Pandas Series | 23/100 Days of Python Algo Trading

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Day 23: Pandas Series

1. What is a Pandas Series?

A) A multi-dimensional data structure for handling large data sets.

B) A one-dimensional array-like object capable of holding any data type.

C) A specialized library used exclusively for time series analysis.

D) A type of Python dictionary optimized for numerical data.

2. How does Pandas support algorithmic trading?

A) By providing tools for high-frequency trading algorithms.

B) By offering data structures that simplify data analysis and manipulation.

C) By directly interfacing with financial markets for trading.

D) By optimizing execution speeds to millisecond latency.

3. Which of the following is a key advantage of using Pandas over NumPy in trading?

A) Pandas supports high-performance matrix operations.

B) Pandas provides better support for labeled data and time series.

C) Pandas exclusively uses less memory than NumPy.

D) Pandas executes computations faster than NumPy.

4. How can you create a Pandas Series from a list?

A) pd.Series(list)

B) pd.Data(list)

C) Series.create(list)

D) Series(list)

5. What attribute would you access to get the data type of a Pandas Series?

A) .type

B) .dtype

C) .datatype

D) .datakind

6. How do you select the first five elements of a Pandas Series?

A) series[:5]

B) series(0, 5)

C) series.select(5)

D) series.first(5)

7. In the context of algorithmic trading, what is a common use of Pandas Series?

A) Storing and manipulating historical stock prices.

B) Executing trades on a stock exchange.

C) Generating real-time trading alerts.

D) Encrypting sensitive financial data.

8. What Pandas functionality allows merging of time-series data based on timestamp alignment?

A) merge()

B) join()

C) concat()

D) align()

9. How would you change the index of a Pandas Series?

A) series.set_index(new_index)

B) series.index = new_index

C) series.reindex(new_index)

D) series.index(new_index)

10. Which Pandas method is used for deleting items from a Series based on a condition?

A) series.drop(labels)

B) series.remove(condition)

C) series.delete(condition)

D) series[condition].drop()

11. What advantage does Pandas have over NumPy for time-series data specifically?

A) Pandas can only perform linear algebra operations.

B) Pandas provides extensive time-series functionality not available in NumPy.

C) Pandas supports three-dimensional arrays.

D) Pandas computations are faster than NumPy.

12. How would you apply a logarithmic transformation to each element of a Pandas Series?

A) np.log(series)

B) series.apply(np.log)

C) series.log()

D) Both A and B

13. What method adds data to an existing Pandas Series?

A) series.append(data)

B) series.add(data)

C) series.concat(data)

D) series.extend(data)

14. Which function would you use to calculate the cumulative return of a stock price series?

A) series.cumsum()

B) series.cumprod()

C) np.cumulative_return(series)

D) series.cumulative()

15. What is the result of using the describe() method on a Pandas Series?

A) It provides a count of non-null elements.

B) It returns the mean and median of the Series.

C) It gives a full summary including count, mean, std, min, quartiles, and max.

D) It plots a graph of the Series data.

16. How can you ensure that your series handles missing data during computations?

A) By using the fillna() method before computations.

B) By ignoring missing data using the skipna=True parameter in computational methods.

C) By removing rows with missing data using the dropna() method.

D) Both A and C

17. What method would you use to sort a Pandas Series by its values?

A) series.sort_values()

B) series.sort()

C) series.order()

D) series.sort_by_values()

18. Which method is used to filter a Series based on a boolean condition?

A) series.filter(condition)

B) series[condition]

C) series.where(condition)

D) Both B and C

19. How do you convert a Pandas Series to a Python list?

A) list(series)

B) series.to_list()

C) series.tolist()

D) Both B and C

20. What is the primary function of the pct_change() method in a Pandas Series?

A) To calculate the percent change between consecutive elements in a Series.

B) To change percentage formats.

C) To perform a percentage rank computation.

D) To adjust the percentage scale of series data.

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