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Commitcff5c3c

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Added tasks 2879-2883
1 parentfd1cee4 commitcff5c3c

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2879\. Display the First Three Rows
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Easy
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DataFrame:`employees`
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+-------------+--------+
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| Column Name | Type |
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+-------------+--------+
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| employee_id | int |
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| name | object |
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| department | object |
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| salary | int |
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+-------------+--------+
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Write a solution to display the**first`3`** rows of this DataFrame.
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**Example 1:**
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**Input:** DataFrame employees
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+-------------+-----------+-----------------------+--------+
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| employee_id | name | department | salary |
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+-------------+-----------+-----------------------+--------+
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| 3 | Bob | Operations | 48675 |
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| 90 | Alice | Sales | 11096 |
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| 9 | Tatiana | Engineering | 33805 |
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| 60 | Annabelle | InformationTechnology | 37678 |
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| 49 | Jonathan | HumanResources | 23793 |
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| 43 | Khaled | Administration | 40454 |
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+-------------+-----------+-----------------------+--------+
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**Output:**
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+-------------+---------+-------------+--------+
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| employee_id | name | department | salary |
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+-------------+---------+-------------+--------+
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| 3 | Bob | Operations | 48675 |
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| 90 | Alice | Sales | 11096 |
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| 9 | Tatiana | Engineering | 33805 |
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+-------------+---------+-------------+--------+
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**Explanation:** Only the first 3 rows are displayed.
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# #Easy #2023_12_23_Time_406_ms_(96.44%)_Space_60.8_MB_(5.67%)
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importpandasaspd
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defselectFirstRows(zs:pd.DataFrame)->pd.DataFrame:
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returnzs.head(3)
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2880\. Select Data
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Easy
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DataFrame students
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+-------------+--------+
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| Column Name | Type |
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+-------------+--------+
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| student_id | int |
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| name | object |
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| age | int |
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+-------------+--------+
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Write a solution to select the name and age of the student with`student_id = 101`.
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The result format is in the following example.
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**Example 1: Input:**
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+------------+---------+-----+
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| student_id | name | age |
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+------------+---------+-----+
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| 101 | Ulysses | 13 |
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| 53 | William | 10 |
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| 128 | Henry | 6 |
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| 3 | Henry | 11 |
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+------------+---------+-----+
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**Output:**
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+---------+-----+
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| name | age |
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+---------+-----+
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| Ulysses | 13 |
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+---------+-----+
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**Explanation:** Student Ulysses has student_id = 101, we select the name and age.
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# #Easy #2023_12_23_Time_428_ms_(94.99%)_Space_60_MB_(83.82%)
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importpandasaspd
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defselectData(students:pd.DataFrame)->pd.DataFrame:
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returnstudents[students.student_id==101][['name','age']]
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2881\. Create a New Column
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Easy
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DataFrame`employees`
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+-------------+--------+
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| Column Name | Type. |
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+-------------+--------+
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| name | object |
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| salary | int. |
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+-------------+--------+
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A company plans to provide its employees with a bonus.
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Write a solution to create a new column name`bonus` that contains the**doubled values** of the`salary` column.
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The result format is in the following example.
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**Example 1:**
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**Input:** DataFrame employees
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+---------+--------+
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| name | salary |
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+---------+--------+
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| Piper | 4548 |
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| Grace | 28150 |
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| Georgia | 1103 |
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| Willow | 6593 |
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| Finn | 74576 |
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| Thomas | 24433 |
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+---------+--------+
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**Output:**
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+---------+--------+--------+
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| name | salary | bonus |
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+---------+--------+--------+
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| Piper | 4548 | 9096 |
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| Grace | 28150 | 56300 |
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| Georgia | 1103 | 2206 |
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| Willow | 6593 | 13186 |
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| Finn | 74576 | 149152 |
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| Thomas | 24433 | 48866 |
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+---------+--------+--------+
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**Explanation:** A new column bonus is created by doubling the value in the column salary.
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# #Easy #2023_12_23_Time_437_ms_(81.98%)_Space_60.3_MB_(37.80%)
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importpandasaspd
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defcreateBonusColumn(employees:pd.DataFrame)->pd.DataFrame:
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employees["bonus"]=employees["salary"]*2
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returnemployees
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2882\. Drop Duplicate Rows
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Easy
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DataFrame customers
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+-------------+--------+
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| Column Name | Type |
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+-------------+--------+
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| customer_id | int |
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| name | object |
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| email | object |
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+-------------+--------+
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There are some duplicate rows in the DataFrame based on the`email` column.
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Write a solution to remove these duplicate rows and keep only the**first** occurrence.
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The result format is in the following example.
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**Example 1:**
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**Input:**
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+-------------+---------+---------------------+
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| customer_id | name | email |
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+-------------+---------+---------------------+
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| 1 | Ella | emily@example.com |
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| 2 | David | michael@example.com |
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| 3 | Zachary | sarah@example.com |
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| 4 | Alice | john@example.com |
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| 5 | Finn | john@example.com |
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| 6 | Violet | alice@example.com |
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+-------------+---------+---------------------+
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**Output:**
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+-------------+---------+---------------------+
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| customer_id | name | email |
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+-------------+---------+---------------------+
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| 1 | Ella | emily@example.com |
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| 2 | David | michael@example.com |
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| 3 | Zachary | sarah@example.com |
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| 4 | Alice | john@example.com |
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| 6 | Violet | alice@example.com |
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+-------------+---------+---------------------+
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**Explanation:** Alic (customer_id = 4) and Finn (customer_id = 5) both usejohn@example.com, so only the first occurrence of this email is retained.
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# #Easy #2023_12_23_Time_405_ms_(97.36%)_Space_60.2_MB_(75.25%)
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importpandasaspd
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defdropDuplicateEmails(customers:pd.DataFrame)->pd.DataFrame:
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customers.drop_duplicates(subset='email',keep='first',inplace=True)
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returncustomers
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2883\. Drop Missing Data
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Easy
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DataFrame students
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+-------------+--------+
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| Column Name | Type |
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+-------------+--------+
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| student_id | int |
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| name | object |
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| age | int |
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+-------------+--------+
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There are some rows having missing values in the`name` column.
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Write a solution to remove the rows with missing values.
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The result format is in the following example.
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**Example 1:**
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**Input:**
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+------------+---------+-----+
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| student_id | name | age |
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+------------+---------+-----+
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| 32 | Piper | 5 |
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| 217 | None | 19 |
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| 779 | Georgia | 20 |
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| 849 | Willow | 14 |
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+------------+---------+-----+
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**Output:**
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+------------+---------+-----+
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| student_id | name | age |
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+------------+---------+-----+
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| 32 | Piper | 5 |
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| 779 | Georgia | 20 |
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| 849 | Willow | 14 |
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+------------+---------+-----+
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**Explanation:** Student with id 217 havs empty value in the name column, so it will be removed.
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# #Easy #2023_12_23_Time_429_ms_(94.97%)_Space_61.2_MB_(12.30%)
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importpandasaspd
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defdropMissingData(students:pd.DataFrame)->pd.DataFrame:
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r=pd.DataFrame(students)
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r.dropna(subset='name',inplace=True)
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returnr

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