Important DataFrame Methods | 25/100 Days of Python Algo Trading

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Day 25: Important DataFrame Methods

1. What does OHLC stand for in financial data analysis?

A) Open, High, Low, Close

B) Offer, Halt, Limit, Close

C) Outlay, Hold, Leverage, Capital

D) Order, Hit, Lag, Clear

2. How can you create a DataFrame in pandas from a dictionary?

A) pd.DataFrame(dict)

B) pd.create_dataframe(dict)

C) DataFrame.init(dict)

D) new DataFrame(dict)

3. Which DataFrame attribute would you use to get the data types of each column?

A) .types

B) .attributes

C) .dtypes

D) .describes()

4. What method is used to describe the statistical summary of a DataFrame?

A) df.describe()

B) df.summary()

C) df.statistics()

D) df.info()

5. How do you select a single column from a DataFrame by column name?

A) df('column_name')

B) df[column_name]

C) df.column_name

D) Both B and C

6. What pandas function is used to select rows from a DataFrame based on conditions?

A) df.loc[condition]

B) df.iloc[condition]

C) df.where[condition]

D) df.select[condition]

7. How can you select rows and specific columns in a DataFrame?

A) df.loc[rows, columns]

B) df.iloc[rows, columns]

C) df.get[rows, columns]

D) Both A and B

8. What is the correct way to add a new column to a DataFrame?

A) df['new_column'] = values

B) df.add_column('new_column', values)

C) df.new_column = values

D) df.insert('new_column', values)

9. Which method can you use to compute the mean of a numeric column in a DataFrame?

A) df['column'].average()

B) df['column'].mean()

C) df.mean('column')

D) df.calculate('column', 'mean')

10. How do you select rows from a DataFrame based on index positions?

A) df.at[1:5]

B) df.loc[1:5]

C) df.iloc[1:5]

D) df.get(1:5)

11. Which method would you use to change the index of a DataFrame?

A) df.set_index('new_index')

B) df.index = 'new_index'

C) df.reindex('new_index')

D) df.change_index('new_index')

12. What pandas method would you use to drop columns from a DataFrame?

A) df.drop(columns=['column_name'])

B) df.remove(['column_name'])

C) df.delete(['column_name'])

D) df.cut(['column_name'])

13. How can you combine data from two DataFrames horizontally?

A) pd.merge(df1, df2)

B) pd.join(df1, df2)

C) pd.concatenate([df1, df2], axis=1)

D) df1.append(df2)

14. What function would you use to save a DataFrame to a CSV file?

A) df.to_csv('filename.csv')

B) df.save('filename.csv')

C) df.export('filename.csv')

D) df.write('filename.csv')

15. Which method is used to calculate the correlation between columns in a DataFrame?

A) df.correlate()

B) df.corr()

C) df.correlation()

D) df.compute_correlation()

16. What is the effect of df.fillna(method='bfill') on a DataFrame?

A) Fills null values with the value before them in the DataFrame.

B) Fills null values with the value after them in the DataFrame.

C) Deletes all rows that contain a null value.

D) Fills null values with the average of the column.

17. How would you filter a DataFrame to show only the records where the column 'A' is greater than 10?

A) df[df.A > 10]

B) df.query('A > 10')

C) Both A and B

D) df.filter('A > 10')

18. What attribute would you use to retrieve the DataFrame index?

A) df.index

B) df.columns

C) df.rows

D) df.labels

19. Which method would you use to change the data type of a DataFrame column from int to float?

A) df['column'].astype(float)

B) df['column'].to_type(float)

C) df['column'].cast(float)

D) df['column'].convert(float)

20. How can you retrieve a random sample of rows from a DataFrame?

A) df.sample(n=5)

B) df.random(5)

C) df.get_random(5)

D) df.select_random(5)

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