Numeric types (int64, float64) in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is int64 in pandas?

int64 is a numeric data type in pandas used to store whole numbers (integers) with 64-bit precision. It can hold both positive and negative integers.

beginner

What does float64 represent in pandas?

float64 is a numeric data type in pandas used to store decimal numbers (floating-point numbers) with 64-bit precision. It can represent numbers with fractions.

beginner

Why would you choose float64 over int64 in a DataFrame?

You choose float64 when your data contains decimal values or fractions. int64 is only for whole numbers without decimals.

beginner

How can you check the data type of a pandas DataFrame column?

You can use df['column_name'].dtype to see the data type of a specific column in a pandas DataFrame.

intermediate

What happens if you try to store a decimal number in an int64 column?

The decimal part will be dropped (truncated), and only the whole number part will be stored. This can lead to loss of information.

Which pandas data type is best for storing whole numbers?

A bool

B float64

C int64

D object

int64 is used for whole numbers. float64 is for decimals, object is for text, and bool is for True/False values.

What does float64 allow you to store that int64 does not?

A Decimal numbers

B Negative numbers

C Text strings

D Boolean values

float64 stores decimal (fractional) numbers, while int64 only stores whole numbers.

How do you check the data type of a pandas DataFrame column named 'age'?

A df.age.type()

B df['age'].dtype

C df.type('age')

D df['age'].type

The correct way is df['age'].dtype to get the data type of the 'age' column.

If you store 3.7 in an int64 column, what happens?

A It stores 3

B It stores 4

C It stores 3.7 exactly

D It causes an error

The decimal part is dropped, so 3.7 becomes 3 when stored in an int64 column.

Which of these is NOT a numeric type in pandas?

A int64

B int32

C float64

D object

object is used for text or mixed types, not numeric data.

Explain the difference between int64 and float64 in pandas.

Think about whether the number has decimals or not.

How can you find out the data type of a column in a pandas DataFrame?

It's a property of the column, not a method.

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