | false | # Complexity\n- Time complexity: $$O(n)$$\n- Space complexity: $$O(n)$$\n\n# Code\n```Python []\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n```\n\n# Important!\n###### If you like the solution or find it useful, feel free to **upvote** for it, it will support me in creating high quality solutions)\n\n# 30 Days of Pandas solutions\n\n### Data Filtering \u2705\n- [Big Countries](https://leetcode.com/problems/big-countries/solutions/3848474/pandas-elegant-short-1-line/)\n- [Recyclable and Low Fat Products](https://leetcode.com/problems/recyclable-and-low-fat-products/solutions/3848500/pandas-elegant-short-1-line/)\n- [Customers Who Never Order](https://leetcode.com/problems/customers-who-never-order/solutions/3848527/pandas-elegant-short-1-line/)\n- [Article Views I](https://leetcode.com/problems/article-views-i/solutions/3867192/pandas-elegant-short-1-line/)\n\n\n### String Methods \u2705\n- [Invalid Tweets](https://leetcode.com/problems/invalid-tweets/solutions/3849121/pandas-elegant-short-1-line/)\n- [Calculate Special Bonus](https://leetcode.com/problems/calculate-special-bonus/solutions/3867209/pandas-elegant-short-1-line/)\n- [Fix Names in a Table](https://leetcode.com/problems/fix-names-in-a-table/solutions/3849167/pandas-elegant-short-1-line/)\n- [Find Users With Valid E-Mails](https://leetcode.com/problems/find-users-with-valid-e-mails/solutions/3849177/pandas-elegant-short-1-line/)\n- [Patients With a Condition](https://leetcode.com/problems/patients-with-a-condition/solutions/3849196/pandas-elegant-short-1-line-regex/)\n\n\n### Data Manipulation \u2705\n- [Nth Highest Salary](https://leetcode.com/problems/nth-highest-salary/solutions/3867257/pandas-elegant-short-1-line/)\n- [Second Highest Salary](https://leetcode.com/problems/second-highest-salary/solutions/3867278/pandas-elegant-short/)\n- [Department Highest Salary](https://leetcode.com/problems/department-highest-salary/solutions/3867312/pandas-elegant-short-1-line/)\n- [Rank Scores](https://leetcode.com/problems/rank-scores/solutions/3872817/pandas-elegant-short-1-line-all-30-days-of-pandas-solutions/)\n- [Delete Duplicate Emails](https://leetcode.com/problems/delete-duplicate-emails/solutions/3849211/pandas-elegant-short/)\n- [Rearrange Products Table](https://leetcode.com/problems/rearrange-products-table/solutions/3849226/pandas-elegant-short-1-line/)\n\n\n### Statistics \u2705\n- [The Number of Rich Customers](https://leetcode.com/problems/the-number-of-rich-customers/solutions/3849251/pandas-elegant-short-1-line/)\n- [Immediate Food Delivery I](https://leetcode.com/problems/immediate-food-delivery-i/solutions/3872719/pandas-elegant-short-1-line-all-30-days-of-pandas-solutions/)\n- [Count Salary Categories](https://leetcode.com/problems/count-salary-categories/solutions/3872801/pandas-elegant-short-1-line-all-30-days-of-pandas-solutions/)\n\n\n### Data Aggregation \u2705\n- [Find Total Time Spent by Each Employee](https://leetcode.com/problems/find-total-time-spent-by-each-employee/solutions/3872715/pandas-elegant-short-1-line-all-30-days-of-pandas-solutions/)\n- [Game Play Analysis I](https://leetcode.com/problems/game-play-analysis-i/solutions/3863223/pandas-elegant-short-1-line/)\n- [Number of Unique Subjects Taught by Each Teacher](https://leetcode.com/problems/number-of-unique-subjects-taught-by-each-teacher/solutions/3863239/pandas-elegant-short-1-line/)\n- [Classes More Than 5 Students](https://leetcode.com/problems/classes-more-than-5-students/solutions/3863249/pandas-elegant-short/)\n- [Customer Placing the Largest Number of Orders](https://leetcode.com/problems/customer-placing-the-largest-number-of-orders/solutions/3863257/pandas-elegant-short-1-line/)\n- [Group Sold Products By The Date](https://leetcode.com/problems/group-sold-products-by-the-date/solutions/3863267/pandas-elegant-short-1-line/)\n- [Daily Leads and Partners](https://leetcode.com/problems/daily-leads-and-partners/solutions/3863279/pandas-elegant-short-1-line/)\n\n\n### Data Aggregation \u2705\n- [Actors and Directors Who Cooperated At Least Three Times](https://leetcode.com/problems/actors-and-directors-who-cooperated-at-least-three-times/solutions/3863309/pandas-elegant-short/)\n- [Replace Employee ID With The Unique Identifier](https://leetcode.com/problems/replace-employee-id-with-the-unique-identifier/solutions/3872822/pandas-elegant-short-1-line-all-30-days-of-pandas-solutions/)\n- [Students and Examinations](https://leetcode.com/problems/students-and-examinations/solutions/3872699/pandas-elegant-short-1-line-all-30-days-of-pandas-solutions/)\n- [Managers with at Least 5 Direct Reports](https://leetcode.com/problems/managers-with-at-least-5-direct-reports/solutions/3872861/pandas-elegant-short/)\n- [Sales Person](https://leetcode.com/problems/sales-person/solutions/3872712/pandas-elegant-short-1-line-all-30-days-of-pandas-solutions/)\n\n\n | 6 | 0 | ['Python', 'Python3', 'Pandas'] | 0 |
select-data | ✔️✔️Beats 98.68% !!!! Simple Solution | Well explained 💥💥 | beats-9868-simple-solution-well-explaine-4inc | Understanding the Code\nDataFrame Selection: students\n\nRow Filtering: students[\'student_id\'] == 101\n\n- students[\'student_id\'] accesses the student_id co | shakthi251004 | NORMAL | 2024-07-31T13:44:19.449654+00:00 | 2024-07-31T13:44:19.449687+00:00 | 686 | false | # Understanding the Code\n**DataFrame Selection:** students\n\n**Row Filtering:** students[\'student_id\'] == 101\n\n- **students[\'student_id\']** accesses the student_id column of the students DataFrame.\n\n- **students[\'student_id\'] == 101** creates a Boolean Series where each entry is True if the corresponding student_id is 101, and False otherwise. This is called a Boolean mask.\n\n**.loc Indexer:** students.loc[...]\n\n- **.loc** is a label-based indexer used to select rows and columns by labels.\n- **students.loc[students[\'student_id\'] == 101]** uses the Boolean mask created in step 2 to select only the rows where the student_id is 101.\n\n**Column Selection:** [\'name\', \'age\']\n\n- After selecting the rows where student_id is 101, **students.loc\n[students[\'student_id\'] == 101, [\'name\', \'age\']]** specifies the columns to be selected, which are name and age.\n\n**Putting It All Together**\nThe entire code **students.loc[students[\'student_id\'] == 101, [\'name\', \'age\']]** performs the following operations:\n\n- **Filter Rows:** Selects rows in the students DataFrame where the student_id is 101.\n- **Select Columns:** From these filtered rows, selects only the name and age columns.\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students[\'student_id\']==101,[\'name\',\'age\']]\n``` | 5 | 0 | ['Pandas'] | 0 |
