Added tasks 2884-2888

src/main/java/g2801_2900/s2884_modify_columns/readme.md

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+2884\. Modify Columns
+
+Easy
+
+DataFrame `employees` 
+
+    +-------------+--------+ 
+    | Column Name | Type   | 
+    +-------------+--------+ 
+    | name        | object | 
+    | salary      | int    | 
+    +-------------+--------+
+
+A company intends to give its employees a pay rise.
+
+Write a solution to **modify** the `salary` column by multiplying each salary by 2.
+
+The result format is in the following example.
+
+**Example 1:**
+
+**Input:** DataFrame employees 
+
+    +---------+--------+ 
+    | name    | salary | 
+    +---------+--------+ 
+    | Jack    | 19666  | 
+    | Piper   | 74754  | 
+    | Mia     | 62509  | 
+    | Ulysses | 54866  | 
+    +---------+--------+
+
+**Output:** 
+
+    +---------+--------+ 
+    | name    | salary | 
+    +---------+--------+ 
+    | Jack    | 39332  | 
+    | Piper   | 149508 | 
+    | Mia     | 125018 | 
+    | Ulysses | 109732 | 
+    +---------+--------+
+
+**Explanation:** Every salary has been doubled.

src/main/java/g2801_2900/s2884_modify_columns/solution.py

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+# #Easy #2023_12_23_Time_401_ms_(96.35%)_Space_60.2_MB_(54.27%)
+
+import pandas as pd
+
+def modifySalaryColumn(employees: pd.DataFrame) -> pd.DataFrame:
+    employees['salary'] = employees['salary'] * 2
+    return employees

src/main/java/g2801_2900/s2885_rename_columns/readme.md

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+2885\. Rename Columns
+
+Easy
+
+DataFrame `students` 
+
+    +-------------+--------+ 
+    | Column Name | Type   | 
+    +-------------+--------+ 
+    | id          | int    | 
+    | first       | object | 
+    | last        | object | 
+    | age         | int    | 
+    +-------------+--------+
+
+Write a solution to rename the columns as follows:
+
+*   `id` to `student_id`
+*   `first` to `first_name`
+*   `last` to `last_name`
+*   `age` to `age_in_years`
+
+The result format is in the following example.
+
+**Example 1:** **Input:** 
+
+    +----+---------+----------+-----+ 
+    | id | first   | last     | age | 
+    +----+---------+----------+-----+ 
+    | 1  | Mason   | King     | 6   | 
+    | 2  | Ava     | Wright   | 7   | 
+    | 3  | Taylor  | Hall     | 16  | 
+    | 4  | Georgia | Thompson | 18  | 
+    | 5  | Thomas  | Moore    | 10  | 
+    +----+---------+----------+-----+
+
+**Output:** 
+
+    +------------+------------+-----------+--------------+ 
+    | student_id | first_name | last_name | age_in_years | 
+    +------------+------------+-----------+--------------+ 
+    | 1          | Mason      | King      | 6            | 
+    | 2          | Ava        | Wright    | 7            | 
+    | 3          | Taylor     | Hall      | 16           | 
+    | 4          | Georgia    | Thompson  | 18           | 
+    | 5          | Thomas     | Moore     | 10           | 
+    +------------+------------+-----------+--------------+
+
+**Explanation:** The column names are changed accordingly.

src/main/java/g2801_2900/s2885_rename_columns/solution.py

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+# #Easy #2023_12_23_Time_467_ms_(68.13%)_Space_60.7_MB_(15.08%)
+
+import pandas as pd
+
+def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
+    students.rename(columns={'id': 'student_id', 'first': 'first_name', 'last': 'last_name', 'age': 'age_in_years'}, inplace=True)
+    return students

src/main/java/g2801_2900/s2886_change_data_type/readme.md

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+2886\. Change Data Type
+
+Easy
+
+DataFrame `students` 
+
+    +-------------+--------+ 
+    | Column Name | Type   | 
+    +-------------+--------+ 
+    | student_id  | int    | 
+    | name        | object | 
+    | age         | int    | 
+    | grade       | float  | 
+    +-------------+--------+
+
+Write a solution to correct the errors:
+
+The `grade` column is stored as floats, convert it to integers.
+
+The result format is in the following example.
+
+**Example 1:** **Input:** DataFrame students: 
+
+    +------------+------+-----+-------+ 
+    | student_id | name | age | grade | 
+    +------------+------+-----+-------+ 
+    | 1          | Ava  | 6   | 73.0  | 
+    | 2          | Kate | 15  | 87.0  | 
+    +------------+------+-----+-------+
+
+**Output:** 
+
+    +------------+------+-----+-------+ 
+    | student_id | name | age | grade | 
+    +------------+------+-----+-------+ 
+    | 1          | Ava  | 6   | 73    | 
+    | 2          | Kate | 15  | 87    | 
+    +------------+------+-----+-------+
+
+**Explanation:** The data types of the column grade is converted to int.

