Descriptive Statistics Part- 2 | 37/100 Days of Python Algo Trading

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Day 37: Descriptive Statistics Part- 2

1. What is the primary step in addressing quality issues in a smartphone dataset used for algorithmic trading?

A) Identifying and handling missing values

B) Standardizing data formats

C) Verifying data accuracy

D) Removing duplicate rows

2. Which of the following is an example of a tidiness issue in a smartphone dataset?

A) Multiple variables stored in one column

B) Incorrect data types

C) Duplicate rows

D) Missing values

3. How can missing values in a smartphone dataset be effectively handled?

A) Using df.fillna() function

B) Removing rows with missing values

C) Using df.dropna() function

D) Using df.interpolate() function

4. What is the best way to detect duplicate entries in a smartphone dataset?

A) Using the df.duplicated() function

B) Using the df.isna() function

C) Using the df.unique() function

D) Using the df.dropna() function

5. Which pandas function can be used to fill missing values with the mean in a smartphone dataset?

A) df.fillna(df.mean())

B) df.mean()

C) df.fillna(df.median())

D) df.interpolate(df.mean())

6. How do you handle inconsistent data entries in a smartphone dataset?

A) Using data standardization techniques

B) By normalizing the data entries

C) By correcting the data entries manually

D) By using data transformation techniques

7. Which data quality issue arises when there are multiple representations of the same data?

A) Data redundancy

B) Data duplication

C) Data inconsistency

D) Data validity

8. What is the role of the 'melt' function in addressing tidiness issues in a dataset?

A) To transform data from wide format to long format

B) To create a summary of the dataset

C) To filter the data based on a condition

D) To create a pivot table for analysis

9. How can you check for outliers in a numerical column of a smartphone dataset?

A) Using statistical methods like Z-score

B) Using visual inspection

C) Using machine learning models

D) Using correlation analysis

10. What is a common method to handle outliers in a smartphone dataset?

A) Removing or transforming the outlier values

B) Ignoring the outlier values

C) Keeping the outlier values as they are

D) Converting outliers to missing values

11. Which function in pandas is used to remove duplicate rows from a smartphone dataset?

A) df.drop_duplicates()

B) df.remove_duplicates()

C) df.dropna()

D) df.clean_duplicates()

12. What is the purpose of the 'pivot_table' function in addressing tidiness issues?

A) To reshape data for better analysis

B) To combine data from different sources

C) To sort data in ascending order

D) To clean the data

13. How can you ensure the accuracy of the data in a smartphone dataset?

A) By validating against a trusted source

B) By cross-referencing with other datasets

C) By using statistical methods

D) By performing data validation checks

14. What is the significance of the 'astype()' function in data cleaning?

A) To change the data type of a column

B) To remove missing values

C) To fill missing values

D) To aggregate data

15. How do you address the issue of inconsistent units in a smartphone dataset?

A) By standardizing the units across the dataset

B) By converting all units to a standard metric

C) By removing all units

D) By converting units to the same scale

16. Which method is used to merge two DataFrames containing smartphone data on a common column?

A) Using the merge() function

B) Using the join() function

C) Using the concat() function

D) Using the append() function

17. How can you handle missing categorical data in a smartphone dataset?

A) By filling with the most frequent value

B) By using a placeholder value

C) By using the median of the column

D) By using the mode of the column

18. What is a tidiness issue that involves multiple variables stored in one column?

A) Multiple variables stored in one column

B) Incorrect data types

C) Duplicate rows

D) Missing values

19. How do you transform a wide-format DataFrame to long-format in pandas?

A) Using the melt() function

B) Using the pivot() function

C) Using the join() function

D) Using the pivot_table() function

20. What is the best approach to validate data after cleaning in a smartphone dataset?

A) Performing exploratory data analysis (EDA)

B) Conducting a data quality audit

C) Verifying against the original data source

D) Creating a data validation report

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