select-data | 🔥Best Solution in Pandas || One-liner || With explanation ||🔥 | best-solution-in-pandas-one-liner-with-e-xdsv | Explanation\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\nThis line defines a function called selectData. It takes one argument students, which is e | deleted_user | NORMAL | 2024-03-17T13:12:03.941154+00:00 | 2024-03-17T13:12:03.941187+00:00 | 1,131 | false | # Explanation\n`def selectData(students: pd.DataFrame) -> pd.DataFrame:`\nThis line defines a function called selectData. It takes one argument students, which is expected to be a pandas DataFrame containing student data. The function is annotated to indicate that students should be a DataFrame, and the return type of the function is specified as a pandas DataFrame.\n\n\n`return students.loc[students[\'student_id\'] == 101, [\'name\', \'age\']]`\nThis line performs the actual selection of data from the DataFrame. Let\'s break it down further:\n\n`students[\'student_id\'] == 101` filters the DataFrame students to only include rows where the \'student_id\' column has a value equal to 101.\n`students.loc[...]` selects rows and columns from the DataFrame using label-based indexing.\n`[:, [\'name\', \'age\']]` selects all rows (represented by the colon :) and only the \'name\' and \'age\' columns. This is specifying that we want to retrieve the \'name\' and \'age\' columns for the rows where the condition `students[\'student_id\'] == 101` is true.\n\n\n# Complexity\n- Time complexity: Filtering the DataFrame to select rows where the \'student_id\' column equals 101 has a time complexity of $$O(n)$$, where n is the number of rows in the DataFrame. This is because the operation involves iterating through each row of the DataFrame to check the condition.\n\n- Space complexity: $$O(1)$$, where 1 is the number of rows in the input DataFrame.\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students[\'student_id\'] == 101, [\'name\', \'age\']]\n```\n\n | 5 | 0 | ['Python', 'Python3', 'Pandas'] | 0 |
select-data | 1 line with masking | Beat 90% | 1-line-with-masking-beat-90-by-ankita290-5ef5 | Intuition\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \n\n# Complexity\n- Time | Ankita2905 | NORMAL | 2023-11-29T09:53:40.452172+00:00 | 2023-11-29T09:53:40.452197+00:00 | 2,032 | false | # Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(df: pd.DataFrame) -> pd.DataFrame:\n return df[df[\'student_id\'] == 101][[\'name\', \'age\']]\n``` | 4 | 0 | ['Pandas'] | 0 |
select-data | Easy to understand code | easy-to-understand-code-by-andersongarce-mls2 | Upvote \n# Code\n\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n student_101 = students.loc[students[\'student_id\'] == 10 | andersongarcesmolina | NORMAL | 2023-11-11T20:49:55.372384+00:00 | 2023-11-11T20:49:55.372408+00:00 | 569 | false | # Upvote \n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n student_101 = students.loc[students[\'student_id\'] == 101, [\'name\', \'age\']]\n return student_101\n\n``` | 4 | 0 | ['Pandas'] | 0 |
select-data | Easy to learn || 100% use to copy the program without error👍️🖤️👍️ | easy-to-learn-100-use-to-copy-the-progra-ot8p | \n Describe your first thoughts on how to solve this problem. \n\n\n Describe your approach to solving the problem. \n\n\n\n Add your time complexity here, e.g. | pranov_raaj__30 | NORMAL | 2024-08-22T03:19:31.873650+00:00 | 2024-08-22T03:19:31.873680+00:00 | 836 | false | \n<!-- Describe your first thoughts on how to solve this problem. -->\n\n\n<!-- Describe your approach to solving the problem. -->\n\n\n\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```pythondata []\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n``` | 3 | 0 | ['Pandas'] | 1 |
select-data | ✅EASY SOLUTION | easy-solution-by-swayam28-b5vj | Intuition\n\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \n\n# Complexity\n- Tim | swayam28 | NORMAL | 2024-08-11T13:42:32.506551+00:00 | 2024-08-11T13:42:32.506607+00:00 | 591 | false | # Intuition\n\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101, ["name", "age"]]\n #OR\n return students.loc[students["student_id"] == 101, "name" :]\n #OR\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n``` | 3 | 0 | ['Pandas'] | 0 |
select-data | My OWN PERSONAL review/study guide for .loc and filtering DFs | my-own-personal-reviewstudy-guide-for-lo-7qrb | Intuition\nThis is a solution that I wrote only as a review guide for myself when I revisit this problem. If you are reading this, I wish you all the best in ev | RangoDanger | NORMAL | 2024-02-22T22:34:48.294663+00:00 | 2024-02-22T22:34:48.294690+00:00 | 705 | false | # Intuition\nThis is a solution that I wrote only as a review guide for **myself** when I revisit this problem. If you are reading this, I wish you all the best in everything you do. Sorry if this is useless to you.\n\n# Approach\nYou can use boolean indexing to filter the DataFrame based on the condition df[\'student_id\'] == 101 and then select the \'name\' and \'age\' columns.\n\nThe **df.loc** indexer in pandas is used to access a group of rows and columns by labels or a boolean array. It is primarily label-based, which means you specify the rows and columns based on their labels.\n\nHere\'s how it works:\n\n- **Row Selection**: If you provide a single label (e.g., a row label), `df.loc[label]`, it returns the row(s) with that label.\n\n- **Row and Column Selection**: If you provide a label for both rows and columns, `df.loc[row_label, column_label]`, it returns the value at the specified row and column.\n\n- **Slice Selection**: You can also use slice notation with `df.loc` to select a range of rows or columns based on their labels. For example, `df.loc[start_row_label:end_row_label]` returns all rows between `start_row_label` and `end_row_label`.\n\n- **Boolean Indexing**: You can pass a boolean array to `df.loc` to select rows based on a condition. For example, `df.loc[condition]` returns all rows where the condition evaluates to `True`.\n\n# Complexity\n- Time complexity:\n\n1. The time complexity of `students[\'student_id\'] == 101` is O(n), where n is the number of rows in the DataFrame. This is because pandas needs to iterate over all the values in the \'student_id\' column to perform the comparison.\n\n2. The time complexity of `students.loc[students[\'student_id\'] == 101, [\'name\',\'age\']]` involves two main operations:\n - Filtering rows based on the condition `students[\'student_id\'] == 101`, which has a time complexity of O(n) as explained above.\n \n - Selecting specific columns \'name\' and \'age\', which typically has a time complexity of O(1) because pandas directly accesses the columns by label.\n \n3. Overall, the time complexity of the `selectData` function is O(n), where n is the number of rows in the DataFrame.\n\n- Space complexity:\n\n1. The space complexity is primarily determined by the size of the returned DataFrame containing the selected rows and columns. Since the returned DataFrame only contains a subset of the original data, its size depends on the number of rows that satisfy the condition `students[\'student_id\'] == 101`.\n\n2. Additionally, pandas may use additional memory for intermediate data structures during the filtering and selection process, but this is typically negligible compared to the size of the input and output DataFrames.\n\n3. Therefore, the space complexity of the `selectData` function is O(m), where m is the number of rows that satisfy the condition `students[\'student_id\'] == 101`.\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n # return the row: students[students[\'student_id\'] == 101]\n row = students.loc[students[\'student_id\'] == 101, [\'name\',\'age\']]\n return row\n``` | 3 | 0 | ['Pandas'] | 0 |
select-data | Simple One Line Code || Beat 100% || | simple-one-line-code-beat-100-by-yashmen-ozz3 | If you got help from this, Plz Upvote\n\n# Complexity\n- Time complexity: O(n)\n Add your time complexity here, e.g. O(n) \n- Space complexity: O(k)\n Add your | yashmenaria | NORMAL | 2023-10-06T04:32:36.459573+00:00 | 2023-10-06T04:32:36.459595+00:00 | 20 | false | # If you got help from this, Plz Upvote\n\n# Complexity\n- Time complexity: O(n)\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n- Space complexity: O(k)\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n where \'k\' is the number of rows that satisfy the condition (in this case, students with \'student_id\' equal to 101).\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n``` | 3 | 0 | ['Pandas'] | 0 |