src/main/java/g2801_2900/s2886_change_data_type/solution.py

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+# #Easy #2023_12_23_Time_421_ms_(94.57%)_Space_59.2_MB_(92.43%)
+
+import pandas as pd
+
+def changeDatatype(students: pd.DataFrame) -> pd.DataFrame:
+    students['grade'] = students['grade'].astype(int) 
+    return students

src/main/java/g2801_2900/s2887_fill_missing_data/readme.md

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+2887\. Fill Missing Data
+
+Easy
+
+DataFrame `products` 
+
+    +-------------+--------+ 
+    | Column Name | Type   | 
+    +-------------+--------+ 
+    | name        | object | 
+    | quantity    | int    | 
+    | price       | int    | 
+    +-------------+--------+
+
+Write a solution to fill in the missing value as <code>**0**</code> in the `quantity` column.
+
+The result format is in the following example.
+
+**Example 1:** 
+
+**Input:**
+
+    +-----------------+----------+-------+ 
+    | name            | quantity | price | 
+    +-----------------+----------+-------+ 
+    | Wristwatch      | None     | 135   | 
+    | WirelessEarbuds | None     | 821   | 
+    | GolfClubs       | 779      | 9319  | 
+    | Printer         | 849      | 3051  | 
+    +-----------------+----------+-------+
+
+**Output:** 
+
+    +-----------------+----------+-------+ 
+    | name            | quantity | price | 
+    +-----------------+----------+-------+ 
+    | Wristwatch      | 0        | 135   | 
+    | WirelessEarbuds | 0        | 821   | 
+    | GolfClubs       | 779      | 9319  | 
+    | Printer         | 849      | 3051  | 
+    +-----------------+----------+-------+
+
+**Explanation:** The quantity for Wristwatch and WirelessEarbuds are filled by 0.

src/main/java/g2801_2900/s2887_fill_missing_data/solution.py

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+# #Easy #2023_12_23_Time_404_ms_(97.11%)_Space_59.7_MB_(74.95%)
+
+import pandas as pd
+
+def fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:
+    products['quantity'].fillna(0, inplace=True)
+    return products

src/main/java/g2801_2900/s2888_reshape_data_concatenate/readme.md

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+2888\. Reshape Data: Concatenate
+
+Easy
+
+DataFrame `df1` 
+
+    +-------------+--------+ 
+    | Column Name | Type   | 
+    +-------------+--------+ 
+    | student_id  | int    | 
+    | name        | object | 
+    | age         | int    | 
+    +-------------+--------+ 
+
+DataFrame `df2` 
+
+    +-------------+--------+ 
+    | Column Name | Type   | 
+    +-------------+--------+ 
+    | student_id  | int    | 
+    | name        | object | 
+    | age         | int    | 
+    +-------------+--------+
+
+Write a solution to concatenate these two DataFrames **vertically** into one DataFrame.
+
+The result format is in the following example.
+
+**Example 1:**
+
+**Input: df1** 
+
+    +------------+---------+-----+ 
+    | student_id | name    | age | 
+    +------------+---------+-----+ 
+    | 1          | Mason   | 8   | 
+    | 2          | Ava     | 6   | 
+    | 3          | Taylor  | 15  | 
+    | 4          | Georgia | 17  | 
+    +------------+---------+-----+ 
+
+**df2** 
+
+    +------------+------+-----+ 
+    | student_id | name | age | 
+    +------------+------+-----+ 
+    | 5          | Leo  | 7   | 
+    | 6          | Alex | 7   | 
+    +------------+------+-----+
+
+**Output:** 
+
+    +------------+---------+-----+ 
+    | student_id | name    | age | 
+    +------------+---------+-----+ 
+    | 1          | Mason   | 8   | 
+    | 2          | Ava     | 6   | 
+    | 3          | Taylor  | 15  | 
+    | 4          | Georgia | 17  | 
+    | 5          | Leo     | 7   | 
+    | 6          | Alex    | 7   | 
+    +------------+---------+-----+
+
+**Explanation:** The two DataFramess are stacked vertically, and their rows are combined.

src/main/java/g2801_2900/s2888_reshape_data_concatenate/solution.py

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+# #Easy #2023_12_23_Time_441_ms_(96.26%)_Space_59_MB_(97.37%)
+
+import pandas as pd
+
+def concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:
+    return pd.concat([df1, df2], ignore_index=True)