Python Data Science & ML Guide 2026

Key Libraries

Data Manipulation

• Pandas: Data analysis
• NumPy: Numerical computing
• SciPy: Scientific computing

Visualization

• Matplotlib: Charts
• Plotly: Interactive
• Seaborn: Statistics

Machine Learning

• scikit-learn: Classical ML
• PyTorch: Deep learning
• TensorFlow: Production ML

NLP

• Hugging Face Transformers
• spaCy
• NLTK

Example Code

Pandas

import pandas as pd
df = pd.read_csv('data.csv')
df['total'] = df['qty'] * df['price']

scikit-learn

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)

PyTorch

import torch.nn as nn
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(10, 1)

Career Opportunities

• Data Scientist: ₹6-15 LPA
• ML Engineer: ₹8-20 LPA
• AI Developer: ₹10-25 LPA

Conclusion

Python is essential for data professionals in 2026.