select-data | Select Data || Easy solution ✅ | select-data-easy-solution-by-lipika02-o15w | Code\n\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n df = pd.DataFrame(students) # whole dataframe\n df = df[df[\'stud | lipika02 | NORMAL | 2023-10-04T01:02:10.557230+00:00 | 2023-10-04T01:02:10.557256+00:00 | 656 | false | # Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n df = pd.DataFrame(students) # whole dataframe\n df = df[df[\'student_id\']==101] # dataframe with student_id==101\n df = df[[\'name\',\'age\']] # selected columns from dataframe with student_id=101\n return df\n \n``` | 3 | 0 | ['Python', 'Python3', 'Pandas'] | 0 |
select-data | Select Data - <PANDAS> | select-data-pandas-by-preeom-fs4u | Code | PreeOm | NORMAL | 2025-03-15T16:08:35.105323+00:00 | 2025-03-15T16:08:35.105323+00:00 | 227 | false |
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 2 | 0 | ['Pandas'] | 0 |
select-data | Easy 1-line solution using .loc | easy-1-line-solution-using-loc-by-hiya99-p58t | Intuition\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \n\n# Complexity\n- Time | Hiya99 | NORMAL | 2024-09-29T05:06:29.547900+00:00 | 2024-09-29T05:06:29.547925+00:00 | 653 | false | # Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```pythondata []\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"]==101,["name","age"]]\n``` | 2 | 0 | ['Pandas'] | 0 |
select-data | Another One | another-one-by-amangurung5225-vvia | Intuition\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \n\n# Complexity\n- Time | amangurung5225 | NORMAL | 2024-01-04T06:20:02.882458+00:00 | 2024-01-04T06:20:02.882497+00:00 | 1,147 | false | # Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students[\'student_id\']==101,[\'name\',\'age\']]\n``` | 2 | 0 | ['Pandas'] | 0 |
select-data | Select Data || Pandas || Solution by bharadwaj | select-data-pandas-solution-by-bharadwaj-c46c | Approach\nSelect Data by Column Name and Condition\n\n# Complexity\n- Time complexity:\nO(n)\n\n- Space complexity:\nO(1)\n\n# Code\n\nimport pandas as pd\n\nde | Manu-Bharadwaj-BN | NORMAL | 2023-10-14T14:35:34.459395+00:00 | 2023-10-14T14:35:34.459412+00:00 | 536 | false | # Approach\nSelect Data by Column Name and Condition\n\n# Complexity\n- Time complexity:\nO(n)\n\n- Space complexity:\nO(1)\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101, "name" :]\n``` | 2 | 0 | ['Pandas'] | 0 |
select-data | Simple | One line code | Python | Query method | ⭐⭐⭐⭐⭐ | simple-one-line-code-python-query-method-soqj | Approach\n\nDataframe.query() method only works if the column name doesn\u2019t have any empty spaces. So before applying the method, spaces in column names are | AniruddhA77 | NORMAL | 2023-10-04T06:01:35.585957+00:00 | 2023-10-04T06:01:35.585975+00:00 | 639 | false | # Approach\n\nDataframe.query() method only works if the column name doesn\u2019t have any empty spaces. So before applying the method, spaces in column names are replaced with \u2018_\u2019 .\nThe data is filtered on the basis of condition given inside query.\n\n`DataFrame.query(expr, inplace=False, **kwargs)`\nwhere exp = condition of filtering\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.query("student_id == 101")[["name", "age"]]\n``` | 2 | 0 | ['Python', 'Python3', 'Pandas', 'Python ML'] | 0 |
select-data | Beats 93% | beats-93-by-rmsxypsgwl-v3t8 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | rMSxyPSgWL | NORMAL | 2025-04-09T10:26:17.980542+00:00 | 2025-04-09T10:26:17.980542+00:00 | 50 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[(students.student_id == 101)].iloc[:, 1:3]
``` | 1 | 0 | ['Pandas'] | 0 |
select-data | Simple and Easy Pandas Solution | simple-and-easy-pandas-solution-by-gokul-dw0y | Code | gokulram2221 | NORMAL | 2025-04-02T11:11:31.560471+00:00 | 2025-04-02T11:11:31.560471+00:00 | 119 | false | # Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
``` | 1 | 0 | ['Pandas'] | 0 |
select-data | Solution for Select Data in Python using Pandas. | solution-for-select-data-in-python-using-61px | IntuitionThis was a problem based on the inbuilt functions of a DataFrame. More on that in Approach.ApproachWe use the loc method on the students DataFrame wher | aahan0511 | NORMAL | 2025-03-23T09:50:58.426796+00:00 | 2025-03-23T16:43:24.498458+00:00 | 111 | false | # Intuition
This was a problem based on the inbuilt functions of a DataFrame. More on that in [Approach](#Approach).
# Approach
We use the `loc` method on the `students` DataFrame where we specify that the `student["student_id"] == 101`, which means `student_id` is `101` and we return the `name` and the `age`, `["name", "age"]`.
# Complexity
- Time complexity: `O(n)` | *Beats 91.22%*
- Space complexity: `O(1)` | *Beats 11.53%*
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
```
# Proof
# Support
If you liked this explanation and solution please **`upvote`**.
| 1 | 0 | ['Array', 'Hash Table', 'Pandas'] | 0 |
select-data | Pandas | pandas-by-adchoudhary-84jk | Code | adchoudhary | NORMAL | 2025-02-28T02:34:10.394540+00:00 | 2025-02-28T02:34:10.394540+00:00 | 164 | false | # Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 1 | 0 | ['Pandas'] | 0 |
select-data | Problem no. 2880 in pandas in easy method | problem-no-2880-in-pandas-in-easy-method-xeyo | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Tushar_1920 | NORMAL | 2025-02-23T16:01:06.818121+00:00 | 2025-02-23T16:01:06.818121+00:00 | 151 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 1 | 0 | ['Pandas'] | 0 |
select-data | Day 4 | Introduction to Pandas | day-4-introduction-to-pandas-by-x2e22ppn-tt95 | Complexity
Time complexity: O(n)
Space complexity: O(k) - Number of Rows
Code | x2e22PPnh5 | NORMAL | 2025-02-13T03:20:19.179347+00:00 | 2025-02-13T03:20:19.179347+00:00 | 171 | false | # Complexity
- Time complexity: O(n)
- Space complexity: O(k) - Number of Rows
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 1 | 0 | ['Python3', 'Pandas'] | 0 |
select-data | simple one line output | simple-one-line-output-by-harshmasiwal-jda1 | IntuitionApproachd=tablename.loc[tablename['columnname']==refine , ['columnfetch','columnfetch']]Complexity
Time complexity:
Space complexity:
Code | harshmasiwal | NORMAL | 2025-01-16T07:51:55.307292+00:00 | 2025-01-16T07:51:55.307292+00:00 | 281 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
d=tablename.loc[tablename['columnname']==refine , ['columnfetch','columnfetch']]
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101, ['name','age']]
``` | 1 | 0 | ['Pandas'] | 0 |
select-data | 🚀1-row solution that beats 999999999,99% (3 variant)🚀 | 1-row-solution-that-beats-99999999999-3-z9com | ApproachThe first variant it's the best practice
query (read pandas.DataFrame.query)
Main parameters
expr - our expression if u want many filter then use next | cesar4ik | NORMAL | 2025-01-13T09:59:04.613003+00:00 | 2025-01-13T09:59:04.613003+00:00 | 225 | false | # Approach
The first variant it's the best practice
- query (read pandas.DataFrame.query)
- Main parameters
- **expr** - our expression if u want many filter then use next syntax df.query('(col1 == 101) | (col2 == "Ulysses")'). Notice that I use different types of quotation marks!
- **inplace=False** - instead of df = df.query('col1 == 101') we can write df.query('col1 == 101', inplace = True)
- **[]**
- df[df.col1 == 101] similar to df.query('col1 == 101')
- **loc**
- Access a group of rows and columns by label(s) or a boolean array
Goodbye, my friends!
