Financial data analysis pattern in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Calculate Moving Average of Stock Prices

Given daily closing prices of a stock, what is the output of the following code that calculates a 3-day moving average?

Data Analysis Python

import pandas as pd
prices = pd.Series([100, 102, 101, 105, 107])
moving_avg = prices.rolling(window=3).mean()
print(moving_avg.tolist())

A [nan, nan, 101.0, 103.0, 106.0]

B [100, 102, 101, 105, 107]

C [100.0, 101.0, 101.0, 102.66666666666667, 104.33333333333333]

D [nan, nan, 101.0, 102.66666666666667, 104.33333333333333]

💡 Hint

Think about how rolling averages handle the first few values when the window is not full.

The rolling function with window=3 calculates the mean of the current and previous two values. The first two values are NaN because there are not enough data points to fill the window.

❓ data_output

intermediate

Identify the Date with Highest Daily Return

Given a DataFrame of stock closing prices indexed by date, which date has the highest daily return calculated by percentage change?

Data Analysis Python

import pandas as pd

data = {'Close': [100, 105, 103, 110, 108]}
dates = pd.to_datetime(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'])
df = pd.DataFrame(data, index=dates)
df['Return'] = df['Close'].pct_change()
max_return_date = df['Return'].idxmax()
print(max_return_date.strftime('%Y-%m-%d'))

A 2024-01-04

B 2024-01-02

C 2024-01-05

D 2024-01-03

💡 Hint

Calculate percentage change between consecutive days and find the max.

The highest daily return is on 2024-01-04 because the price jumped from 103 to 110, which is the largest percentage increase.

❓ visualization

advanced

Plot Cumulative Returns Over Time

Which option produces the correct plot of cumulative returns from daily returns in a DataFrame?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

returns = pd.Series([0.01, -0.005, 0.02, -0.01, 0.015])
cum_returns = (1 + returns).cumprod() - 1
plt.plot(cum_returns)
plt.title('Cumulative Returns')
plt.xlabel('Days')
plt.ylabel('Cumulative Return')
plt.show()

A Line plot starting near 0 and ending around 0.03 showing cumulative growth

B Bar plot showing daily returns as bars

C Scatter plot of daily returns with no cumulative calculation

D Pie chart showing proportion of positive vs negative returns

💡 Hint

Cumulative returns multiply daily returns over time, showing growth trend.

The code calculates cumulative returns and plots them as a line chart showing growth over days.

🔧 Debug

advanced

Identify the Error in Volatility Calculation

What error does the following code raise when calculating volatility (standard deviation of returns)?

Data Analysis Python

import pandas as pd
prices = pd.Series([100, 102, 101, 105])
returns = prices.pct_change()
volatility = returns.std(ddof=0)
print(volatility.values)

A ValueError: std() got an unexpected keyword argument 'ddof'

B TypeError: pct_change() missing required argument

C AttributeError: 'float' object has no attribute 'values'

D No error, prints the volatility value

💡 Hint

Check the type of the volatility variable before accessing .values.

The std() method returns a float, which does not have a .values attribute, causing an AttributeError.

🚀 Application

expert

Determine the Number of Trading Days with Positive Returns

Given a DataFrame of daily closing prices, how many days have positive returns?

Data Analysis Python

import pandas as pd
prices = pd.Series([100, 101, 99, 102, 104, 103])
returns = prices.pct_change()
positive_days = returns[returns > 0].count()
print(positive_days)

A 5

B 3

C 2

D 4

💡 Hint

Count how many daily returns are greater than zero, ignoring the first NaN.

The positive returns occur on days where price increased compared to previous day: from 100 to 101, 99 to 102, and 102 to 104, totaling 3 days.

Practice

1. What is the main purpose of analyzing financial data in Python?

easy

A. To write novels

B. To create video games

C. To design websites

D. To understand money trends and make informed decisions

Solution

1. Step 1: Understand the goal of financial data analysis

   Financial data analysis focuses on studying money-related data to find patterns and trends.

2. Step 2: Match the goal with the options

   Only To understand money trends and make informed decisions talks about understanding money trends and making decisions, which fits the purpose.

