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')

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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]()

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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]()

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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}

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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 }}

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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.