Data_Analytics

Popular Libraries in Python for Data Analytics

NumPy:

Provides support for large multi-dimensional arrays and matrices. Contains a collection of mathematical functions to operate on these arrays.
import numpy as np

Pandas:

Offers data structures like Series and DataFrame for data manipulation and analysis. Provides functionalities for reading and writing data, handling missing values, and merging datasets.
import pandas as pd

Matplotlib:

A plotting library for creating static, interactive, and animated visualizations in Python.
import matplotlib.pyplot as plt

Seaborn:

Built on top of Matplotlib, it provides a high-level interface for drawing attractive and informative statistical graphics.
import seaborn as sns

Scikit-learn:

A machine learning library that provides simple and efficient tools for data mining and data analysis. Includes algorithms for classification, regression, clustering, and more.
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

SciPy:

Builds on NumPy and provides additional tools for optimization, integration, and other scientific computations.

Statsmodels:

Provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests.