Pivot Table and Melt in Pandas | 31/100 Days of Python Algo Trading

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Day 31: Pivot Table and Melt in Pandas

1. Which pandas method would you use to convert all strings in a Series to lowercase?

a) str.lower()

b) str.lowercase()

c) str.to_lower()

d) str.convert_lower()

2. How can you remove leading and trailing spaces from all strings in a pandas Series?

a) series.strip()

b) series.str.strip()

c) series.trim()

d) series.str.trim()

3. What is the purpose of the `str.contains` method in pandas?

a) To check if each string contains a pattern.

b) To replace parts of the string.

c) To split strings into lists.

d) To concatenate strings.

4. Which method converts a column to datetime in pandas?

a) pd.to_datetime()

b) pd.convert_datetime()

c) pd.to_date()

d) pd.datetime()

5. How would you extract the month from a datetime column in pandas?

a) df['date'].month

b) df['date'].dt.month

c) df['date'].extract_month()

d) df['date'].to_month()

6. How can you filter rows based on a specific date range in pandas?

a) df[df['date'].between(start_date, end_date)]

b) df[df['date'].range(start_date, end_date)]

c) df[df['date'].is_in_range(start_date, end_date)]

d) df[df['date'].loc[start_date:end_date]]

7. What is a primary benefit of using long data format?

a) It reduces memory usage.

b) It is easier to read.

c) It is more suitable for time series analysis.

d) It requires fewer columns.

8. Which pandas function can you use to transform wide data into long data?

a) pd.melt()

b) pd.pivot()

c) pd.transform()

d) pd.stack()

9. In the context of financial data, why might you prefer long format over wide format?

a) To reduce the file size.

b) To simplify the application of statistical models.

c) To improve data visualization.

d) To increase the number of columns.

10. What does the `pd.melt` function do in pandas?

a) Transforms a DataFrame from wide to long format.

b) Aggregates data based on specific columns.

c) Filters data based on conditions.

d) Joins multiple DataFrames.

11. Which parameters are essential in the `pd.melt` function to specify the identifier and value variables?

a) id_vars and value_vars

b) key_vars and val_vars

c) id_columns and val_columns

d) key_columns and value_columns

12. What is the purpose of a pivot table in pandas?

a) To filter rows based on conditions.

b) To reshape data by aggregating values.

c) To merge multiple DataFrames.

d) To split strings into columns.

13. How would you create a pivot table to summarize the average trading volume per stock and year?

a) pd.pivot_table(df, values='volume', index='stock', columns='year', aggfunc='mean')

b) df.pivot(values='volume', index='stock', columns='year')

c) df.groupby(['stock', 'year'])['volume'].mean()

d) df.pivot_table(values='volume', index='stock', columns='year', aggfunc='mean')

14. Which of the following is NOT an aggregation function in pandas?

a) mean

b) sum

c) count

d) split

15. How can you apply multiple aggregation functions to a groupby object?

a) df.groupby('column').aggregate(['mean', 'sum'])

b) df.groupby('column').apply(['mean', 'sum'])

c) df.groupby('column').transform(['mean', 'sum'])

d) df.groupby('column').agg(['mean', 'sum'])

16. Which pandas function would you use to fill missing values in a DataFrame?

a) fillna()

b) dropna()

c) isna()

d) notna()

17. What does the `drop_duplicates` function do in a DataFrame?

a) Removes columns with duplicate names.

b) Removes rows with duplicate values.

c) Sorts the DataFrame by a specific column.

d) Filters rows based on conditions.

18. Which function would you use to extract the year from a datetime column in pandas?

a) df['date'].year

b) df['date'].dt.year

c) df['date'].get_year()

d) df['date'].to_year()

19. How can you resample a time series DataFrame to monthly frequency?

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

b) df.groupby('M').mean()

c) df.set_freq('M').mean()

d) df.resample('month').mean()

20. Which method would you use to shift all dates in a datetime column by one month?

a) df['date'] + pd.DateOffset(months=1)

b) df['date'].shift(1, freq='M')

c) df['date'].add_months(1)

d) df['date'] + pd.Timedelta('1M')

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