<3
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.query('student_id == 101')[['name', 'age']]
#or
return students[students.student_id == 101][['name', 'age']]
#or
return students[students.student_id.loc[101]][['name', 'age']]
``` | 1 | 0 | ['Pandas'] | 0 |
select-data | No one seems to have used this method | no-one-seems-to-have-used-this-method-by-0wjw | Intuition\nWhy do we need to tell loc to return "name" and "age"? what if we dont know the column headings and just want to return the entire row?\n\n# Approach | u5n9JCF6S7 | NORMAL | 2024-08-31T13:16:01.599388+00:00 | 2024-08-31T13:16:01.599419+00:00 | 15 | false | # Intuition\nWhy do we need to tell loc to return "name" and "age"? what if we dont know the column headings and just want to return the entire row?\n\n# Approach\nstudents df is not indexed, first create an index for student_id and set inplace = True, find the row "101" in student_id and then return the entire row.\n\n# Code\n```pythondata []\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n students.set_index(\'student_id\', inplace = True)\n return (students.loc[[101]])\n``` | 1 | 0 | ['Pandas'] | 0 |
select-data | Python pandas | python-pandas-by-shreyagarg63-098v | Complexity\n- Time complexity:\nO(n)\n\n- Space complexity:\nO(1)\n\n# Code\npython []\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.Data | ShreyaGarg63 | NORMAL | 2024-08-23T13:58:57.847551+00:00 | 2024-08-23T13:58:57.847584+00:00 | 5 | false | # Complexity\n- Time complexity:\nO(n)\n\n- Space complexity:\nO(1)\n\n# Code\n```python []\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101,["name","age"]]\n``` | 1 | 0 | ['Pandas'] | 0 |
select-data | easy to learn | easy-to-learn-by-yogesh_12-rvaz | Intuition\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \n\n# Complexity\n- Time | Yogesh_12__ | NORMAL | 2024-08-19T07:17:40.283462+00:00 | 2024-08-19T07:17:40.283486+00:00 | 574 | false | # Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101, ["name", "age"]]\n #OR\n return students.loc[students["student_id"] == 101, "name" :]\n #OR\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n \n``` | 1 | 0 | ['Pandas'] | 0 |
select-data | One line solution | 100% beats | one-line-solution-100-beats-by-vigneshva-d27n | \n\n# Code\n\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101,[\'name\',\' | vigneshvaran0101 | NORMAL | 2024-07-02T17:29:38.156773+00:00 | 2024-07-02T17:29:38.156795+00:00 | 10 | false | \n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students["student_id"] == 101,[\'name\',\'age\']]\n \n``` | 1 | 0 | ['Pandas'] | 0 |
select-data | Easiest one line solution. | easiest-one-line-solution-by-deleted_use-qrmm | Intuition\nThe problem likely involves filtering data based on certain criteria, specifically selecting rows where the student ID is 101.\n\n# Approach\nYour ap | deleted_user | NORMAL | 2024-06-09T08:41:40.323889+00:00 | 2024-06-09T08:45:03.925960+00:00 | 217 | false | # Intuition\nThe problem likely involves filtering data based on certain criteria, specifically selecting rows where the student ID is 101.\n\n# Approach\nYour approach seems to use the Pandas library to filter a DataFrame. You\'re selecting rows where the student ID is 101 and extracting only the \'name\' and \'age\' columns.\n# Complexity\n\n* Time complexity: \nO(n), where n is the number of rows in the input DataFrame. Filtering rows based on a condition typically requires scanning through each row once.\n\n* Space complexity: \nO(1) if the returned DataFrame is not considered, or \nO(m) if considering the returned DataFrame, where \n\uD835\uDC5A is the number of selected rows.\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students.loc[students[\'student_id\']==101,[\'name\',\'age\']]\n``` | 1 | 0 | ['Pandas'] | 0 |
select-data | ✔️✔️✔️2 easy approaches - 1 liner - PANDAS - with & without using loc ⚡⚡⚡ | 2-easy-approaches-1-liner-pandas-with-wi-eokx | approach 1 - using loc\n\nimport pandas as pd\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n\n return students.loc[students[\'student_id\'] == 10 | anish_sule | NORMAL | 2024-04-10T06:55:53.996186+00:00 | 2024-04-10T07:03:53.997905+00:00 | 241 | false | # approach 1 - using `loc`\n```\nimport pandas as pd\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n\n return students.loc[students[\'student_id\'] == 101, [\'name\', \'age\']]\n```\n\n# approach 2 - without using `loc`\n```\nimport pandas as pd\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n \n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n``` | 1 | 0 | ['Pandas'] | 0 |
select-data | easy solution | easy-solution-by-nandanadileep-sp41 | Intuition\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \n\n# Complexity\n- Time | nandanadileep | NORMAL | 2023-12-12T11:02:29.090580+00:00 | 2023-12-12T11:02:29.090604+00:00 | 1,198 | false | # Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(students: pd.DataFrame) -> pd.DataFrame:\n return students[students[\'student_id\'] == 101][[\'name\', \'age\']]\n \n``` | 1 | 0 | ['Pandas'] | 0 |
select-data | 1 line with masking | 1-line-with-masking-by-salvadordali-3wc4 | Intuition\nYou need three things. Mask which tells what are the rows you select then do df[mask] and then take corresponding columns\n\n# Code\n\nimport pandas | salvadordali | NORMAL | 2023-10-04T05:18:30.892433+00:00 | 2023-10-04T05:18:30.892455+00:00 | 1,365 | false | # Intuition\nYou need three things. Mask which tells what are the rows you select then do `df[mask]` and then take corresponding columns\n\n# Code\n```\nimport pandas as pd\n\ndef selectData(df: pd.DataFrame) -> pd.DataFrame:\n return df[df[\'student_id\'] == 101][[\'name\', \'age\']]\n``` | 1 | 0 | ['Pandas'] | 1 |
select-data | Select Data | select-data-by-robaireth-dwdl | Code | RobaireTH | NORMAL | 2025-04-11T00:42:36.978799+00:00 | 2025-04-11T00:42:36.978799+00:00 | 1 | false |
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame):
return students[students.student_id == 101][["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | 1 line code | 1-line-code-by-durga_raju-ig1c | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | durga_raju | NORMAL | 2025-04-09T09:08:40.475201+00:00 | 2025-04-09T09:08:40.475201+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101,['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | select data | select-data-by-rajshivam352-5qih | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | rajshivam352 | NORMAL | 2025-04-06T14:43:57.695475+00:00 | 2025-04-06T14:43:57.695475+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101,["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | day4 | day4-by-ingridtseng-pq0l | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | ingridtseng | NORMAL | 2025-04-03T13:17:30.172264+00:00 | 2025-04-03T13:17:30.172264+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
df = students.loc[students['student_id'] == 101]
df = df .drop(columns = 'student_id')
return df
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Pandas lesson 5 | pandas-lesson-5-by-titouanm-vdee | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | titouanm | NORMAL | 2025-04-03T06:23:02.404290+00:00 | 2025-04-03T06:23:02.404290+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | select data | select-data-by-d8750-vf6b | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | d8750 | NORMAL | 2025-04-02T13:00:39.027454+00:00 | 2025-04-02T13:00:39.027454+00:00 | 2 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
from typing import List
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
# Creating DataFrame
students = {
'student_id': [101, 53, 128, 3],
'name': ['ulysses', 'william', 'henry', 'henry'],
'age': [13, 10, 6, 11]
}
df = pd.DataFrame(students)
# Calling the function and printing result
print(selectData(df))
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | select data | select-data-by-d8750-ilr6 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | d8750 | NORMAL | 2025-04-02T13:00:34.965458+00:00 | 2025-04-02T13:00:34.965458+00:00 | 0 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
from typing import List
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
# Creating DataFrame
students = {
'student_id': [101, 53, 128, 3],
'name': ['ulysses', 'william', 'henry', 'henry'],
'age': [13, 10, 6, 11]
}
df = pd.DataFrame(students)
# Calling the function and printing result
print(selectData(df))
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy Solution | easy-solution-by-utkarsh-kushwaha-3aj1 | def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']] | utkarsh-kushwaha | NORMAL | 2025-04-01T16:48:44.469602+00:00 | 2025-04-01T16:48:44.469602+00:00 | 3 | false | def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
| 0 | 0 | ['Pandas'] | 0 |
select-data | Solution | solution-by-krupadharamshi-ex76 | Code | KrupaDharamshi | NORMAL | 2025-03-31T08:05:12.961642+00:00 | 2025-03-31T08:05:12.961642+00:00 | 2 | false |
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101] [["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | label-based data selection | label-based-data-selection-by-ojs4yvkyqr-syi2 | IntuitionStudent Ulysses has student_id = 101, we select the name and age.Approach.locComplexity
Time Complexity: 𝑂(𝑛)
Space Complexity: 𝑂(𝑛+𝑘)
Code | ojs4YVKYqR | NORMAL | 2025-03-30T08:55:51.172572+00:00 | 2025-03-30T08:55:51.172572+00:00 | 5 | false | # Intuition
Student Ulysses has student_id = 101, we select the name and age.
