For the given DataFrame 'df', complete the Python code appropriately:

```python
import pandas as pd
import numpy as np

data = {
'Name': ['Rohan', 'Shreya', 'Kavya', 'Vansh', 'Kushal'],
'Age': [25, 30, 35, np.nan, 40],
'Salary': [50000, 60000, np.nan, 80000, 90000],
'Department': ['HR', 'IT', 'IT', 'Finance', 'HR']
}

df = pd.DataFrame(data)
```

Based on the above, complete the following sentences:

(a) To create a new column "Experience" with random values between 1 and 10, we use `...................`

(b) To sort the DataFrame by "Salary" in descending order, we use `...................`

(c) To select only the columns "Name" and "Salary", we use `...................`

(d) To check for duplicate rows in the DataFrame, we use `...................`

Question

19-01-2024
Computers and Technology

High School

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For the given DataFrame 'df', complete the Python code appropriately:

```python
import pandas as pd
import numpy as np

data = {
'Name': ['Rohan', 'Shreya', 'Kavya', 'Vansh', 'Kushal'],
'Age': [25, 30, 35, np.nan, 40],
'Salary': [50000, 60000, np.nan, 80000, 90000],
'Department': ['HR', 'IT', 'IT', 'Finance', 'HR']
}

df = pd.DataFrame(data)
```

Based on the above, complete the following sentences:

(a) To create a new column "Experience" with random values between 1 and 10, we use `...................`

(b) To sort the DataFrame by "Salary" in descending order, we use `...................`

(c) To select only the columns "Name" and "Salary", we use `...................`

(d) To check for duplicate rows in the DataFrame, we use `...................`

Answer :

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JessicaJessy JessicaJessy · Answer 1 · 2025-01-24T04:49:45-05:00

Let's address each part of the question step by step using the Pandas library in Python:

(a) To create a new column "Experience" with random values between 1 and 10, we use:

df['Experience'] = np.random.randint(1, 11, size=len(df))

Explanation: Here, np.random.randint(1, 11, size=len(df)) generates random integers between 1 and 10 for each row in the DataFrame. The parameter size=len(df) ensures that the number of random values matches the number of rows in df.

(b) To sort the DataFrame by "Salary" in descending order, we use:

df.sort_values(by='Salary', ascending=False, inplace=True)

Explanation: The sort_values function sorts the DataFrame based on the "Salary" column. Setting ascending=False arranges the values in descending order. Using inplace=True alters the original DataFrame instead of creating a new sorted DataFrame.

(c) To select only the columns "Name" and "Salary", we use:

df[['Name', 'Salary']]

Explanation: To select specific columns in a DataFrame, use double brackets [[ ]], where the inner list contains the names of the columns you want to retain.

(d) To check for duplicate rows in the DataFrame, we use:

df.duplicated()

Explanation: The duplicated() function returns a boolean Series denoting the duplicate rows. True indicates a duplicate row, and False signifies an original row.

These commands are useful tools when performing data manipulation and cleaning tasks in Python, particularly when using the Pandas library. Each function has its own unique role in organizing and processing data to gain meaningful insights.