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Data Wrangling

Cleaning and transforming data

Data wrangling is the process of cleaning and transforming raw data into a format that is suitable for analysis. It involves tasks such as cleaning and filtering data, addressing missing or incorrect values, and transforming data into a more usable format.

5 minutes 5 Questions

Data Wrangling is the process of transforming and mapping raw data into a more useful format for analysis. As a fundamental step in the data science workflow, data wrangling typically consumes 60-80% of a data scientist's time and effort. The process begins with data discovery, where you explore a…

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Big Data Scientist - Data Wrangling Example Questions

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Question 1

What is the purpose of the np.where() function in NumPy?

np.where() function is used to find the cumulative sum of elements in a NumPy array. np.where() function is used to sort a NumPy array in ascending order. np.where() function is used to check if two NumPy arrays are equal. np.where() function is used to calculate the mean of elements in a NumPy array. np.where() function is used to return elements from one of the two passed arrays based on a condition. np.where() function is used to delete elements from a NumPy array.

Correct Answer: np.where() function is used to return elements from one of the two passed arrays based on a condition.

np.where() is a powerful function in NumPy that helps to return elements from one of the two passed arrays based on a condition. The function takes three arguments: 'condition', 'x', and 'y'. The 'condition' argument is a boolean array that specifies which elements to choose from either 'x' or 'y'. If the element in the 'condition' array is True, the corresponding element from 'x' is selected, otherwise, the corresponding element from 'y' is selected. This function is commonly used in data cleaning and preprocessing operations.

Question 2

What is the difference between mean and median?

Mean is the middle value in a sorted list of values, while median is the average of all values. Mean is the highest value, while median is the lowest value in a list of values. Mean is the sum of all values, while median is the difference between the highest and lowest values. Mean is the lowest value, while median is the highest value in a list of values. Mean is the average of all values, while median is the middle value in a sorted list of values. Mean and median are the same thing.

Correct Answer: Mean is the average of all values, while median is the middle value in a sorted list of values.

The correct difference between mean and median is that mean is the average of all values, while median is the middle value in a sorted list of values. The other options are incorrect.

Question 3

What is the purpose of the pivot_table() function in pandas?

To rename columns in a DataFrame based on a specified mapping. To merge two DataFrames into one based on a specified column. To sort a DataFrame based on a specified column. To group rows in a DataFrame together based on a specified column. To create a spreadsheet-style pivot table as a DataFrame. To filter rows from a DataFrame based on a specified condition.

Correct Answer: To create a spreadsheet-style pivot table as a DataFrame.

The pivot_table() function in pandas is used to create a spreadsheet-style pivot table as a DataFrame. This can be useful in situations where the data needs to be summarized or aggregated by one or more variables. The function cannot be used to filter, sort, merge, or rename columns in a DataFrame.

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