# Approach
.loc
# Complexity
- Time Complexity: 𝑂(𝑛)
- Space Complexity: 𝑂(𝑛+𝑘)
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101 , ['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Super-Easy Python Solution | Beginner friendly | super-easy-python-solution-beginner-frie-v6pm | Code | rajsekhar5161 | NORMAL | 2025-03-30T07:52:34.701524+00:00 | 2025-03-30T07:52:34.701524+00:00 | 4 | false |
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
df=pd.DataFrame(students)
return df.loc[df['student_id']==101,['name','age']]
``` | 0 | 0 | ['Array', 'Python', 'Python3', 'Pandas'] | 0 |
select-data | 1 liner | 1-liner-by-plavak_d10-8web | Code | plavak_d10 | NORMAL | 2025-03-29T18:19:38.418579+00:00 | 2025-03-29T18:19:38.418579+00:00 | 2 | false |
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students["student_id"]==101][['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | My First Submission | my-first-submission-by-acabato-f79o | Complexity
Time complexity: O(N)
Space complexity: O(N)
Code | ACabato | NORMAL | 2025-03-27T03:07:42.961711+00:00 | 2025-03-27T03:07:42.961711+00:00 | 2 | false | # Complexity
- Time complexity: O(N)
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity: O(N)
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
# Filtering ulysses' row from students
ulysses = students[students["student_id"] == 101]
# Dropping student_id column
ulysses.drop(ulysses.columns[0], axis=1, inplace=True)
return ulysses
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Pythonic Solution | pythonic-solution-by-ikfb47ngox-59bc | IntuitionUse the built-in Pandas function for locating a rowApproachUse .loc for label based selectingComplexity
Time complexity:
O(1) for selecting
Space compl | ikFB47NGox | NORMAL | 2025-03-26T19:19:22.246364+00:00 | 2025-03-26T19:19:22.246364+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
Use the built-in Pandas function for locating a row
# Approach
<!-- Describe your approach to solving the problem. -->
Use .loc for label based selecting
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
O(1) for selecting
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
O(m) for returning the elemnents
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
result = students.loc[students['student_id'] == 101, ['name', 'age']]
return result
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy and Simple solution | easy-and-simple-solution-by-ulrichwolves-ult9 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | ulrichwolves | NORMAL | 2025-03-26T15:14:45.211758+00:00 | 2025-03-26T15:14:45.211758+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
student_101 = students['student_id'] == 101
return students.loc[student_101, ['name', 'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | select data | select-data-by-kittur_manjunath-61rl | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | kittur_manjunath | NORMAL | 2025-03-26T09:29:53.116329+00:00 | 2025-03-26T09:29:53.116329+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.query("student_id == 101")[['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | select data | select-data-by-maheshmarathe05-4d4n | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | MaheshMarathe05 | NORMAL | 2025-03-25T17:26:17.093414+00:00 | 2025-03-25T17:26:17.093414+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"]==101,['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Select Data | select-data-by-shalinipaidimuddala-jyat | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | ShaliniPaidimuddala | NORMAL | 2025-03-24T08:17:46.321638+00:00 | 2025-03-24T08:17:46.321638+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Solution | solution-by-alinaqwertyuiop-s3cf | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | alinaqwertyuiop | NORMAL | 2025-03-24T01:27:56.387039+00:00 | 2025-03-24T01:27:56.387039+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
result = students.loc[students["student_id"] == 101, ["name", "age"]]
return result
students = {
'student_id': [101, 53, 128, 3],
'name': ['Ulysses', 'William', 'Henry', 'Henry'],
'age': [13, 10, 6, 11]
}
student = pd.DataFrame(students)
nameage = selectData(student)
print(nameage)
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Select Data | select-data-by-naeem_abd-kyw7 | Understanding the CodeDataFrame Selection: studentsRow Filtering: students['student_id'] == 101students['student_id'] accesses the student_id column of the stud | Naeem_ABD | NORMAL | 2025-03-23T14:29:38.797909+00:00 | 2025-03-23T14:29:38.797909+00:00 | 1 | false |
# Understanding the Code
# DataFrame Selection: students
Row Filtering: students['student_id'] == 101
students['student_id'] accesses the student_id column of the students DataFrame.
students['student_id'] == 101 creates a Boolean Series where each entry is True if the corresponding student_id is 101, and False otherwise. This is called a Boolean mask.
.loc Indexer: students.loc[...]
.loc is a label-based indexer used to select rows and columns by labels.
students.loc[students['student_id'] == 101] uses the Boolean mask created in step 2 to select only the rows where the student_id is 101.
Column Selection: ['name', 'age']
After selecting the rows where student_id is 101, students.loc
[students['student_id'] == 101, ['name', 'age']] specifies the columns to be selected, which are name and age.
Putting It All Together
The entire code students.loc[students['student_id'] == 101, ['name', 'age']] performs the following operations:
Filter Rows: Selects rows in the students DataFrame where the student_id is 101.
Select Columns: From these filtered rows, selects only the name and age columns.
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | 📌 Explained solution using loc 📌 | explained-solution-using-loc-by-iazinsch-dywp | 🎯 Selecting a Specific Student's Data in Pandas📌 Problem BreakdownWe are given a Pandas DataFrame named students with three columns:
student_id (integer) → Uniq | iazinschi2005 | NORMAL | 2025-03-22T13:55:48.196283+00:00 | 2025-03-22T13:55:48.196283+00:00 | 2 | false | # 🎯 Selecting a Specific Student's Data in Pandas
## 📌 Problem Breakdown
We are given a **Pandas DataFrame** named `students` with three columns:
- `student_id` (integer) → Unique ID for each student.
- `name` (string) → Name of the student.
- `age` (integer) → Age of the student.
Our goal is to **extract only the name and age** of the student who has `student_id = 101`.
---
## 🚀 Step-by-Step Explanation
### **1️⃣ Filtering the Data**
We use **boolean indexing** to filter out only the rows where `student_id == 101`:
```python
students[students.student_id == 101]
students.student_id == 101 creates a Boolean Series, where:
```
True corresponds to rows where student_id is 101.
False corresponds to all other rows.
Applying this condition to students returns only the rows where the condition is True.
## 2️⃣ Selecting the Required Columns
After filtering, we select only the name and age columns:
```python
students[students.student_id == 101].loc[:, ['name', 'age']]
```
-> .loc[:, ['name', 'age']] is used for column selection:
-> : means all rows (after filtering).
-> ['name', 'age'] selects only the desired columns.
## 3️⃣ Returning the Final DataFrame
```python
def selectData(students: pd.DataFrame) -> pd.DataFrame:
data = students[students.student_id == 101].loc[:, ['name', 'age']]
return data
```
We store the filtered DataFrame in data.
| 0 | 0 | ['Pandas'] | 0 |
select-data | Select Data using Panda DataFrame | select-data-using-panda-dataframe-by-qpa-2dyr | Code | QPaJdvI7XA | NORMAL | 2025-03-21T11:30:57.188567+00:00 | 2025-03-21T11:30:57.188567+00:00 | 1 | false | # Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame(students)
fetch_data = df['student_id'] == 101
return df.loc[fetch_data, ['name', 'age']]
``` | 0 | 0 | ['Python3', 'Pandas'] | 0 |
select-data | leetcode | leetcode-by-shalini_selva02-piyb | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | shalini_selva02 | NORMAL | 2025-03-20T14:09:10.423445+00:00 | 2025-03-20T14:09:10.423445+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | 3 EASY WAYS | 3-easy-ways-by-kahkasha_d-oszv | null | kahkasha_D | NORMAL | 2025-03-18T18:36:18.951222+00:00 | 2025-03-18T18:36:18.951222+00:00 | 2 | false |
```pythondata []
import pandas as pd
# USE LOC
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
# USE QUERY
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.query("student_id == 101")[["name", "age"]]
# USE FILTER
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students["student_id"] == 101].filter(items=["name", "age"])
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | easy | easy-by-macha_pratisha-r5bj | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | macha_pratisha | NORMAL | 2025-03-18T14:45:38.845332+00:00 | 2025-03-18T14:45:38.845332+00:00 | 1 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"]==101,["name","age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Select Data | select-data-by-i3twd28w8n-ehd6 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | I3Twd28W8N | NORMAL | 2025-03-11T05:10:30.810450+00:00 | 2025-03-11T05:10:30.810450+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def createDataframe(student_data):
# Define column names
columns = ["student_id", "age"]
# Create the DataFrame
df = pd.DataFrame(student_data, columns=columns)
return df
def getDataframeSize(df):
return list(df.shape)
def selectFirstRows(df):
return df.head(3)
def selectData(df):
