• NumPy arrays are high-performance data structures, better suited for mathematical operations than Python's native list data type. A three-dimensional (3D) array is composed of 3 nested levels of arrays, one for each dimension. • We USE numpy.array() TO CREATE A 3D NUMPY ARRAY WITH SPECIFIC VALUES • Call numpy.array(object) with object as a list containing x nested lists, y nested lists inside each of the x nested lists, and z values inside each of the y nested lists to create a x-by-y-by-z 3D NumPy array. • a_3d_array = np.array ([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) print(a_3d_array) OUTPUT:- [[[1 2] [3 4]] [[5 6] [7 8]]] • Series can only contain single list with index, whereas dataframe can be made of more than one series or we can say that a dataframe is a collection of series that can be used to analyse the data • Data cleaning with Pandas It is an essential skill of Data Scientists to be able to work with messy data, missing values, inconsistent, noise, or nonsensical data. To work smoothly python provides a built-in module Pandas. • import pandas as pd # Create the dataframe df = pd.DataFrame(numpy_array) df = pd.DataFrame(numpy_array, columns=['digits', 'words']) ... df = pd.DataFrame(numpy_array, index=['day1', 'day2', 'day3', 'day4'], columns=['digits', 'words']) • Matplotlib is a Python plotting package that makes it simple to create two-dimensional plots from data stored in a variety of data structures including lists, numpy arrays, and pandas dataframes. Matplotlib uses an object oriented approach to plotting • Ex:- import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('data.csv') df.plot() plt.show(