Loading datasets (CSV, built-in datasets) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

ML Python

import pandas as pd

data = pd.read_csv([1])

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

Aread_csv('data.csv')

Bdata.csv

C'data.csv'

Dpd.read_csv('data.csv')

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file name.

Passing the function call itself as argument.

The file path must be a string inside quotes when using pd.read_csv().

2fill in blank

medium

Complete the code to load the Iris dataset from sklearn.

ML Python

from sklearn import datasets

iris = datasets.[1]()

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

Aload_iris

Bfetch_iris

Cget_iris

Diris_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using fetch_iris which does not exist.

Trying to access iris_data attribute.

The correct function to load the Iris dataset is load_iris().

3fill in blank

hard

Fix the error in the code to load a CSV file with pandas and display the first 5 rows.

ML Python

import pandas as pd

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

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

Ashow

Bfirst

Ctop

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.first() which does not exist.

Using df.show() which is not a pandas method.

The method to show the first 5 rows of a DataFrame is head().

4fill in blank

hard

Fill both blanks to load the digits dataset and get its data and target arrays.

ML Python

from sklearn import datasets

digits = datasets.[1]()
data = digits.[2]

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

Aload_digits

Bfetch_digits

Cdata

Dtarget

Attempts:

3 left

💡 Hint

Common Mistakes

Using fetch_digits which is incorrect.

Accessing 'target' instead of 'data' for features.

Use load_digits() to load the dataset and access the data attribute for features.

5fill in blank

hard

Fill all three blanks to load the wine dataset, get its features and labels.

ML Python

from sklearn.datasets import [1]

wine = [1]()
features = wine.[2]
labels = wine.[3]

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

Aload_wine

Bdata

Ctarget

Dfetch_wine

Attempts:

3 left

💡 Hint

Common Mistakes

Using fetch_wine which does not exist.

Mixing up 'data' and 'target' attributes.

Use load_wine() to load the dataset, then access 'data' for features and 'target' for labels.

Practice

1. What is the main purpose of loading datasets in machine learning?

easy

A. To get data from files or built-in sources for training models

B. To save the model after training

C. To visualize the model's predictions

D. To delete unused data from memory

Solution

1. Step 1: Understand dataset loading

   Loading datasets means bringing data into your program so you can work with it.

2. Step 2: Identify the purpose in machine learning

   Data is needed to train and test machine learning models, so loading datasets is the first step.

3. Final Answer:

   To get data from files or built-in sources for training models -> Option A

4. Quick Check:

   Loading datasets = Getting data [OK]

Hint: Loading datasets means getting data ready for models [OK]

Common Mistakes:

• Confusing loading data with saving models
• Thinking loading means visualizing results
• Mixing loading with deleting data

2. Which Python code correctly loads a CSV file named data.csv using pandas?

easy

A. import pandas as pd
df = pd.csv_read('data.csv')

B. import pandas as pd
df = pd.load_csv('data.csv')

C. import pandas as pd
df = pd.read('data.csv')

D. import pandas as pd
df = pd.read_csv('data.csv')

Solution

1. Step 1: Recall pandas CSV loading syntax

   The correct function to load CSV files in pandas is read_csv().

2. Step 2: Check each option's function name

   Only import pandas as pd df = pd.read_csv('data.csv') uses pd.read_csv('data.csv'), which is correct syntax.

3. Final Answer:

   import pandas as pd\ndf = pd.read_csv('data.csv') -> Option D

4. Quick Check:

   pandas.read_csv() loads CSV files [OK]

Hint: Use pandas.read_csv() to load CSV files [OK]

Common Mistakes:

• Using wrong function names like load_csv or csv_read
• Forgetting to import pandas
• Using read() instead of read_csv()

3. What will be the output type of the following code?

from sklearn.datasets import load_iris
iris = load_iris()
print(type(iris.data))

medium

A. <class 'numpy.ndarray'>

B. <class 'list'>

C. <class 'pandas.DataFrame'>

D. <class 'dict'>

Solution

1. Step 1: Understand sklearn's load_iris output

   The load_iris() function returns a Bunch object with data as a NumPy array.

2. Step 2: Check the type of iris.data

   Printing type(iris.data) shows it is a numpy.ndarray.

3. Final Answer:

   <class 'numpy.ndarray'> -> Option A

4. Quick Check:

   sklearn data attribute = numpy.ndarray [OK]

Hint: Sklearn datasets return numpy arrays for data [OK]

Common Mistakes:

• Thinking iris.data is a list or dict
• Confusing with pandas DataFrame
• Not knowing sklearn returns numpy arrays

4. Identify the error in this code snippet that tries to load a CSV file:

import pandas as pd
df = pd.read_csv('data.csv', delimiter=';')
print(df.head())

Assuming the CSV uses commas, what is wrong?

medium

A. Missing import statement for csv module

B. The delimiter should be ',' not ';'

C. The function read_csv does not exist

D. print(df.head()) is incorrect syntax

Solution

1. Step 1: Check delimiter parameter usage

   The code uses delimiter=';' but the CSV file uses commas, so this is wrong.

2. Step 2: Understand correct delimiter for comma CSV

   For comma-separated files, the delimiter should be ',' or omitted (default).

3. Final Answer:

   The delimiter should be ',' not ';' -> Option B

4. Quick Check:

   Delimiter must match CSV separator [OK]

Hint: Match delimiter to CSV separator character [OK]

Common Mistakes:

• Using wrong delimiter for CSV files
• Thinking read_csv is missing
• Confusing print syntax errors

5. You want to load the built-in digits dataset from sklearn and convert it to a pandas DataFrame with feature columns named pixel_0 to pixel_63. Which code correctly does this?

hard

A. import pandas as pd
df = pd.read_csv('digits.csv')
df.columns = [f'pixel_{i}' for i in range(64)]

B. from sklearn.datasets import load_digits
import pandas as pd
digits = load_digits()
df = pd.DataFrame(digits, columns=[f'pixel_{i}' for i in range(64)])

C. from sklearn.datasets import load_digits
import pandas as pd
digits = load_digits()
df = pd.DataFrame(digits.data, columns=[f'pixel_{i}' for i in range(64)])

D. from sklearn.datasets import load_digits
import pandas as pd
digits = load_digits()
df = pd.DataFrame(digits.target, columns=[f'pixel_{i}' for i in range(64)])

Solution

1. Step 1: Load digits dataset correctly

   Use load_digits() to get the dataset object with data attribute for features.

2. Step 2: Create DataFrame with correct data and columns

   Pass digits.data to pd.DataFrame() and set columns to pixel_0 to pixel_63.

3. Final Answer:

   from sklearn.datasets import load_digits\nimport pandas as pd\ndigits = load_digits()\ndf = pd.DataFrame(digits.data, columns=[f'pixel_{i}' for i in range(64)]) -> Option C

4. Quick Check:

   Use digits.data for features in DataFrame [OK]

Hint: Use digits.data for features, not digits or digits.target [OK]

Common Mistakes:

• Passing digits object directly to DataFrame
• Using digits.target as data
• Trying to load digits from CSV without file