Financial data analysis pattern in Data Analysis Python - Mini Project: Build & Apply

Financial data analysis pattern

📖 Scenario: You work as a financial analyst. You have a small dataset of monthly sales figures for different products. Your task is to analyze this data to find which products have sales above a certain threshold.

🎯 Goal: Build a simple Python program that stores sales data, sets a sales threshold, filters products with sales above the threshold, and prints the filtered results.

📋 What You'll Learn

Create a dictionary with product names as keys and monthly sales as values.

Create a variable to hold the sales threshold.

Use a dictionary comprehension to filter products with sales above the threshold.

Print the filtered dictionary showing products with sales above the threshold.

💡 Why This Matters

🌍 Real World

Financial analysts often need to filter and analyze sales data to identify top-performing products.

💼 Career

This pattern helps in preparing reports and making data-driven decisions in finance and sales roles.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Laptop': 1200, 'Smartphone': 850, 'Tablet': 400, 'Headphones': 150, 'Smartwatch': 300.

Data Analysis Python

# Create the sales_data dictionary with product names and sales
# Your code here

Hint

Use curly braces {} to create a dictionary with keys and values separated by colons.

2

Set the sales threshold

Create a variable called threshold and set it to 500.

Data Analysis Python

sales_data = {'Laptop': 1200, 'Smartphone': 850, 'Tablet': 400, 'Headphones': 150, 'Smartwatch': 300}
# Create the threshold variable and set it to 500
# Your code here

Hint

Just assign the number 500 to the variable threshold.

3

Filter products with sales above threshold

Use a dictionary comprehension to create a new dictionary called high_sales that contains only the products from sales_data with sales greater than threshold. Use product and sales as the loop variables.

Data Analysis Python

sales_data = {'Laptop': 1200, 'Smartphone': 850, 'Tablet': 400, 'Headphones': 150, 'Smartwatch': 300}
threshold = 500
# Create high_sales dictionary with products having sales > threshold
# Your code here

Hint

Use {key: value for key, value in dict.items() if condition} to filter the dictionary.

4

Print the filtered high sales products

Print the high_sales dictionary to display the products with sales above the threshold.

Data Analysis Python

sales_data = {'Laptop': 1200, 'Smartphone': 850, 'Tablet': 400, 'Headphones': 150, 'Smartwatch': 300}
threshold = 500
high_sales = {product: sales for product, sales in sales_data.items() if sales > threshold}
# Print the high_sales dictionary
# Your code here

Hint

Use print(high_sales) to show the filtered dictionary.

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