Vectorized String Operations | 32/100 Days of Python Algo Trading

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Day 32: Vectorized String Operations

1. Which vectorized string operation would be most efficient to extract domain names from email addresses in an algorithmic trading dataset?

A) df['email'].str.split('@').str[1]

B) df['email'].apply(lambda x: x.split('@')[1])

C) df['email'].str.extract(r'@(.+)$')[0]

D) df['email'].map(lambda x: x.split('@')[1])

2. How can you convert a 'date' column in a DataFrame to datetime in pandas for efficient time-series analysis in trading?

A) pd.to_datetime(df['date'])

B) pd.to_datetime(df, format='%Y-%m-%d')

C) df['date'].astype('datetime64')

D) df['date'] = df['date'].apply(pd.to_datetime)

3. What is the key advantage of using long data format over wide data format in pandas when dealing with time-series trading data?

A) It reduces memory usage.

B) It is easier to filter specific dates.

C) It allows for more complex transformations.

D) It supports hierarchical indexing.

4. Which function would you use to transform a wide DataFrame into a long DataFrame suitable for analysis in pandas?

A) pd.melt(df)

B) df.pivot()

C) pd.pivot_table(df)

D) pd.pivot(df)

5. When creating a pivot table in pandas for trading data, what parameter allows you to add multiple aggregation functions?

A) values

B) aggfunc

C) index

D) columns

6. How can you use the agg function to apply different aggregations to different columns in a trading DataFrame?

A) df.agg({'column1': 'sum', 'column2': 'mean'})

B) df.agg(['sum', 'mean'])

C) df.groupby('column').agg({'column1': 'sum', 'column2': 'mean'})

D) df.agg({'column1': 'sum', 'column2': 'mean', 'column3': 'count'})

7. What is the benefit of using vectorized string operations in pandas when processing large datasets in algorithmic trading?

A) They are faster than looping through rows.

B) They provide better readability.

C) They integrate seamlessly with numpy functions.

D) They can be used in lambda functions.

8. Which common function in pandas can be used to handle missing data in a DataFrame containing trading signals?

A) df.fillna(0)

B) df.replace(np.nan, 0)

C) df.interpolate()

D) df.dropna(how='all')

9. How can you ensure that the datetime conversion in pandas is done considering the time zone information for trading data?

A) df['date'] = pd.to_datetime(df['date'], utc=True)

B) df['date'] = pd.to_datetime(df['date']).dt.tz_localize('UTC')

C) df['date'] = pd.to_datetime(df['date']).dt.tz_convert('UTC')

D) df['date'] = df['date'].apply(pd.to_datetime).dt.tz_localize('UTC')

10. What is the purpose of using the 'pivot_table' function in pandas for algorithmic trading data analysis?

A) To reshape data for plotting.

B) To group data for summary statistics.

C) To create pivot tables for multi-dimensional analysis.

D) To reshape and summarize large datasets.

11. Which vectorized string method would you use to check if each string in a Series of trading symbols starts with a specific prefix?

A) df['symbol'].str.startswith('AAPL')

B) df['symbol'].apply(lambda x: x.startswith('AAPL'))

C) df['symbol'].str.endswith('AAPL')

D) df['symbol'].str.match('^AAPL')

12. What is the difference between 'melt' and 'pivot' in pandas when restructuring trading data?

A) melt is for converting wide to long, pivot is for converting long to wide.

B) melt is for converting long to wide, pivot is for converting wide to long.

C) melt is for aggregating data, pivot is for filtering data.

D) melt is for summarizing data, pivot is for grouping data.

13. How can you calculate the daily returns from a time-series of stock prices using pandas?

A) df['daily_return'] = df['price'].pct_change()

B) df['daily_return'] = (df['price'] - df['price'].shift(1)) / df['price'].shift(1)

C) df['daily_return'] = df['price'].diff() / df['price'].shift(1)

D) df['daily_return'] = (df['price'] / df['price'].shift(1)) - 1

14. What is a practical use of the 'apply' function in pandas when dealing with algorithmic trading strategies?

A) To apply a custom function to each row.

B) To apply a custom function to each column.

C) To apply a predefined function to the DataFrame.

D) To apply a custom function to each element.

15. Why is it important to handle time zones correctly when dealing with datetime data in algorithmic trading?

A) To ensure correct alignment with market hours.

B) To manage daylight saving time transitions.

C) To ensure data consistency across different regions.

D) To enable comparison across different time zones.

16. Which pandas function allows you to convert a time-series DataFrame from wide format to long format?

A) df.melt()

B) df.pivot()

C) pd.wide_to_long()

D) df.pivot_table()

17. How do you handle large datasets with missing values in algorithmic trading using pandas?

A) df.dropna()

B) df.fillna(method='ffill')

C) df.dropna(axis=1)

D) df.fillna(axis=1, value=0)

18. What method in pandas allows you to perform element-wise string matching using regular expressions in a trading dataset?

A) df['column'].str.match('regex')

B) df['column'].str.contains('regex')

C) df['column'].str.find('regex')

D) df['column'].str.replace('regex', '')

19. How can you use pandas to group and aggregate trading data by specific time intervals?

A) df.groupby(pd.Grouper(key='date', freq='M')).sum()

B) df.groupby('date').sum()

C) df.groupby(df['date'].dt.to_period('M')).sum()

D) df.groupby(pd.Grouper(freq='M')).sum()

20. Which function in pandas allows you to resample time-series trading data?

A) df.resample('D').sum()

B) df.resample('M').mean()

C) df.resample('W').sum()

D) df.resample('H').sum()

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