return df[df["student_id"] == 101][["name", "age"]]
# Example input
player_data = [
[846, "Mason", 21, "Forward", "RealMadrid"],
[749, "Riley", 30, "Winger", "Barcelona"],
[155, "Bob", 28, "Striker", "ManchesterUnited"],
[583, "Isabella", 32, "Goalkeeper", "Liverpool"],
[388, "Zachary", 24, "Midfielder", "BayernMunich"],
[883, "Ava", 23, "Defender", "Chelsea"],
[355, "Violet", 18, "Striker", "Juventus"],
[247, "Thomas", 27, "Striker", "ParisSaint-Germain"],
[761, "Jack", 33, "Midfielder", "ManchesterCity"],
[642, "Charlie", 36, "Center-back", "Arsenal"]
]
# Create the DataFrame for players
columns = ["player_id", "name", "age", "position", "team"]
players_df = pd.DataFrame(player_data, columns=columns)
# Get and print the shape
print(getDataframeSize(players_df))
# Example input for employees
employees_data = [
[3, "Bob", "Operations", 48675],
[90, "Alice", "Sales", 11096],
[9, "Tatiana", "Engineering", 33805],
[60, "Annabelle", "InformationTechnology", 37678],
[49, "Jonathan", "HumanResources", 23793],
[43, "Khaled", "Administration", 40454]
]
# Create the DataFrame for employees
columns = ["employee_id", "name", "department", "salary"]
employees_df = pd.DataFrame(employees_data, columns=columns)
# Get and print the first three rows
print(selectFirstRows(employees_df))
# Example input for students
students_data = [
[101, "Ulysses", 13],
[53, "William", 10],
[128, "Henry", 6],
[3, "Henry", 11]
]
# Create the DataFrame for students
columns = ["student_id", "name", "age"]
students_df = pd.DataFrame(students_data, columns=columns)
# Select student by ID
print(selectData(students_df))
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | select data | select-data-by-bala_bi-bt0r | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Bala_bi | NORMAL | 2025-03-03T07:03:04.217454+00:00 | 2025-03-03T07:03:04.217454+00:00 | 4 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Simple and quick solution | simple-and-quick-solution-by-rasikapandi-5usx | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | rasikapandit33 | NORMAL | 2025-03-01T15:47:57.604781+00:00 | 2025-03-01T15:47:57.604781+00:00 | 2 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101,['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Filtering the Columns from Dataframe | filtering-the-columns-from-dataframe-by-5ya0l | Filtering the data from students dataframe by selecting the name and age columns**Breakdown:
**df[df['student_id'] == 101] → Gets the row where student_id = 101 | NavyaYadagiri | NORMAL | 2025-02-27T09:44:26.649353+00:00 | 2025-02-27T09:44:26.649353+00:00 | 4 | false | Filtering the data from students dataframe by selecting the name and age columns
**Breakdown:
**
df[df['student_id'] == 101] → Gets the row where student_id = 101 (This is boolean selection)
[['name', 'age']] → Selects only the name and age columns. (This is selecting the specific columns)
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
result = students[students['student_id'] == 101][['name', 'age']]
return result
students = {
'student_id' : [101,53,128,3],
'name' : ['Ulysses', 'William', 'Henry', 'Henry'],
'age': [13, 10, 6, 11]
}
pd_Student = pd.DataFrame(students)
print(selectData(pd_Student))
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | using loc of specified row | using-loc-of-specified-row-by-teja_puram-navv | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Teja_puramshetti | NORMAL | 2025-02-26T10:35:11.552891+00:00 | 2025-02-26T10:35:11.552891+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] ==101,["name",'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | simple solution | simple-solution-by-faizan_farhad-70ht | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Faizan_farhad | NORMAL | 2025-02-24T10:37:56.866351+00:00 | 2025-02-24T10:37:56.866351+00:00 | 2 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame(students)
return df[df['student_id'] == 101].drop('student_id',axis=1)
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | 🚀 Python3: Select Rows by Condition | 99.12% Runtime | python3-select-rows-by-condition-9912-ru-xmhi | IntuitionWe need to efficiently filter aDataFrameto select rows wherestudent_id == 101and return only thenameandagecolumns. The naive approach uses.loc[], but w | namebogsecret | NORMAL | 2025-02-23T04:35:57.179543+00:00 | 2025-02-23T04:35:57.179543+00:00 | 2 | false | # Intuition
We need to efficiently filter a `DataFrame` to select rows where `student_id == 101` and return only the `name` and `age` columns. The naive approach uses `.loc[]`, but we can optimize performance by leveraging NumPy's efficient array operations.
# Approach
1. **Use `.values` for Fast Boolean Indexing**:
- Extract `student_id` as a NumPy array using `.values` to speed up comparison.
- This avoids Pandas' internal index checks, making the filter operation significantly faster.
2. **Apply Boolean Masking**:
- Instead of using `.loc[]`, we directly filter `students[...]`, which is faster and more memory-efficient.
3. **Select Required Columns Efficiently**:
- After filtering, we directly select only the `name` and `age` columns to minimize data processing overhead.
# Complexity
- **Time complexity**: $$O(n)$$ where $$n$$ is the number of rows in `students`, as we perform a single pass to filter data.
- **Space complexity**: $$O(1)$$ since filtering is done in place without creating additional data structures.
# Code
```python3 []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students.student_id.values == 101][['name', 'age']] | 0 | 0 | ['Pandas'] | 0 |
select-data | Select Data Based on Specific student_id – Simple & Clean Solution | select-data-based-on-specific-student_id-5r9x | IntuitionApproachComplexity
Time complexity:O(N)
Space complexity:O(N) (linear in terms of the number of rows)
Code | hxHTE2C44x | NORMAL | 2025-02-22T20:55:30.234421+00:00 | 2025-02-22T20:55:30.234421+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:O(N)
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:O(N) (linear in terms of the number of rows)
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
dataframe = pd.DataFrame(students)
return dataframe.loc[dataframe['student_id'] == 101, ['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Select Data Based on Specific student_id – Simple & Clean Solution | select-data-based-on-specific-student_id-ezoc | IntuitionApproachComplexity
Time complexity:O(N)
Space complexity:O(N) (linear in terms of the number of rows)
Code | hxHTE2C44x | NORMAL | 2025-02-22T20:40:44.804497+00:00 | 2025-02-22T20:40:44.804497+00:00 | 2 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:O(N)
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:O(N) (linear in terms of the number of rows)
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
dataframe = pd.DataFrame(students)
return dataframe.loc[dataframe['student_id'] == 101, ['name','age']]
students = {
'student_id': [101, 53, 128, 3],
'name': ['Ulysses', 'William', 'Henry', 'Henry'],
'age': [13, 10, 6, 11]
}
res = selectData(students)
print(res)
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | My 18th Problem (pd) ;) | my-18th-problem-pd-by-chefcurry4-9zi7 | ApproachUse pandas filtering to extract the row where student_id = 101, then select only the name and age columns.Complexity
Time complexity:O(n)
Space complex | Chefcurry4 | NORMAL | 2025-02-20T00:03:04.222628+00:00 | 2025-02-20T00:03:04.222628+00:00 | 3 | false | # Approach
<!-- Describe your approach to solving the problem. -->
Use pandas filtering to extract the row where student_id = 101, then select only the name and age columns.
# Complexity
- Time complexity: $$O(n)$$
<!-- Add your time complexity here, e.g. c -->
- Space complexity: $$O(1)$$
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | DAY-4 | Pandas | Select Data | Beats 87.85% | day-4-pandas-select-data-beats-8785-by-n-nb10 | Complexity
Time complexity: O(N)
Space complexity: O(1)
Code | Nidhi_Kamal | NORMAL | 2025-02-18T13:13:28.887636+00:00 | 2025-02-18T13:13:28.887636+00:00 | 3 | false | # Complexity
- Time complexity: O(N)
- Space complexity: O(1)
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Pandas Easy Ways Solution | pandas-easy-ways-solution-by-a_nishkumar-hqy7 | IntuitionThe students DataFrame has three columns:
student_id (type: int) - a unique identifier for the student.
name (type: object, which is generally a string | A_nishkumar | NORMAL | 2025-02-11T14:43:53.936138+00:00 | 2025-02-11T14:43:53.936138+00:00 | 4 | false | # Intuition
The students DataFrame has three columns:
1. student_id (type: int) - a unique identifier for the student.
2. name (type: object, which is generally a string in pandas) - the student's name.
3. age (type: int) - the student's age.
# Overview
This problem provides us with a pandas DataFrame and requires us to return data about one of the records in the DataFrame.
**Key Concepts:**
1. DataFrame: a 2D table-like structure, similar to a spreadsheet or SQL table. Each row represents an individual record and each column represents a different attribute. It is size-mutable designed to handle a mix of different types of data.
2. loc attribute: one of the primary ways to select data from a DataFrame. It is label-based, which means you have to specify the name of the rows or columns to select data. loc is label-based.
3. boolean mask: a series of True/False values used to filter or select elements from another data structure, such as a list, array, or DataFrame, based on a certain condition.
