Key Differences Between Python 2 and Python 3 in Data Science

Table of Contents

1. Fundamental Concept Differences
   • Print Function
   • Integer Division
   • Unicode Strings
   • Exception Handling
2. Usage Method Differences
   • Library Compatibility
   • Syntax Changes
3. Common Practices in Python 2 and Python 3 for Data Science
   • Data Manipulation
   • Data Visualization
   • Machine Learning
4. Best Practices for Data Scientists
5. Conclusion
6. References

Fundamental Concept Differences

Print Function

In Python 2, print is a statement, while in Python 3, it is a function.

Python 2 Example:

# Python 2
print "Hello, World!"

Python 3 Example:

# Python 3
print("Hello, World!")

Integer Division

In Python 2, when you divide two integers, the result is also an integer, truncating the decimal part. In Python 3, the division operator / performs true division, returning a float.

Python 2 Example:

# Python 2
result = 5 / 2
print result  # Output: 2

Python 3 Example:

# Python 3
result = 5 / 2
print(result)  # Output: 2.5

Unicode Strings

Python 2 has two types of strings: ASCII strings (str) and Unicode strings (unicode). Python 3 uses Unicode strings by default, and the str type represents Unicode strings.

Python 2 Example:

# Python 2
ascii_string = 'Hello'
unicode_string = u'你好'

Python 3 Example:

# Python 3
unicode_string = '你好'

Exception Handling

The syntax for exception handling has a minor difference. In Python 2, you can use the as keyword optionally, while in Python 3, it is mandatory.

Python 2 Example:

# Python 2
try:
    result = 1 / 0
except ZeroDivisionError, e:
    print "Error:", e

Python 3 Example:

# Python 3
try:
    result = 1 / 0
except ZeroDivisionError as e:
    print("Error:", e)

Usage Method Differences

Library Compatibility

Many modern data science libraries, such as pandas, NumPy, and scikit - learn, have fully embraced Python 3 and may have limited or no support for Python 2. For example, Python 2 support for pandas was officially dropped after version 0.24.

Syntax Changes

Python 3 introduced some new syntax features that are not available in Python 2. For example, the yield from syntax for generators.

# Python 3
def gen1():
    yield 1
    yield 2

def gen2():
    yield from gen1()
    yield 3

for num in gen2():
    print(num)

Common Practices in Python 2 and Python 3 for Data Science

Data Manipulation

In both Python 2 and Python 3, pandas is a popular library for data manipulation. However, with the deprecation of Python 2 support in pandas, Python 3 is the recommended choice for new projects.

# Python 3
import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df)

Data Visualization

Matplotlib is a widely used library for data visualization. It supports both Python 2 and Python 3, but Python 3 may offer better performance and compatibility with other modern libraries.

# Python 3
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.show()

Machine Learning

scikit - learn is a powerful machine learning library. Python 3 is the preferred version as it ensures compatibility with the latest features and bug fixes.

# Python 3
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)

knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
accuracy = knn.score(X_test, y_test)
print("Accuracy:", accuracy)

Best Practices for Data Scientists

• Use Python 3 for New Projects: Given the deprecation of Python 2 and the better support for modern libraries, Python 3 should be the default choice for new data science projects.
• Migrate Existing Python 2 Code: If you have existing Python 2 code, plan to migrate it to Python 3 to ensure long - term compatibility and access to the latest features.
• Test Code Compatibility: When using third - party libraries, test your code thoroughly to ensure it works correctly in Python 3.

Conclusion

The differences between Python 2 and Python 3 in data science are significant and can impact various aspects of your projects. While Python 2 was once the standard, Python 3 has become the new norm due to its improved features, better Unicode support, and wider library compatibility. Data scientists should embrace Python 3 for new projects and migrate existing Python 2 code to stay up - to - date with the latest developments in the field.

References

• Python official documentation: https://docs.python.org/
• Pandas official documentation: https://pandas.pydata.org/docs/
• NumPy official documentation: https://numpy.org/doc/
• scikit - learn official documentation: https://scikit - learn.org/stable/
• Matplotlib official documentation: https://matplotlib.org/stable/