1. Numpy: Numpy (Numerical Python) is a fundamental package for scientific computing with Python. It provides various mathematical functions for arrays and matrices, enabling array operations to be performed more efficiently than native Python arrays.
Example: # Importing numpy import numpy as np
# Creating a numpy array a = np.array([1,2,3]) b = np.array([4,5,6])
# Adding two numpy arrays c = a + b
# Multiplying two numpy arrays d = a * b
Package: numpy
2. Pandas: Pandas is an open-source data manipulation and analysis library. It provides data structures to effectively handle structured data like csv and excel. It is built on top of Numpy package and provides easy-to-use data manipulation functions.
Example: # Importing pandas library import pandas as pd
# Reading a csv file data = pd.read_csv('filename.csv')
# Selecting a column column = data['column_name']
# Filtering data using condition filtered_data = data[data['column_name']>2]
Package: pandas
3. Matplotlib: Matplotlib is a plotting library for creating static, animated, and interactive visualizations in Python. It provides a simple yet versatile way to create custom visualizations.
Example: # Importing matplotlib library import matplotlib.pyplot as plt
# Creating a line plot x = [1, 2, 3, 4, 5] y = [3, 6, 2, 7, 1] plt.plot(x, y)
# Adding axis labels and title plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Line Plot')
Package: matplotlib
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