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Commit5f2a1b4

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Added tasks 2884-2888
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2884\. Modify Columns
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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 intends to give its employees a pay rise.
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Write a solution to**modify** the`salary` column by multiplying each salary by 2.
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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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| Jack | 19666 |
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| Piper | 74754 |
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| Mia | 62509 |
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| Ulysses | 54866 |
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+---------+--------+
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**Output:**
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+---------+--------+
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| name | salary |
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+---------+--------+
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| Jack | 39332 |
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| Piper | 149508 |
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| Mia | 125018 |
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| Ulysses | 109732 |
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+---------+--------+
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**Explanation:** Every salary has been doubled.
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# #Easy #2023_12_23_Time_401_ms_(96.35%)_Space_60.2_MB_(54.27%)
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importpandasaspd
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defmodifySalaryColumn(employees:pd.DataFrame)->pd.DataFrame:
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employees['salary']=employees['salary']*2
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returnemployees
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2885\. Rename Columns
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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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| id | int |
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| first | object |
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| last | object |
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| age | int |
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+-------------+--------+
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Write a solution to rename the columns as follows:
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*`id` to`student_id`
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*`first` to`first_name`
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*`last` to`last_name`
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*`age` to`age_in_years`
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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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| id | first | last | age |
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+----+---------+----------+-----+
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| 1 | Mason | King | 6 |
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| 2 | Ava | Wright | 7 |
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| 3 | Taylor | Hall | 16 |
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| 4 | Georgia | Thompson | 18 |
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| 5 | Thomas | Moore | 10 |
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+----+---------+----------+-----+
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**Output:**
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+------------+------------+-----------+--------------+
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| student_id | first_name | last_name | age_in_years |
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+------------+------------+-----------+--------------+
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| 1 | Mason | King | 6 |
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| 2 | Ava | Wright | 7 |
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| 3 | Taylor | Hall | 16 |
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| 4 | Georgia | Thompson | 18 |
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| 5 | Thomas | Moore | 10 |
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+------------+------------+-----------+--------------+
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**Explanation:** The column names are changed accordingly.
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# #Easy #2023_12_23_Time_467_ms_(68.13%)_Space_60.7_MB_(15.08%)
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importpandasaspd
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defrenameColumns(students:pd.DataFrame)->pd.DataFrame:
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students.rename(columns={'id':'student_id','first':'first_name','last':'last_name','age':'age_in_years'},inplace=True)
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returnstudents
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2886\. Change Data Type
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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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| grade | float |
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+-------------+--------+
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Write a solution to correct the errors:
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The`grade` column is stored as floats, convert it to integers.
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The result format is in the following example.
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**Example 1:****Input:** DataFrame students:
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+------------+------+-----+-------+
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| student_id | name | age | grade |
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+------------+------+-----+-------+
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| 1 | Ava | 6 | 73.0 |
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| 2 | Kate | 15 | 87.0 |
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+------------+------+-----+-------+
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**Output:**
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+------------+------+-----+-------+
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| student_id | name | age | grade |
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+------------+------+-----+-------+
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| 1 | Ava | 6 | 73 |
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| 2 | Kate | 15 | 87 |
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+------------+------+-----+-------+
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**Explanation:** The data types of the column grade is converted to int.
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# #Easy #2023_12_23_Time_421_ms_(94.57%)_Space_59.2_MB_(92.43%)
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importpandasaspd
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defchangeDatatype(students:pd.DataFrame)->pd.DataFrame:
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students['grade']=students['grade'].astype(int)
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returnstudents
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2887\. Fill Missing Data
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Easy
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DataFrame`products`
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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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| quantity | int |
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| price | int |
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+-------------+--------+
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Write a solution to fill in the missing value as <code>**0**</code> in the`quantity` column.
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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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| name | quantity | price |
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+-----------------+----------+-------+
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| Wristwatch | None | 135 |
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| WirelessEarbuds | None | 821 |
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| GolfClubs | 779 | 9319 |
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| Printer | 849 | 3051 |
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+-----------------+----------+-------+
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**Output:**
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+-----------------+----------+-------+
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| name | quantity | price |
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+-----------------+----------+-------+
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| Wristwatch | 0 | 135 |
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| WirelessEarbuds | 0 | 821 |
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| GolfClubs | 779 | 9319 |
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| Printer | 849 | 3051 |
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+-----------------+----------+-------+
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**Explanation:** The quantity for Wristwatch and WirelessEarbuds are filled by 0.
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# #Easy #2023_12_23_Time_404_ms_(97.11%)_Space_59.7_MB_(74.95%)
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importpandasaspd
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deffillMissingValues(products:pd.DataFrame)->pd.DataFrame:
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products['quantity'].fillna(0,inplace=True)
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returnproducts
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2888\. Reshape Data: Concatenate
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Easy
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DataFrame`df1`
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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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DataFrame`df2`
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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 concatenate these two DataFrames**vertically** into one DataFrame.
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The result format is in the following example.
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**Example 1:**
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**Input: df1**
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+------------+---------+-----+
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| student_id | name | age |
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+------------+---------+-----+
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| 1 | Mason | 8 |
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| 2 | Ava | 6 |
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| 3 | Taylor | 15 |
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| 4 | Georgia | 17 |
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+------------+---------+-----+
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**df2**
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+------------+------+-----+
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| student_id | name | age |
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+------------+------+-----+
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| 5 | Leo | 7 |
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| 6 | Alex | 7 |
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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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| 1 | Mason | 8 |
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| 2 | Ava | 6 |
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| 3 | Taylor | 15 |
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| 4 | Georgia | 17 |
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| 5 | Leo | 7 |
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| 6 | Alex | 7 |
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+------------+---------+-----+
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**Explanation:** The two DataFramess are stacked vertically, and their rows are combined.
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# #Easy #2023_12_23_Time_441_ms_(96.26%)_Space_59_MB_(97.37%)
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importpandasaspd
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defconcatenateTables(df1:pd.DataFrame,df2:pd.DataFrame)->pd.DataFrame:
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returnpd.concat([df1,df2],ignore_index=True)

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