Numeric types (int64, float64) in Pandas

Introduction

Numeric types like int64 and float64 help us store numbers in tables. They let us do math and analysis easily.

When you want to store whole numbers like counts or ages.

When you need to store decimal numbers like prices or measurements.

When you want to perform calculations like sums, averages, or differences.

When you want to check or change the type of numbers in your data.

When you want to save memory by choosing the right number type.

Syntax

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.5, 2.5, 3.5]})

# Check data types
df.dtypes

# Convert column to int64
df['B'] = df['B'].astype('int64')

int64 means 64-bit integer (whole numbers).

float64 means 64-bit floating point (decimal numbers).

Examples

This shows the 'Age' column is int64 because it has whole numbers.

Pandas

import pandas as pd

df = pd.DataFrame({'Age': [25, 30, 22]})
print(df.dtypes)

This shows the 'Price' column is float64 because it has decimal numbers.

Pandas

import pandas as pd

df = pd.DataFrame({'Price': [10.5, 20.75, 15.0]})
print(df.dtypes)

This converts the 'Score' column from float64 to int64 by removing decimals.

Pandas

import pandas as pd

df = pd.DataFrame({'Score': [90.0, 85.5, 88.0]})
df['Score'] = df['Score'].astype('int64')
print(df)

Sample Program

This program creates a table with whole numbers and decimals. It shows the types, then changes the decimal column to whole numbers by rounding and converting the type.

Pandas

import pandas as pd

# Create a DataFrame with int64 and float64 columns
df = pd.DataFrame({
    'Count': [10, 20, 30],
    'Weight': [1.5, 2.3, 3.7]
})

# Show original data types
print('Original data types:')
print(df.dtypes)

# Convert 'Weight' to int64 by rounding
df['Weight'] = df['Weight'].round().astype('int64')

# Show updated DataFrame and data types
print('\nUpdated DataFrame:')
print(df)
print('\nUpdated data types:')
print(df.dtypes)

Important Notes

Changing float64 to int64 removes decimal parts, so be careful with rounding.

int64 and float64 use 64 bits, which means they can store very large numbers.

Use df.dtypes to check the types of your data columns.

Summary

int64 stores whole numbers, float64 stores decimal numbers.

Use astype() to change a column's numeric type.

Checking and setting numeric types helps with correct math and saving memory.

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