Comparison of Keras, TensorFlow, Pandas, Scikit-learn, Seaborn and numpy

The Python ecosystem offers a wide range of libraries for machine learning, data manipulation, visualization, and mathematical operations. Among the most popular are Keras, TensorFlow, Pandas, Scikit-learn, Seaborn and NumPy. Each of these libraries serves distinct purposes and is tailored to specific aspects of data science and machine learning workflows. This article provides a detailed comparison of these libraries to help you understand their strengths, weaknesses, and use cases.

Keras vs TensorFlow

Relationship Between Keras and TensorFlow

• Keras is a high-level neural network API that simplifies the process of building and training deep learning models.
• TensorFlow is a comprehensive machine learning framework that provides both high-level (e.g., tf.keras) and low-level APIs for building complex models.
• Since TensorFlow 2.0, Keras has been integrated as its official high-level API (tf.keras) [1][3][7].

Key Differences

When to Use

• Use Keras if you are a beginner or need to quickly prototype neural networks.
• Use TensorFlow if you require advanced features like distributed training, custom layers, or production-ready pipelines.

Pandas

Overview

Pandas is a Python library for data manipulation and analysis. It provides powerful tools for working with structured data such as tabular datasets (e.g., CSV files or SQL tables). Performance can be slower with very large datasets compared to specialized tools like Dask or PySpark.

Key Features

• DataFrames: Two-dimensional labeled data structures.
• Data Cleaning: Handles missing values, duplicates, and transformations. Built-in functions for cleaning, transforming, and aggregating data.
• Integration: Works seamlessly with other libraries like NumPy, Scikit-learn, and Seaborn.

Use Case

Pandas is essential for preprocessing data before feeding it into machine learning models or visualization tools.

Example

import pandas as pd

# Load dataset
data = pd.read_csv('dataset.csv')

# Clean missing values
data.fillna(0, inplace=True)

# Perform basic analysis
print(data.describe())

Scikit-learn

Overview

Scikit-learn is one of the most popular libraries for classical machine learning algorithms. It provides tools for supervised and unsupervised learning, along with utilities for model evaluation and preprocessing.

Key Features

• Algorithms: Includes regression (e.g., Linear Regression), classification (e.g., SVM), clustering (e.g., K-Means), and more.
• Preprocessing: Tools like scaling, encoding, and splitting datasets.
• Model Evaluation: Cross-validation, metrics like accuracy or F1-score.

Use Case

Scikit-learn is ideal for classical machine learning tasks where deep learning is not required.

Example

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = LinearRegression()
model.fit(X_train, y_train)

# Evaluate model
predictions = model.predict(X_test)
print(mean_squared_error(y_test, predictions))

Seaborn

Overview

Seaborn is a Python library built on top of Matplotlib that simplifies the creation of statistical visualizations. It integrates seamlessly with Pandas DataFrames to make data visualization intuitive.

Key Features

• High-level interface for creating attractive plots.
• Built-in themes for consistent aesthetics.
• Statistical plots like histograms, scatterplots, boxplots, heatmaps.

Use Case

Seaborn is perfect for exploring datasets visually to identify trends or relationships between variables.

Example

import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
tips = sns.load_dataset('tips')

# Create scatter plot
sns.scatterplot(x='total_bill', y='tip', hue='time', data=tips)

plt.title('Total Bill vs Tip')
plt.show()

NumPy

Overview

Provides support for multi-dimensional arrays and mathematical operations on them. Type: Numerical computation library.

Key Features:

• Efficient array operations using ndarray.
• Linear algebra functions (e.g., matrix multiplication).
• Random number generation utilities.

Use Cases:

• Performing mathematical computations on large datasets efficiently.
• Supporting backend computations for libraries like Pandas and Scikit-Learn.

Example:

import numpy as np

# Create array
arr = np.array([1, 2, 3])

# Perform operations
print(arr.mean())
print(arr * 2)

Comparison Table

Real-Life Scenarios

Scenario 1: Building a Deep Learning Model

• Use Pandas to clean the dataset (e.g., handle missing values).
• Use Seaborn to visualize relationships between variables (e.g., correlation heatmap).
• Preprocess numerical data using NumPy arrays.
• Build a neural network using Keras (tf.keras if using TensorFlow backend).

