Essential Python Libraries for Data Science

The Data Science Stack

Python dominates data science thanks to these powerful libraries. Master them and you can handle most data tasks.

NumPy — Numerical Computing

import numpy as np
arr = np.array([1, 2, 3, 4])
arr.mean()   # 2.5
arr * 2      # [2, 4, 6, 8]

Pandas — Data Manipulation

import pandas as pd
df = pd.read_csv("data.csv")
df.head()
df[df["age"] > 18]
df.groupby("city")["sales"].sum()

Matplotlib / Seaborn — Visualization

import matplotlib.pyplot as plt
plt.plot(x, y)
plt.show()

scikit-learn — Machine Learning

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
model.predict(X_test)

The Full Stack

Library	Purpose
NumPy	Arrays & math
Pandas	DataFrames
Matplotlib/Seaborn	Charts
scikit-learn	ML models
TensorFlow/PyTorch	Deep learning

FAQs

Which to learn first?

Pandas and NumPy — they're the foundation. More in our Data Science guides.

Jupyter Notebook?

Yes — the standard interactive environment for data science.