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‎introduction-pandas/2877-create-dataframe-list.py‎

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"""
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2877. Create a DataFrame from List
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Solved
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Easy
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Companies
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Hint
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Write a solution to create a DataFrame from a 2D list called student_data. This 2D list contains the IDs and ages of some students.
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The DataFrame should have two columns, student_id and age, and be in the same order as the original 2D list.
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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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student_data:
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[
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[1, 15],
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[2, 11],
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[3, 11],
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[4, 20]
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]
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Output:
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+------------+-----+
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| student_id | age |
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+------------+-----+
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| 1 | 15 |
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| 2 | 11 |
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| 3 | 11 |
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| 4 | 20 |
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+------------+-----+
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Explanation:
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A DataFrame was created on top of student_data, with two columns named student_id and age."
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"""
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import pandas as pd
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def createDataframe(student_data: List[List[int]]) -> pd.DataFrame:
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return pd.DataFrame(student_data, columns=["student_id", "age"])

‎introduction-pandas/2878-get-size-dataframe.py‎

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"""
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2878. Get the Size of a DataFrame
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Solved
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Easy
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Companies
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Hint
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DataFrame players:
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+-------------+--------+
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| Column Name | Type |
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+-------------+--------+
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| player_id | int |
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| name | object |
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| age | int |
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| position | object |
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| ... | ... |
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+-------------+--------+
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Write a solution to calculate and display the number of rows and columns of players.
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Return the result as an array:
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[number of rows, number of columns]
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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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| player_id | name | age | position | team |
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+-----------+----------+-----+-------------+--------------------+
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| 846 | Mason | 21 | Forward | RealMadrid |
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| 749 | Riley | 30 | Winger | Barcelona |
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| 155 | Bob | 28 | Striker | ManchesterUnited |
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| 583 | Isabella | 32 | Goalkeeper | Liverpool |
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| 388 | Zachary | 24 | Midfielder | BayernMunich |
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| 883 | Ava | 23 | Defender | Chelsea |
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| 355 | Violet | 18 | Striker | Juventus |
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| 247 | Thomas | 27 | Striker | ParisSaint-Germain |
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| 761 | Jack | 33 | Midfielder | ManchesterCity |
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| 642 | Charlie | 36 | Center-back | Arsenal |
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+-----------+----------+-----+-------------+--------------------+
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Output:
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[10, 5]
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Explanation:
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This DataFrame contains 10 rows and 5 columns."
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"""
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import pandas as pd
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def getDataframeSize(players: pd.DataFrame) -> List[int]:
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return list(players.shape)

‎introduction-pandas/2880-select-data.py‎

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"""
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2880. Select Data
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Solved
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Easy
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Companies
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Hint
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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:
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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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| 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:
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Student Ulysses has student_id = 101, we select the name and age."
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"""
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import pandas as pd
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def selectData(students: pd.DataFrame) -> pd.DataFrame:
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return students.loc[students["student_id"] == 101, ["name", "age"]]

‎introduction-pandas/2881-create-new-column.py‎

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"""
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2881. Create a New Column
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Solved
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Easy
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Companies
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Hint
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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:
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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:
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A new column bonus is created by doubling the value in the column salary."
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"""
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import pandas as pd
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def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame:
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employees["bonus"] = employees["salary"] * 2
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return employees

‎introduction-pandas/2882-drop-duplicate-rows.py‎

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"""
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2882. Drop Duplicate Rows
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Solved
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Easy
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Companies
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Hint
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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:
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Alic (customer_id = 4) and Finn (customer_id = 5) both use john@example.com, so only the first occurrence of this email is retained."
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"""
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import pandas as pd
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def dropDuplicateEmails(customers: pd.DataFrame) -> pd.DataFrame:
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return customers.drop_duplicates("email")

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