Scenario 2: Classical Machine Learning Workflow

• Load the dataset using Pandas into a DataFrame.
• Perform feature selection using Scikit-learn’s SelectKBest.
• Train a Random Forest model using Scikit-learn’s RandomForestClassifier.
• Evaluate the model’s performance using cross-validation metrics.

Scenario 3: Researching Large Datasets

• Use TensorFlow’s low-level APIs to handle distributed computing across GPUs/TPUs for large-scale datasets.
• Analyze intermediate results using NumPy arrays or Pandas DataFrames.

Choosing the Right Library

If you’re working on deep learning projects:

• Start with Keras if you’re a beginner or need quick prototyping.
• Use TensorFlow if you require advanced control or scalability.

If your focus is on classical machine learning:

• Scikit-learn is the best choice due to its extensive algorithm library and ease of use.

For data preprocessing:

• Pandas should be your go-to library.

For visualizing data:

• Use Seaborn for statistical plots or when working with Pandas DataFrames.

For Numerical Computations:

NumPy
The backbone of numerical computations in Python; use it directly or as a supporting library for other tools like Pandas or Scikit-Learn.

Real-World Workflow Example

A typical workflow might involve using multiple libraries together:

1. Use Pandas to load and preprocess the dataset:

   import pandas as pd
   data = pd.read_csv('dataset.csv')

2. Visualize relationships using Seaborn:

   import seaborn as sns
   sns.pairplot(data)

3. Train a model using Scikit-learn:

   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LogisticRegression

   X_train, X_test, y_train, y_test = train_test_split(X, y)
   model = LogisticRegression()
   model.fit(X_train, y_train)

4. Build a deep learning model using Keras/TensorFlow:

   from tensorflow.keras.models import Sequential
   from tensorflow.keras.layers import Dense

   model = Sequential([
       Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
       Dense(1, activation='sigmoid')
   ])

   model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
   model.fit(X_train, y_train)

Conclusion

Each library—Keras, TensorFlow, Pandas, Scikit-learn, and Seaborn—plays a unique role in the Python data science ecosystem:

• Use Keras/TensorFlow when working on deep learning projects requiring neural networks or large-scale computations.
• Leverage Pandas/NumPy for preprocessing structured numerical data efficiently before feeding it into models.
• Apply Scikit-learn when working with classical machine learning algorithms like regression or clustering.
• Utilize Seaborn to create insightful visualizations during exploratory analysis.

By combining these libraries effectively in your projects, you can build robust pipelines that handle everything from data preprocessing to advanced modeling and visualization!

Sources

[1] Keras vs. TensorFlow: Understanding the Powerhouse Duo of Deep … https://www.linkedin.com/pulse/keras-vs-tensorflow-understanding-powerhouse-duo-deep-learning
[2] Data Visualization with Seaborn – Python – GeeksforGeeks https://www.geeksforgeeks.org/data-visualization-with-python-seaborn/
[3] PyTorch vs TensorFlow vs Keras for Deep Learning – DataCamp https://www.datacamp.com/tutorial/pytorch-vs-tensorflow-vs-keras
[4] Python Seaborn Tutorial For Beginners: Start Visualizing Data https://www.datacamp.com/tutorial/seaborn-python-tutorial
[5] Difference between Keras and TensorFlow? : r/learnmachinelearning https://www.reddit.com/r/learnmachinelearning/comments/biynuy/difference_between_keras_and_tensorflow/
[6] seaborn: statistical data visualization — seaborn 0.13.2 documentation https://seaborn.pydata.org
[7] Difference between TensorFlow and Keras – GeeksforGeeks https://www.geeksforgeeks.org/difference-between-tensorflow-and-keras/
[8] Visualization with Seaborn | Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/04.14-visualization-with-seaborn.html
[9] Keras vs. tf.keras: What’s the difference in TensorFlow 2.0? https://pyimagesearch.com/2019/10/21/keras-vs-tf-keras-whats-the-difference-in-tensorflow-2-0/