Descriptive Statistics | 36/100 Days of Python Algo Trading

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Day 36: Descriptive Statistics

1. What is the primary purpose of data assessing in algorithmic trading?

A) To identify and correct errors in the dataset

B) To transform data into a usable format

C) To visualize data trends and patterns

D) To integrate data from multiple sources

2. Which type of unclean data is characterized by incorrect or inconsistent data values?

A) Incorrect data

B) Incomplete data

C) Duplicate data

D) Inconsistent data

3. What is the best method to summarize data for a quick overview in pandas?

A) df.describe()

B) df.head()

C) df.info()

D) df.summarize()

4. What are the two main types of data assessment?

A) Manual and automated

B) Qualitative and quantitative

C) Internal and external

D) Static and dynamic

5. How does manual data assessment differ from automatic data assessment?

A) Manual assessment is performed by humans, automatic is done by software

B) Manual assessment uses data visualizations, automatic uses algorithms

C) Manual assessment is quicker, automatic is more accurate

D) Manual assessment focuses on outliers, automatic focuses on patterns

6. Which data quality dimension is most concerned with the accuracy of trading signals?

A) Accuracy

B) Completeness

C) Timeliness

D) Consistency

7. What is the first step in the data cleaning process?

A) Identifying and handling missing data

B) Removing duplicate entries

C) Normalizing data

D) Aggregating data

8. Which pandas function is commonly used to identify missing values in a DataFrame?

A) df.isnull()

B) df.isna()

C) df.isnan()

D) df.null()

9. How can you handle duplicate data entries in a trading dataset using pandas?

A) df.drop_duplicates()

B) df.remove_duplicates()

C) df.clean_duplicates()

D) df.remove_duplicates()

10. What is the purpose of using the 'describe()' function in pandas?

A) To provide a summary of the dataset

B) To visualize the distribution of data

C) To provide detailed information on missing values

D) To find correlations between variables

11. Which type of unclean data involves data that is duplicated or redundant?

A) Duplicate data

B) Incorrect data

C) Inconsistent data

D) Outdated data

12. How can you automate the assessment of data quality in a large trading dataset?

A) Using data profiling tools

B) By visual inspection

C) Using machine learning algorithms

D) Using data integration tools

13. Which pandas function can be used to replace missing values with a specific value?

A) df.fillna(value)

B) df.replace_na(value)

C) df.na_fill(value)

D) df.fillna_missing(value)

14. What does the 'info()' function in pandas provide about a dataset?

A) Summary of data types and non-null values

B) Summary of numeric statistics

C) Summary of data dimensions

D) Summary of data distribution

15. Why is it important to assess data quality before using it for algorithmic trading?

A) To ensure the data is suitable for analysis

B) To determine the relevance of the data

C) To assess the quality of the data

D) To improve the accuracy of the models

16. Which data quality dimension addresses the completeness of trading data?

A) Completeness

B) Accuracy

C) Timeliness

D) Consistency

17. How can you assess the consistency of data in a trading dataset?

A) By checking for inconsistent data entries

B) By comparing data from different sources

C) By running data validation checks

D) By performing data type checks

18. What is the role of exploratory data analysis (EDA) in data assessing?

A) To understand the data before analysis

B) To identify patterns and relationships

C) To summarize the main characteristics of the data

D) To automate the data cleaning process

19. How can you detect outliers in a trading dataset using pandas?

A) Using statistical methods like z-score

B) Using visualizations like box plots

C) Using machine learning models

D) Using correlation analysis

20. What is the benefit of using the 'dropna()' function in data cleaning?

A) To remove rows with missing values

B) To replace missing values with the mean

C) To handle missing values by interpolation

D) To remove rows with duplicated values

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