3. Final Answer:

   To understand money trends and make informed decisions -> Option D

4. Quick Check:

   Financial data analysis = Understand money trends [OK]

Hint: Focus on money trends and decisions for financial analysis [OK]

Common Mistakes:

• Confusing financial analysis with unrelated fields
• Ignoring the purpose of data analysis
• Choosing options unrelated to money or data

2. Which of the following is the correct way to import pandas for financial data analysis in Python?

easy

A. import pandas as pd

B. import panda as pd

C. import pd as pandas

D. import pandas

Solution

1. Step 1: Recall the standard pandas import syntax

   The common way to import pandas is using 'import pandas as pd' for easy reference.

2. Step 2: Check each option for correctness

   import pandas as pd matches the standard syntax. Options B and C have wrong module names or order. import pandas imports pandas but without alias, which is less common.

3. Final Answer:

   import pandas as pd -> Option A

4. Quick Check:

   Standard pandas import = import pandas as pd [OK]

Hint: Use 'import pandas as pd' for standard data analysis [OK]

Common Mistakes:

• Misspelling 'pandas' as 'panda'
• Reversing import order
• Not using alias 'pd' which is common practice

3. Given the code:

import pandas as pd

data = {'Date': ['2024-01-01', '2024-01-02'], 'Price': [100, 105]}
df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
print(df.loc['2024-01-02'])

What will be the output?

medium

A. Error: KeyError

B. Price 105
Name: 2024-01-02 00:00:00, dtype: int64

C. Date 2024-01-02
Price 105

D. Price 100
Name: 2024-01-01 00:00:00, dtype: int64

Solution

1. Step 1: Understand DataFrame creation and index setting

   The DataFrame has dates converted to datetime and set as index. So rows are accessed by date index.

2. Step 2: Access row with loc for '2024-01-02'

   Using df.loc['2024-01-02'] returns the row with Price 105 and index as Timestamp '2024-01-02 00:00:00'.

3. Final Answer:

   Price 105 Name: 2024-01-02 00:00:00, dtype: int64 -> Option B

4. Quick Check:

   df.loc['2024-01-02'] = Price 105 [OK]

Hint: Remember loc uses index; datetime index returns full timestamp row [OK]

Common Mistakes:

• Expecting a DataFrame instead of a Series output
• Not converting 'Date' to datetime before indexing
• Using wrong date format causing KeyError

4. Identify the error in this code snippet for calculating daily returns:

import pandas as pd
prices = pd.Series([100, 105, 103, 108])
returns = prices.pct_change()
print(returns)

medium

A. No error; code correctly calculates daily returns

B. pct_change() should be pct_change(1)

C. prices should be a DataFrame, not Series

D. Missing import for numpy

Solution

1. Step 1: Understand pct_change() method

   pct_change() calculates percentage change between current and prior element by default (periods=1).

2. Step 2: Check code correctness

   prices is a Series, which supports pct_change(). No import or DataFrame needed. Code runs fine and prints daily returns.

3. Final Answer:

   No error; code correctly calculates daily returns -> Option A

4. Quick Check:

   pct_change() default periods=1 works [OK]

Hint: pct_change() defaults to 1 period; no extra args needed [OK]

Common Mistakes:

• Thinking pct_change needs argument 1 explicitly
• Confusing Series with DataFrame necessity
• Assuming numpy import is required for pct_change

5. You have a DataFrame df with columns Date (string), Open, and Close prices. How do you create a new column Daily_Return that shows the percentage change from Open to Close for each day?

hard

A. df['Daily_Return'] = df['Close'].pct_change()

B. df['Daily_Return'] = df['Open'] / df['Close']

C. df['Daily_Return'] = (df['Close'] - df['Open']) / df['Open'] * 100

D. df['Daily_Return'] = df['Close'] - df['Open']

Solution

1. Step 1: Understand daily return calculation

   Daily return is the percent change from Open to Close price: ((Close - Open) / Open) * 100.

2. Step 2: Match formula with options

   df['Daily_Return'] = (df['Close'] - df['Open']) / df['Open'] * 100 correctly applies the formula. df['Daily_Return'] = df['Close'].pct_change() calculates percent change between rows, not within a row. Options C and D do not calculate percentage change correctly.

3. Final Answer:

   df['Daily_Return'] = (df['Close'] - df['Open']) / df['Open'] * 100 -> Option C

4. Quick Check:

   Daily return = ((Close - Open)/Open)*100 [OK]

Hint: Use (Close - Open) / Open * 100 for daily percent return [OK]

Common Mistakes:

• Using pct_change() which compares different rows
• Dividing Open by Close instead of difference over Open
• Not multiplying by 100 for percentage