### Understanding np.where and its Applications in Pandas

NumPy's np.where function is a versatile tool that can be used for conditional execution. It's especially handy when working with large datasets in pandas DataFrame or Series objects. In this post, we'll unravel the use of np.where in a real-life data manipulation task involving a movie dataset. Here's a code snippet we'll be discussing (Google Colab Interactive Example ): ************************Code Snippet*******************************; import pandas as pd import numpy as np loc = 'https://raw.githubusercontent.com/aew5044/Python---Public/main/movie.csv' m = pd.read_csv(loc) m1 = ( m .assign(color = lambda x: x.color.fillna('Missing')) #Fill in missing values with "Missing" .assign(bw = lambda x: np.where(x.color != 'Color', 1, 0)) #Create a new column called "bw" .assign(bw_after_1939 = lambda x: np.where((x.title_year > 1939) & (x.color.str.startswith('B') | x.color.str.startswith('