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.

Practice

1. Which pandas numeric type is best for storing whole numbers without decimals?

easy

A. bool

B. float64

C. int64

D. object

Solution

1. Step 1: Understand numeric types in pandas

   int64 is used for integers (whole numbers), float64 for decimals.

2. Step 2: Identify the type for whole numbers

   Whole numbers require int64 because float64 stores decimals.

3. Final Answer:

   int64 -> Option C

4. Quick Check:

   Whole numbers = int64 [OK]

Hint: Whole numbers use int64, decimals use float64 [OK]

Common Mistakes:

• Confusing float64 as default for all numbers
• Choosing object type for numeric data
• Using bool for numeric storage

2. Which of the following is the correct way to convert a pandas DataFrame column df['col'] to float64 type?

easy

A. df.astype('float64')['col']

B. df['col'].convert('float64')

C. astype(df['col'], 'float64')

D. df['col'].astype('float64')

Solution

1. Step 1: Recall pandas method to change column type

   The method to change a column's type is astype() called on the column.

2. Step 2: Apply correct syntax

   Use df['col'].astype('float64') to convert the column to float64.

3. Final Answer:

   df['col'].astype('float64') -> Option D

4. Quick Check:

   astype() on column = correct syntax [OK]

Hint: Use astype() on the column to change type [OK]

Common Mistakes:

• Calling astype() on the whole DataFrame incorrectly
• Using non-existent convert() method
• Wrong function syntax like astype(df['col'], 'float64')

3. Given the code:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.0, 2.5, 3.5]})
df['A'] = df['A'].astype('float64')
print(df.dtypes)

What will be the output?

medium

A. A float64
B float64
dtype: object

B. A int64
B float64
dtype: object

C. A object
B float64
dtype: object

D. A float64
B int64
dtype: object

Solution

1. Step 1: Check initial dtypes

   Column 'A' starts as int64, 'B' as float64.

2. Step 2: Convert 'A' to float64

   Using astype('float64') changes 'A' from int64 to float64.

3. Final Answer:

   A float64 B float64 dtype: object -> Option A

4. Quick Check:

   astype changes int64 to float64 [OK]

Hint: astype changes column type; check dtypes after conversion [OK]

Common Mistakes:

• Assuming original types remain unchanged
• Confusing int64 and float64 outputs
• Misreading print output format

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'num': [1, 2, 3]})
df['num'] = df['num'].astype(int64)

medium

A. int64 is not defined without quotes

B. astype() cannot convert to int64

C. DataFrame column cannot be reassigned

D. Missing import for astype

Solution

1. Step 1: Identify the error in astype argument

   int64 must be passed as a string 'int64', not as a bare name.

2. Step 2: Understand Python name error

   Without quotes, Python looks for a variable named int64 which is undefined.

3. Final Answer:

   int64 is not defined without quotes -> Option A

4. Quick Check:

   Use quotes around type names in astype() [OK]

Hint: Always quote type names in astype() [OK]

Common Mistakes:

• Passing type names without quotes
• Thinking astype() needs import
• Believing columns cannot be reassigned

5. You have a DataFrame df with a column 'values' containing numbers as strings, e.g., ['1', '2.5', '3']. You want to convert this column to numeric types efficiently, storing integers as int64 and decimals as float64 where needed. Which approach is best?

hard

A. Use pd.to_numeric(df['values'], downcast='integer')

B. Use pd.to_numeric(df['values'], errors='coerce') and then convert integers to int64

C. Use df['values'] = df['values'].astype('float64') for all values

D. Manually check each value and convert to int64 or float64

Solution

1. Step 1: Understand mixed numeric strings

   Strings include integers and decimals, so direct astype('float64') converts all to float.

2. Step 2: Use pd.to_numeric with errors='coerce'

   This converts strings to numbers, coercing errors to NaN, allowing safe conversion.

3. Step 3: Convert integers to int64 after identifying them

   After conversion, integers can be cast to int64 to save memory.

4. Final Answer:

   Use pd.to_numeric(df['values'], errors='coerce') and then convert integers to int64 -> Option B

5. Quick Check:

   pd.to_numeric + careful conversion handles mixed types best [OK]

Hint: Use pd.to_numeric with errors='coerce' for mixed numeric strings [OK]

Common Mistakes:

• Converting all to float64 losing integer type
• Trying manual conversion for each value
• Ignoring errors in conversion causing crashes