Financial data analysis pattern in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load financial data from a CSV file into a DataFrame.

Data Analysis Python

import pandas as pd

data = pd.[1]('financial_data.csv')

Drag options to blanks, or click blank then click option'

Ato_csv

Bread_csv

Cread_excel

Dread_json

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'read_excel' when the file is a CSV.

Using 'to_csv' which is for saving, not loading.

The read_csv function loads CSV files into a DataFrame, which is perfect for financial data stored in CSV format.

2fill in blank

medium

Complete the code to calculate the daily returns from the 'Close' price column.

Data Analysis Python

data['Daily_Return'] = data['Close'].[1]()

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Apct_change

Bdiff

Ccumsum

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'diff' which calculates absolute difference, not percentage.

Using 'cumsum' which accumulates sums, not returns.

The pct_change() function calculates the percentage change between the current and prior element, which is used to find daily returns.

3fill in blank

hard

Fix the error in the code to filter rows where the 'Volume' is greater than 1 million.

Data Analysis Python

high_volume = data[data['Volume'] [1] 1000000]

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A==

B<

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '<=' which select smaller volumes.

Using '==' which selects only volumes exactly equal to 1 million.

The '>' operator filters rows where 'Volume' is greater than 1,000,000, which matches the requirement.

4fill in blank

hard

Fill both blanks to create a dictionary of average closing prices for each stock symbol.

Data Analysis Python

avg_close = {symbol: data[data['Symbol'] == symbol]['[1]'].[2]() for symbol in data['Symbol'].unique()}

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AClose

BVolume

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Volume' instead of 'Close' for price data.

Using 'sum' instead of 'mean' for average.

We select the 'Close' prices and calculate their mean to get the average closing price per symbol.

5fill in blank

hard

Fill all three blanks to create a filtered DataFrame with stocks having positive daily returns and volume above 500,000.

Data Analysis Python

filtered_data = data[(data['Daily_Return'] [1] 0) & (data['Volume'] [2] 500000)]
result = filtered_data[['[3]', 'Daily_Return', 'Volume']]

Drag options to blanks, or click blank then click option'

A>

B>=

CSymbol

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering positive returns.

Selecting wrong columns in the final DataFrame.

We filter for daily returns greater than 0 and volume greater than 500,000, then select the 'Symbol' column along with returns and volume.