Reproducible analysis patterns in Data Analysis Python - Mini Project: Build & Apply

Reproducible Analysis Patterns

📖 Scenario: You are working as a data analyst for a small bakery. You want to analyze daily sales data in a way that you can easily repeat the analysis every day with new data. This means your steps should be clear and easy to follow again and again.

🎯 Goal: Build a simple, reproducible analysis pattern in Python that loads sales data, sets a sales threshold, filters the data for days with high sales, and prints the filtered results.

📋 What You'll Learn

Create a dictionary with daily sales data

Set a sales threshold variable

Filter the sales data using a dictionary comprehension

Print the filtered sales data

💡 Why This Matters

🌍 Real World

Businesses often need to analyze sales or other data repeatedly. Using clear steps and variables makes it easy to update and rerun the analysis with new data.

💼 Career

Data analysts and scientists use reproducible patterns to save time and avoid mistakes when working with data regularly.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called daily_sales with these exact entries: 'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100.

Data Analysis Python

# Create the daily_sales dictionary with exact values
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate each day and sales number with a colon, and separate pairs with commas.

2

CONFIGURATION: Set the sales threshold

Create a variable called sales_threshold and set it to 150.

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
# Set sales_threshold to 150
# Your code here

Hint

Just assign the number 150 to the variable sales_threshold.

3

CORE LOGIC: Filter days with sales above the threshold

Create a new dictionary called high_sales_days using a dictionary comprehension. Include only the days from daily_sales where the sales value is greater than sales_threshold.

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 150
# Create high_sales_days dictionary with days having sales > sales_threshold
# Your code here

Hint

Use a dictionary comprehension with for day, sales in daily_sales.items() and an if condition to filter.

4

OUTPUT: Print the filtered high sales days

Write a print statement to display the high_sales_days dictionary.

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 150
high_sales_days = {day: sales for day, sales in daily_sales.items() if sales > sales_threshold}
# Print the high_sales_days dictionary
# Your code here

Hint

Use print(high_sales_days) to show the filtered dictionary.

Practice

1. What is the main goal of reproducible analysis in data science?

easy

A. To ensure the analysis can be repeated with the same results

B. To make the code run faster

C. To use the latest software versions

D. To create complex visualizations

Solution

1. Step 1: Understand reproducible analysis meaning

   Reproducible analysis means you can run your work again and get the same results.

2. Step 2: Identify the main goal

   The main goal is to make sure the analysis is repeatable and reliable.

3. Final Answer:

   To ensure the analysis can be repeated with the same results -> Option A

4. Quick Check:

   Reproducible analysis = repeatable results [OK]

Hint: Focus on repeatability and consistent results [OK]

Common Mistakes:

• Thinking speed or software version is the main goal
• Confusing reproducibility with visualization quality

2. Which of the following is the correct way to define a function in Python for reproducible analysis?

easy

A. def load_data():
return pd.read_csv('data.csv')

B. function load_data() {
return read.csv('data.csv')
}

C. load_data() => pd.read_csv('data.csv')

D. def load_data:
return pd.read_csv('data.csv')

Solution

1. Step 1: Identify Python function syntax

   Python functions start with 'def', followed by name and parentheses, then a colon.

2. Step 2: Check each option

   def load_data(): return pd.read_csv('data.csv') uses correct Python syntax. function load_data() { return read.csv('data.csv') } is JavaScript style. load_data() => pd.read_csv('data.csv') is incorrect Python syntax. def load_data: return pd.read_csv('data.csv') misses parentheses after function name.

3. Final Answer:

   def load_data():\n return pd.read_csv('data.csv') -> Option A

4. Quick Check:

   Python function = def name(): [OK]

Hint: Remember Python functions use def name(): [OK]

Common Mistakes:

• Using JavaScript or other language syntax
• Omitting parentheses after function name
• Missing colon at the end of function definition

3. Given the code below, what will be the output of print(cleaned_data.shape)?

import pandas as pd

def load_data():
    return pd.DataFrame({'A': [1, 2, None], 'B': [4, None, 6]})

def clean_data(df):
    return df.dropna()

data = load_data()
cleaned_data = clean_data(data)
print(cleaned_data.shape)

medium

A. (2, 2)

B. (0, 2)

C. (3, 2)

D. (1, 2)

Solution

1. Step 1: Understand the data and cleaning

   Original data has 3 rows and 2 columns. Rows with null are dropped by dropna(). Only the first row has no null values.

2. Step 2: Count remaining rows and columns

   After dropna(), only 1 row remains with 2 columns, so shape is (1, 2).

3. Final Answer:

   (1, 2) -> Option D

4. Quick Check:

   dropna() removes rows with null = (1, 2) [OK]

Hint: dropna() removes rows with missing values [OK]

Common Mistakes:

• Counting rows with null as valid
• Confusing columns count
• Assuming dropna() removes columns

4. Identify the error in the following reproducible analysis script:

def analyze_data():
    data = pd.read_csv('data.csv')
    cleaned = data.dropna
    return cleaned.describe()

result = analyze_data()
print(result)

medium

A. Incorrect file path in read_csv

B. Missing parentheses after dropna method call

C. Missing return statement in function

D. print statement syntax error

Solution

1. Step 1: Check method usage in code

   dropna is a method and must be called with parentheses: dropna()

2. Step 2: Identify error effect

   Without parentheses, cleaned is a method object, not a DataFrame, causing error on describe() call.

3. Final Answer:

   Missing parentheses after dropna method call -> Option B

4. Quick Check:

   Method call needs () = error if missing [OK]

Hint: Always add () when calling methods [OK]

Common Mistakes:

• Forgetting parentheses on method calls
• Assuming dropna is a property, not a method
• Ignoring error messages about method objects

5. You want to create a reproducible analysis script that loads data, cleans it by removing rows with missing values, and then calculates the mean of column 'score'. Which of the following code snippets correctly implements this pattern?

hard

A. def load_data:
return pd.read_csv('data.csv')

clean_data = lambda df: df.dropna()
mean_score = clean_data(data)['score'].mean()
print(mean_score)

B. data = pd.read_csv('data.csv')
clean = data.dropna
mean_score = clean['score'].mean()
print(mean_score)

C. def load_data():
return pd.read_csv('data.csv')

def clean_data(df):
return df.dropna()

def analyze(df):
return df['score'].mean()

# Run full analysis
raw = load_data()
clean = clean_data(raw)
result = analyze(clean)
print(result)

D. def load_data():
return pd.read_csv('data.csv')

clean_data = data.dropna()
mean_score = clean_data['score'].mean()
print(mean_score)

Solution

1. Step 1: Check function definitions and calls

   def load_data(): return pd.read_csv('data.csv') def clean_data(df): return df.dropna() def analyze(df): return df['score'].mean() # Run full analysis raw = load_data() clean = clean_data(raw) result = analyze(clean) print(result) defines functions for loading, cleaning, and analyzing, then runs them in order, ensuring reproducibility.

2. Step 2: Verify correctness of each step

   Functions use correct syntax, dropna() is called properly, and mean is calculated on cleaned data.

3. Final Answer:

   The code that defines functions for loading, cleaning, and analyzing, then calls them sequentially -> Option C

4. Quick Check:

   Functions + proper calls = reproducible script [OK]

Hint: Use functions and call them in sequence [OK]

Common Mistakes:

• Forgetting parentheses on dropna()
• Not defining functions properly
• Using variables before assignment