Working with large files efficiently in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a large CSV file efficiently using NumPy.

NumPy

import numpy as np
large_data = np.genfromtxt('data.csv', delimiter=[1])

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

A\t

B;

C|

D,

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong delimiter like semicolon or tab.

Forgetting to specify the delimiter.

The delimiter for CSV files is a comma, so we use ',' to correctly parse the file.

2fill in blank

medium

Complete the code to load only the first 1000 rows from a large file using NumPy.

NumPy

import numpy as np
subset_data = np.genfromtxt('data.csv', delimiter=',', max_rows=[1])

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

A1500

B500

C1000

D2000

Attempts:

3 left

💡 Hint

Common Mistakes

Using max_rows larger than the file size.

Not using max_rows and loading the entire file.

Setting max_rows=1000 loads only the first 1000 rows, which helps handle large files efficiently.

3fill in blank

hard

Fix the error in the code to load a large binary file efficiently with NumPy.

NumPy

import numpy as np
large_array = np.fromfile('data.bin', dtype=[1])

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Afloat64

Bint

Cstring

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using Python types like 'int' or 'list' instead of NumPy dtypes.

Using 'string' which is not valid for binary numeric data.

The dtype must be a valid NumPy data type like 'float64' to correctly read binary data.

4fill in blank

hard

Fill both blanks to create a memory-mapped array for efficient large file access.

NumPy

import numpy as np
memmap_array = np.memmap('large_data.dat', dtype=[1], mode=[2])

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A'float32'

B'r+'

C'int64'

D'w+'

Attempts:

3 left

💡 Hint

Common Mistakes

Using mode='w+' which overwrites the file.

Using an incorrect dtype that doesn't match the file data.

Using dtype='float32' and mode='r+' creates a memory-mapped array for reading and writing efficiently.

5fill in blank

hard

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

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = { [1] : [2] for [3] in words if len([3]) > 3 }

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

Aword

Blen(word)

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not filtering words by length.

The comprehension uses 'word' as the variable, maps word to its length, and filters words longer than 3 characters.