Reproducible analysis patterns in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code using a fixed random seed?

This code sets a random seed before generating random numbers. What will be the printed output?

Data Analysis Python

import numpy as np
np.random.seed(42)
random_numbers = np.random.rand(3)
print(random_numbers)

A [0.37454012 0.95071431 0.73199394]

B [0.37454012 0.95071431 0.59865848]

C [0.5488135 0.71518937 0.60276338]

D [0.64589411 0.43758721 0.891773 ]

💡 Hint

Setting a random seed fixes the sequence of random numbers generated.

Using np.random.seed(42) fixes the random number generator's state, so np.random.rand(3) always produces the same three numbers: approximately 0.3745, 0.9507, and 0.7320.

❓ data_output

intermediate

What is the shape of the DataFrame after filtering?

Given this DataFrame and filtering code, what is the shape of the resulting DataFrame?

Data Analysis Python

import pandas as pd
data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]}
df = pd.DataFrame(data)
filtered_df = df[df['A'] > 2]

A (3, 1)

B (3, 2)

C (2, 2)

D (2, 1)

💡 Hint

Count how many rows have 'A' values greater than 2.

Rows where 'A' > 2 are those with values 3 and 4, so 2 rows remain. The DataFrame has 2 columns, so shape is (2, 2).

❓ visualization

advanced

Which plot shows a reproducible histogram with fixed bins?

You want to create a histogram that looks the same every time you run the code. Which option produces a reproducible histogram with fixed bins?

Data Analysis Python

import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
data = np.random.randn(1000)

A plt.hist(data, bins=np.linspace(-4,4,21)); plt.show()

B plt.hist(data, bins=20); plt.show()

C plt.hist(data); plt.show()

D plt.hist(data, bins='auto'); plt.show()

💡 Hint

Fixed bins mean the bin edges are explicitly set.

Option A uses fixed bin edges from -4 to 4 with 20 bins, ensuring the histogram is reproducible and consistent across runs.

🔧 Debug

advanced

What error does this code raise when saving a DataFrame without specifying index?

This code saves a DataFrame to CSV. What error or issue occurs?

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'x':[1,2], 'y':[3,4]})
df.to_csv('output.csv', index=False)
with open('output.csv') as f:
    lines = f.readlines()
print(lines[0])

A IndexError

B '0,x,y\n'

C FileNotFoundError

D 'x,y\n'

💡 Hint

Check the first line of the CSV file when index=False is used.

When index=False, the CSV header contains only column names without an index column, so the first line is 'x,y\n'.

🚀 Application

expert

Which option ensures reproducible train-test split in scikit-learn?

You want to split your dataset into training and testing sets reproducibly. Which code snippet guarantees the same split every time?

Data Analysis Python

from sklearn.model_selection import train_test_split
X = [[i] for i in range(10)]
y = [0,1,0,1,0,1,0,1,0,1]

A train_test_split(X, y, test_size=0.3, shuffle=False)

B train_test_split(X, y, test_size=0.3, random_state=42)

C train_test_split(X, y, test_size=0.3)

D train_test_split(X, y, test_size=0.3, random_state=None)

💡 Hint

Setting random_state fixes the randomness of the split.

Only specifying random_state=42 ensures the split is the same every time you run the code.

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