Numeric types (int64, float64) in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a pandas Series with integer type.

Pandas

import pandas as pd
s = pd.Series([1, 2, 3], dtype=[1])
print(s.dtype)

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

A"str"

B"float64"

C"int64"

D"bool"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'float64' instead of 'int64' for integer data.

Not specifying dtype at all.

Using 'str' which is for text data.

Using "int64" sets the Series to integer type.

2fill in blank

medium

Complete the code to convert a pandas Series to float type.

Pandas

import pandas as pd
s = pd.Series([1, 2, 3])
s = s.astype([1])
print(s.dtype)

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

A"int32"

B"object"

C"bool"

D"float64"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'int32' which keeps the type as integer.

Using 'bool' which converts to True/False values.

Using 'object' which is for mixed types or strings.

Using astype("float64") converts the Series to float type.

3fill in blank

hard

Fix the error in the code to create a DataFrame column with float type.

Pandas

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]})
df['A'] = df['A'].astype([1])
print(df['A'].dtype)

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

A"float64"

B"category"

C"int64"

D"string"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'int64' which does not change the type.

Using 'string' which is for text data.

Using 'category' which is for categorical data.

To convert the column to float type, use "float64" with astype.

4fill in blank

hard

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

Pandas

words = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword

Dword == 'data'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using a condition that does not filter by length.

Confusing the order of key and value in the dictionary.

The dictionary maps each word to its length using len(word). The condition keeps words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their length if length is greater than 2.

Pandas

words = ['cat', 'dog', 'a', 'bird']
result = { [1]: [2] for w in words if [3] }
print(result)

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

Aw.upper()

Blen(w)

Clen(w) > 2

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using the word itself instead of its length for values.

Incorrect condition that does not filter properly.

The dictionary keys are uppercase words using w.upper(). The values are their lengths using len(w). The condition filters words longer than 2 characters using len(w) > 2.