Data analysis workflow (collect, clean, explore, visualize, conclude) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load data from a CSV file using pandas.

Data Analysis Python

import pandas as pd
data = pd.[1]('data.csv')

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

Aopen_csv

Bload_csv

Cread_csv

Dimport_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent functions like load_csv or open_csv.

Trying to use Python's open() instead of pandas read_csv.

The pandas function to read CSV files is read_csv.

2fill in blank

medium

Complete the code to remove rows with missing values from the DataFrame.

Data Analysis Python

clean_data = data.[1]()

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

Afillna

Bremove_na

Ccleanna

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() which fills missing values instead of removing rows.

Using non-existent methods like remove_na or cleanna.

The dropna() method removes rows with missing values.

3fill in blank

hard

Fix the error in the code to calculate the average of the 'age' column.

Data Analysis Python

average_age = data['age'].[1]()

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

Amean

Bsum

Ccount

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() which adds values but does not average.

Using count() which counts non-missing values.

The mean() method calculates the average value of a column.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' to filter words.

Using 'word' instead of len(word) for the dictionary values.

We use len(word) to get length and '>' to filter words longer than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values greater than zero.

Data Analysis Python

result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper() for keys.

Using '<' instead of '>' for filtering values.

We convert keys to uppercase, keep values, and filter values greater than zero.

Practice

1. Which step in the data analysis workflow involves removing errors and fixing missing values in the data?

easy

A. Collect

B. Clean

C. Visualize

D. Conclude

Solution

1. Step 1: Understand the meaning of each workflow step

   Cleaning data means fixing errors and handling missing values to prepare data for analysis.

2. Step 2: Match the step to the description

   Removing errors and fixing missing values is exactly what cleaning does, not collecting or visualizing.

3. Final Answer:

   Clean -> Option B

4. Quick Check:

   Cleaning = Fix errors and missing values [OK]

Hint: Cleaning fixes errors and missing data [OK]

Common Mistakes:

• Confusing 'collect' with cleaning
• Thinking visualization fixes data
• Mixing 'conclude' with cleaning step

2. Which Python library is commonly used for data visualization in the data analysis workflow?

easy

A. matplotlib

B. scikit-learn

C. numpy

D. pandas

Solution

1. Step 1: Identify libraries and their main uses

   pandas is for data manipulation, numpy for numerical operations, scikit-learn for machine learning, matplotlib for plotting graphs.

2. Step 2: Choose the library for visualization

   matplotlib is the standard Python library for creating charts and graphs.

3. Final Answer:

   matplotlib -> Option A

4. Quick Check:

   Visualization = matplotlib [OK]

Hint: matplotlib is for plotting graphs [OK]

Common Mistakes:

• Choosing pandas for visualization
• Confusing numpy with plotting
• Selecting scikit-learn for charts

3. What will be the output of this Python code snippet?

import pandas as pd

data = {'age': [25, 30, None, 22], 'score': [88, 92, 85, None]}
df = pd.DataFrame(data)
clean_df = df.dropna()
print(clean_df)

medium

A. DataFrame with all rows including those with None values

B. DataFrame with rows containing None values replaced by 0

C. DataFrame with rows containing None values removed

D. Error because dropna() is not a valid method

Solution

1. Step 1: Understand what dropna() does in pandas

   dropna() removes rows that have any missing (None/NaN) values.

2. Step 2: Apply dropna() to the DataFrame

   Rows with None in 'age' or 'score' are removed, leaving only rows with complete data.

3. Final Answer:

   DataFrame with rows containing None values removed -> Option C

4. Quick Check:

   dropna() removes rows with missing data [OK]

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

Common Mistakes:

• Thinking dropna() replaces missing values
• Expecting an error from dropna()
• Assuming dropna() keeps all rows

4. Identify the error in this data exploration code and choose the correct fix:

import pandas as pd

data = {'height': [170, 165, 180], 'weight': [65, 70, 75]}
df = pd.DataFrame(data)
print(df.mean(axis=1))

medium

A. No error, code runs correctly

B. Use df.mean() without axis parameter

C. Replace df.mean(axis=1) with df.sum(axis=1)

D. Change axis=1 to axis=0 to get mean of columns

Solution

1. Step 1: Understand what df.mean(axis=1) does

   axis=1 calculates the mean across columns for each row, which is valid here.

2. Step 2: Check if code runs without error

   The code runs correctly and prints the mean height and weight per row.

3. Final Answer:

   No error, code runs correctly -> Option A

4. Quick Check:

   df.mean(axis=1) calculates row means [OK]

Hint: axis=1 means row-wise operation [OK]

Common Mistakes:

• Thinking axis=1 causes error
• Confusing axis=0 and axis=1
• Replacing mean with sum unnecessarily

5. You have a dataset with sales data including missing values. Which sequence of steps correctly follows the data analysis workflow to find average sales per region?

Options:
A) Collect data -> Visualize data -> Clean data -> Explore data -> Conclude
B) Collect data -> Clean data -> Explore data -> Visualize data -> Conclude
C) Clean data -> Collect data -> Explore data -> Visualize data -> Conclude
D) Collect data -> Explore data -> Clean data -> Visualize data -> Conclude

hard

A. Collect data -> Visualize data -> Clean data -> Explore data -> Conclude

B. Collect data -> Explore data -> Clean data -> Visualize data -> Conclude

C. Clean data -> Collect data -> Explore data -> Visualize data -> Conclude

D. Collect data -> Clean data -> Explore data -> Visualize data -> Conclude

Solution

1. Step 1: Recall the correct order of data analysis workflow

   The standard order is: collect data, clean data, explore data, visualize data, then conclude.

2. Step 2: Match the options to this order

   Collect data -> Clean data -> Explore data -> Visualize data -> Conclude matches the correct sequence exactly.

3. Final Answer:

   Collect data -> Clean data -> Explore data -> Visualize data -> Conclude -> Option D

4. Quick Check:

   Workflow order = B [OK]

Hint: Follow collect -> clean -> explore -> visualize -> conclude [OK]

Common Mistakes:

• Visualizing before cleaning data
• Cleaning data before collecting it
• Exploring data before cleaning