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101, ['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Simple and easy solution | simple-and-easy-solution-by-men0enmcx1-ii0d | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | MEN0enMCx1 | NORMAL | 2025-02-06T19:17:37.534094+00:00 | 2025-02-06T19:17:37.534094+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
df2=students[students['student_id']==101]
return df2[['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Sql-like solution | sql-like-solution-by-ardipazij-2mga | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | ardipazij | NORMAL | 2025-02-03T08:17:05.223643+00:00 | 2025-02-03T08:17:05.223643+00:00 | 5 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.query("student_id == 101")[["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Best way to solve | best-way-to-solve-by-naincyrohela1927-2ybr | IntuitionPandas DataFrame.loc attribute accesses a group of rows and columns by label(s) or a boolean array in the given Pandas DataFrame.ApproachStudents is th | NaincyRohela1927 | NORMAL | 2025-01-29T08:34:15.347798+00:00 | 2025-01-29T08:34:15.347798+00:00 | 5 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
Pandas DataFrame.loc attribute accesses a group of rows and columns by label(s) or a boolean array in the given Pandas DataFrame.
# Approach
<!-- Describe your approach to solving the problem. -->
Students is the name of the data frame where "students_id","age","name" is the coloumn name.
we have to search for the student through "student_id" and display name and age
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101,['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy Solution with Explanation | easy-solution-with-explanation-by-aisha_-44w6 | IntuitionThe intuition for this problem is to filter the DataFrame to select the row where student_id is 101, and then select only the 'name' and 'age' columns | Aisha_Hadi | NORMAL | 2025-01-29T06:54:06.440711+00:00 | 2025-01-29T06:54:06.440711+00:00 | 4 | false | # Intuition
The intuition for this problem is to filter the DataFrame to select the row where student_id is 101, and then select only the 'name' and 'age' columns from that row.
# Approach
We use the .loc[] method to filter rows where student_id is 101 and select only the "name" and "age" columns. First, a Boolean mask students["student_id"] == 101 identifies matching rows. Then, applying .loc[] extracts the required data efficiently.
# Complexity
**Time complexity:** O(n)
The time complexity is O(n), where n is the number of rows in the DataFrame. This is because the boolean indexing operation needs to check each row to find where student_id is 101.
**space complexity:** O(1)
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Python Bro | python-bro-by-shrijithsm-rye0 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | shrijithsm | NORMAL | 2025-01-28T17:11:45.491749+00:00 | 2025-01-28T17:11:45.491749+00:00 | 6 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101,['name','age']]
``` | 0 | 0 | ['Python', 'Python3', 'Pandas'] | 0 |
select-data | Filtering out the Data | filtering-out-the-data-by-pratyushdas-wckb | Fetch Student DataIntuition
First thoughts: The goal is to extract specific columns (name and age) for a student with a given student_id.
Focus: Filter the Data | PratyushDas | NORMAL | 2025-01-27T10:11:03.072016+00:00 | 2025-01-27T10:11:03.072016+00:00 | 3 | false | # Fetch Student Data
## Intuition
- **First thoughts**: The goal is to extract specific columns (`name` and `age`) for a student with a given `student_id`.
- **Focus**: Filter the DataFrame efficiently to obtain the desired information.
## Approach
- **Library import**: Import the pandas library for data manipulation.
- **Function definition**: Define a function `selectData` that takes a DataFrame as input.
- **Filtering**: Use boolean indexing to filter the DataFrame for rows where `student_id` is 101.
- **Column selection**: Select the `name` and `age` columns for the filtered rows.
- **Return result**: Return the filtered DataFrame containing the `name` and `age` of the student.
## Complexity
- **Time complexity**:
- The operation to filter and select the DataFrame is $$O(n)$$, where $$n$$ is the number of rows in the DataFrame.
- **Space complexity**:
- The space complexity is $$O(1)$$, as it operates on the original DataFrame without using additional space proportional to the input size.
## Code
```python
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[['name', 'age']][students['student_id'] == 101]
| 0 | 0 | ['Pandas'] | 0 |
select-data | BEST SOLUTION | best-solution-by-rhyd3dxa6x-bv52 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | rhyD3DxA6x | NORMAL | 2025-01-24T11:52:00.420479+00:00 | 2025-01-24T11:52:00.420479+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
#OR
return students.loc[students["student_id"] == 101, "name" :]
#OR
return students[students['student_id'] == 101][['name', 'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Follow industry standards by always creating a filter.🚀 | follow-industry-standards-by-always-crea-pmxo | IntuitionFollow industry standards by always creating a filterCode | aayushmaanhooda | NORMAL | 2025-01-23T08:41:49.362671+00:00 | 2025-01-23T08:41:49.362671+00:00 | 3 | false | # Intuition
Follow industry standards by always creating a filter
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
# Create a filter
filt = (students['student_id'] == 101)
# use loc to fetch that row with that filter
return students.loc[filt, ['name', 'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | one line solution | one-line-solution-by-prettywired-czrs | IntuitionApproachSince .loc gets the row you want, we can fulfill condition through that however we also dont want the result to show student_id so clarify whic | Prettywired | NORMAL | 2025-01-21T07:42:47.310479+00:00 | 2025-01-21T07:42:47.310479+00:00 | 6 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
Since .loc gets the row you want, we can fulfill condition through that however we also dont want the result to show student_id so clarify which columns to show.
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[['name','age']].loc[students['student_id']==101]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Python_EAsy | python_easy-by-prabhat7667-wdw8 | IntuitionApproachComplexity
Time complexity:
o(n)
Space complexity:
o(1)Code | prabhat7667 | NORMAL | 2025-01-18T13:21:38.270991+00:00 | 2025-01-18T13:21:38.270991+00:00 | 6 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
o(n)
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
o(1)
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
data = students[students['student_id']==101][['name','age']]
data1 = pd.DataFrame(data,columns=['name','age'])
return data1
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Select Data | select-data-by-reneematthew-ltva | IntuitionTo Select data from the Table givenApproachComplexity
Time complexity:
547 ms
Beats 23.36%
Space complexity:
Code | ReneeMatthew | NORMAL | 2025-01-17T03:17:56.131612+00:00 | 2025-01-17T03:17:56.131612+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
To Select data from the Table given
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
<i>547 ms
Beats 23.36%</i>
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students['student_id']==101][['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | we can do this | we-can-do-this-by-gaurav_kumar_borad-qcf4 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | GAURAV_KUMAR_BORAD | NORMAL | 2025-01-15T19:02:18.745567+00:00 | 2025-01-15T19:02:18.745567+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students['student_id']==101][['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy Solution | easy-solution-by-archana154799-ana0 | Code | Archana154799 | NORMAL | 2025-01-15T09:40:37.524142+00:00 | 2025-01-15T09:40:37.524142+00:00 | 2 | false |
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame()
df = students[students["student_id"] == 101]
df = df.filter(items=["name","age"])
return df
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Select data from dataset | select-data-from-dataset-by-prathmesh_52-73ne | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | prathmesh_5214 | NORMAL | 2025-01-09T13:55:18.003231+00:00 | 2025-01-09T13:55:18.003231+00:00 | 5 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101,['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Simple Solution | simple-solution-by-muhammad_saleem-clfw | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Muhammad_Saleem | NORMAL | 2025-01-09T08:59:11.746380+00:00 | 2025-01-09T08:59:11.746380+00:00 | 5 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy solution | easy-solution-by-koushik_55_koushik-ubyz | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Koushik_55_Koushik | NORMAL | 2025-01-08T13:05:49.736898+00:00 | 2025-01-08T13:05:49.736898+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id']==101,['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | 2880. Select Data | 2880-select-data-by-g8xd0qpqty-kdev | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | G8xd0QPqTy | NORMAL | 2025-01-07T03:52:18.514578+00:00 | 2025-01-07T03:52:18.514578+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students[students['student_id'] == 101][['name', 'age']]
students = pd.DataFrame([
[101, "Ulysses", 13],
[53, "William", 10],
[128, "Henry", 6],
[3, "Henry", 11]
], columns=["student_id", "name", "age"])
print(selectData(students))
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | query() used | query-used-by-rupam_noni-cgqh | null | rupam_noni | NORMAL | 2025-01-06T14:11:57.412349+00:00 | 2025-01-06T14:11:57.412349+00:00 | 2 | false |
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.query("student_id == 101")[['name','age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy CODE For Beginner | easy-code-for-beginner-by-mueen_khattak-w524 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Mueen_Khattak | NORMAL | 2025-01-05T17:11:09.300751+00:00 | 2025-01-05T17:11:09.300751+00:00 | 0 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
new = students.loc[students["student_id"] == 101 , ["name" ,"age"]]
return new
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy CODE For Beginner | easy-code-for-beginner-by-mueen_khattak-azmr | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | Mueen_Khattak | NORMAL | 2025-01-05T17:11:01.808479+00:00 | 2025-01-05T17:11:01.808479+00:00 | 2 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
new = students.loc[students["student_id"] == 101 , ["name" ,"age"]]
return new
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | 2880. Select Data | 2880-select-data-by-jessica_mangla-cb7s | IntuitionThe goal of the selectData function is to filter a DataFrame to get the data of a specific student with a student_id of 101. Specifically, the function | Jessica_Mangla | NORMAL | 2025-01-04T13:51:52.903539+00:00 | 2025-01-04T13:51:52.903539+00:00 | 4 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
The goal of the selectData function is to filter a DataFrame to get the data of a specific student with a student_id of 101. Specifically, the function is meant to return a subset of the DataFrame containing the name and age of the student whose student_id is 101.
