End to End Stock Analysis Pandas Dataframes In Python| 26/100 Days of Python Algo Trading

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Day 26: End to End Stock Analysis Pandas Dataframes In Python

1. What does the term "OHLC" refer to in financial datasets?

A) Order, Hold, Leverage, Contract

B) Open, High, Low, Close

C) Offer, Hedge, Lend, Collateral

D) Operate, Handle, Load, Compute

2. Which DataFrame function would you use to find the highest value for each stock in a period?

A) df.max()

B) df.high()

C) df.top()

D) df.peak()

3. How can you calculate the overall return from the start to the end of a dataset for each stock?

A) (df['Close'].iloc[-1] - df['Open'].iloc[0]) / df['Open'].iloc[0]

B) (df['Close'] - df['Open']) / df['Open']

C) df['Return'] = (df['Close'].shift(-1) - df['Open']) / df['Open']

D) df['Return'] = df['Close'] / df['Open'] - 1

4. What Pandas function is used to count instances where daily price changes exceed 5% for each stock?

A) df['Price Change'] = df['Close'].pct_change(); df[df['Price Change'] > 0.05].count()

B) df[df['Close'].diff() / df['Close'] > 0.05].count()

C) df[df['Close'] / df['Open'] - 1 > 0.05].count()

D) df['Price Change'] = (df['Close'] - df['Open']) / df['Open']; df[df['Price Change'] > 0.05].count()

5. To analyze price increases in the first quarter, which method is most appropriate?

A) df['Q1 Increase'] = df[df.index.quarter == 1]['Close'].diff() > 0; df['Q1 Increase'].sum()

B) df[df.index.month <= 3]['Close'].apply(lambda x: x > x.shift()).sum()

C) df['Q1'] = df[df.index.month <= 3]; df['Q1'].diff().gt(0).sum()

D) df[df.index.month <= 3]['Close'].diff().gt(0).sum()

6. Which method best summarizes the basic statistics of a financial dataset?

A) df.describe()

B) df.summary()

C) df.info()

D) df.stats()

7. How do you select data for a specific stock from a multi-stock DataFrame?

A) df[df['Stock'] == 'AAPL']

B) df.loc['AAPL']

C) df.get('AAPL')

D) df.select('AAPL')

8. Which function would you use to compute the moving average of closing prices over a 10-day period?

A) df['Close'].rolling(10).mean()

B) df['Close'].moving_average(10)

C) df['Close'].average(10)

D) df['Close'].smooth(10)

9. How can you filter out entries from a DataFrame where the closing price is below the opening price?

A) df[df['Close'] > df['Open']]

B) df[df['Close'] < df['Open']]

C) df.query('Close < Open')

D) Both B and C

10. What method should be used to concatenate two DataFrames containing stock data vertically?

A) pd.concat([df1, df2], axis=0)

B) pd.join([df1, df2])

C) pd.merge([df1, df2])

D) df1.append(df2)

11. Which line of code correctly calculates the logarithmic return of stock prices?

A) np.log(df['Close'] / df['Close'].shift(1))

B) df['Close'].log_return()

C) df['Log Return'] = log(df['Close'] / df['Open'])

D) df.apply(lambda x: np.log(x['Close']/x['Open']))

12. To determine the number of days the closing price was higher than the opening, which method is best?

A) df[df['Close'] > df['Open']].count()

B) (df['Close'] > df['Open']).sum()

C) df['High Days'] = df['Close'] > df['Open']

D) Both A and B

13. What would be the most efficient way to apply a custom function to each element in a DataFrame column?

A) df['Column'].apply(custom_function)

B) custom_function(df['Column'])

C) df['Column'].map(custom_function)

D) Both A and C

14. How can you ensure all DataFrame operations skip missing values?

A) df.dropna(inplace=True)

B) Using the parameter skipna=True in mathematical functions.

C) df.fillna(0)

D) Both B and C

15. Which method allows the direct modification of DataFrame entries based on conditions?

A) df.loc[df['Close'] < df['Open'], 'Flag'] = 'Loss'

B) df.set_value('Loss', df['Close'] < df['Open'])

C) df.update(df['Close'] < df['Open'])

D) df.apply(lambda x: 'Loss' if x['Close'] < x['Open'] else x)

16. How do you add a column that shows the difference between the high and low prices?

A) df['Range'] = df['High'] - df['Low']

B) df.add_column('Range', df['High'] - df['Low'])

C) df['Range'].calc(df['High'], df['Low'])

D) df.calculate('Range', df['High'] - df['Low'])

17. What function would you use to replace all NaN values in a DataFrame with the mean of their column?

A) df.fillna(df.mean())

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

C) df['Column'].fillna(df['Column'].mean())

D) Both A and C

18. Which function allows for condition-based merging of two DataFrame objects?

A) pd.concat([df1, df2], on='key')

B) pd.merge(df1, df2, on='key')

C) df1.join(df2, on='key')

D) df1.combine(df2, on='key')

19. What is the best method to visualize the distribution of stock returns in a DataFrame?

A) df['Return'].hist()

B) df.plot(kind='hist', column='Return')

C) df['Return'].plot.hist()

D) Both A and C

20. How can you create a multi-level index in a DataFrame to better organize hierarchical data?

A) df.set_index(['Level1', 'Level2'])

B) df.multi_index(['Level1', 'Level2'])

C) df.index.set_levels(['Level1', 'Level2'])

D) pd.MultiIndex.from_arrays([df['Level1'], df['Level2']])

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