Why Python is the top choice for data analysis in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the popular data analysis library in Python.

Data Analysis Python

import [1] as pd

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Anumpy

Bpandas

Cscipy

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Using numpy instead of pandas for data frames.

Importing matplotlib which is for plotting, not data manipulation.

The pandas library is widely used for data analysis in Python because it provides easy-to-use data structures and data manipulation tools.

2fill in blank

medium

Complete the code to read a CSV file into a pandas DataFrame.

Data Analysis Python

df = pd.[1]('data.csv')

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Aload_csv

Bto_csv

Copen_csv

Dread_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_csv which saves data instead of reading.

Using load_csv or open_csv which are not pandas functions.

The read_csv function loads data from a CSV file into a DataFrame for analysis.

3fill in blank

hard

Fix the error in the code to display the first 5 rows of the DataFrame.

Data Analysis Python

print(df.[1]())

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Adisplay

Bshow

Chead

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() which shows the last rows.

Using show() or display() which are not pandas DataFrame methods.

The head() method shows the first 5 rows of a DataFrame, which is useful to quickly check the data.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Data Analysis Python

{word: [1] for word in words if [2]

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Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word > 3 which compares string to number.

Not using len() to get word length.

The dictionary comprehension maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if count is greater than 1.

Data Analysis Python

{ [1]: [2] for [3], [2] in data.items() if [2] > 1 }

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Aword.upper()

Bcount

Cword

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using data instead of word in the loop variable.

Not converting word to uppercase.

Using count variable incorrectly.

The comprehension maps each word converted to uppercase (word.upper()) to its count. It iterates over word, count pairs from data.items() and filters counts greater than 1.