# Approach
<!-- Describe your approach to solving the problem. -->
1. **Filter the DataFrame:** We need to filter the DataFrame (students) based on a condition. Here, the condition is that the student_id column should equal 101.
2. **Select Specific Columns:** Once the row is filtered, we need to select only the name and age columns for the selected row(s).
This can be achieved using pandas' .loc[] method. .loc[] allows for conditional selection of rows and columns in the DataFrame.
# Complexity
- Time complexity: O(n), where n is the number of rows in the DataFrame.
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity: O(k), where k is the number of rows in the filtered result (students with student_id == 101).
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"] == 101, ["name", "age"]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Selecting Specific Data from a DataFrame Based on a Condition | selecting-specific-data-from-a-dataframe-ij1t | # Intuition
Filter the DataFrame for rows matching the given student_id and select the required columns (name, age).# Approach
Use loc to filter rows based on s | Mohdmustufa | NORMAL | 2024-12-30T16:26:29.570079+00:00 | 2024-12-30T16:26:29.570079+00:00 | 5 | false | **# Intuition**
Filter the DataFrame for rows matching the given `student_id` and select the required columns (`name`, `age`).
**# Approach**
Use `loc` to filter rows based on `student_id` and retrieve only the necessary columns.
**# Complexity**
- **Time complexity**: $$O(n)$$ — Filtering the DataFrame involves scanning all rows.
- **Space complexity**: $$O(1)$$ — No additional data structures are used.
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame,student_id: int=101) -> pd.DataFrame:
result = students.loc[students['student_id'] == student_id, ['name', 'age']]
return result
data = {
"student_id": [101, 102, 103, 104],
"name": ['Alice', 'Bob', 'Charlie', 'David'],
"age": [20, 21, 19, 22],
"grade": ['A', 'B', 'C', 'A']
}
df=pd.DataFrame(data)
result=selectData(df)
print(result)
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | 2880. Select Data | 2880-select-data-by-py_nitin-qls6 | IntuitionApproachComplexity
Time complexity:
Space complexity:
Code | py_nitin | NORMAL | 2024-12-25T16:55:33.686454+00:00 | 2024-12-25T16:55:33.686454+00:00 | 8 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students['student_id'] == 101, ['name', 'age']]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Simplest Solution Python ✅ | Please Upvote 😇 | simplest-solution-python-please-upvote-b-isp8 | IntuitionUsing query function, index or ilocApproachUsing the pandas library, select the name and age of the student with student_id 101 from the given DataFram | varunve | NORMAL | 2024-12-25T05:40:24.793745+00:00 | 2024-12-25T05:40:24.793745+00:00 | 4 | false | # Intuition
Using query function, index or iloc
# Approach
Using the pandas library, select the name and age of the student with student_id 101 from the given DataFrame students.
Return a DataFrame containing the name and age of the student with student_id 101.
The query method is used to select rows from a DataFrame based on a condition.
The query method takes a string as an argument that specifies the condition for selecting rows.
The query method returns a DataFrame containing the rows that satisfy the condition.
# Code
```pythondata []
import pandas as pd
'''
Using the pandas library, select the name and age of the student with student_id 101 from the given DataFrame students.
Return a DataFrame containing the name and age of the student with student_id 101.
The query method is used to select rows from a DataFrame based on a condition.
The query method takes a string as an argument that specifies the condition for selecting rows.
The query method returns a DataFrame containing the rows that satisfy the condition.
'''
def selectData(students: pd.DataFrame) -> pd.DataFrame:
# dt = students.loc[students['student_id']==101,['name','age']] # Alternative solution using the loc method
# dt = students[students['student_id']==101][['name','age']] # Alternative solution using boolean indexing
# dt = students[students['student_id']==101].loc[:,['name','age']] # Alternative solution using boolean indexing and the loc method
# dt = students[students['student_id']==101].iloc[:,[1,2]] # Alternative solution using boolean indexing and the iloc method
# dt = students[students['student_id']==101].iloc[:,1:3] # Alternative solution using boolean indexing and the iloc method
# dt = students.loc[students["student_id"] == 101, "name" :] # Alternative solution using the loc method
dt = students.query('student_id == 101')[['name','age']] # Select the name and age of the student with student_id 101 from the DataFrame students
return dt
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Using iloc | using-iloc-by-apjs-ob5k | IntuitionIn pandas, loc and iloc are used to access rows and columns in a DataFrame. iloc Accesses rows and columns by integer positions.ApproachSyntax: df.iloc | APJS | NORMAL | 2024-12-25T04:11:54.782176+00:00 | 2024-12-25T04:11:54.782176+00:00 | 4 | false | # Intuition
In pandas, `loc` and `iloc` are used to access rows and columns in a DataFrame. `iloc` Accesses rows and columns by integer positions.
# Approach
Syntax: `df.iloc[row_index, column_index]`
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
result = students[students.student_id == 101]
return result.iloc[:, [1,2]]
``` | 0 | 0 | ['Pandas'] | 0 |
select-data | Easy One Line Solution | easy-one-line-solution-by-jagadeesh27-l4uc | IntuitionApproach.loc used to locate the data specified. In this case, it is student_id and [name, age] columns are also passed as parameter.Complexity
Time co | Jagadeesh27 | NORMAL | 2024-12-23T17:41:41.553041+00:00 | 2024-12-23T17:41:41.553041+00:00 | 3 | false | # Intuition
<!-- Describe your first thoughts on how to solve this problem. -->
# Approach
<!-- Describe your approach to solving the problem. -->
.loc used to locate the data specified. In this case, it is student_id and [name, age] columns are also passed as parameter.
# Complexity
- Time complexity:
<!-- Add your time complexity here, e.g. $$O(n)$$ -->
- Space complexity:
<!-- Add your space complexity here, e.g. $$O(n)$$ -->
# Code
```pythondata []
import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
return students.loc[students["student_id"]==101,["name","age"]]
#OR
return students.loc[students["student_id"]==101,"name" :]
``` | 0 | 0 | ['Pandas'] | 0 |
bulls-and-cows | One pass Java solution | one-pass-java-solution-by-ruben3-1ynw | The idea is to iterate over the numbers in secret and in guess and count all bulls right away. For cows maintain an array that stores count of the number appear | ruben3 | NORMAL | 2015-10-30T22:17:26+00:00 | 2018-10-23T06:47:47.042945+00:00 | 72,641 | false | The idea is to iterate over the numbers in `secret` and in `guess` and count all bulls right away. For cows maintain an array that stores count of the number appearances in `secret` and in `guess`. Increment cows when either number from `secret` was already seen in `guest` or vice versa.\n\n\n public String getHint(String secret, String guess) {\n int bulls = 0;\n int cows = 0;\n int[] numbers = new int[10];\n for (int i = 0; i<secret.length(); i++) {\n int s = Character.getNumericValue(secret.charAt(i));\n int g = Character.getNumericValue(guess.charAt(i));\n if (s == g) bulls++;\n else {\n if (numbers[s] < 0) cows++;\n if (numbers[g] > 0) cows++;\n numbers[s] ++;\n numbers[g] --;\n }\n }\n return bulls + "A" + cows + "B";\n }\n\nA slightly more concise version:\n\n public String getHint(String secret, String guess) {\n int bulls = 0;\n int cows = 0;\n int[] numbers = new int[10];\n for (int i = 0; i<secret.length(); i++) {\n if (secret.charAt(i) == guess.charAt(i)) bulls++;\n else {\n if (numbers[secret.charAt(i)-'0']++ < 0) cows++;\n if (numbers[guess.charAt(i)-'0']-- > 0) cows++;\n }\n }\n return bulls + "A" + cows + "B";\n } | 886 | 4 | ['Java'] | 66 |
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