not fit into memory)so we need to create sparse representations directly here is way to create the same sparse matrix as beforeusing the coo formatin[ ]data np ones( row_indices np arange( col_indices np arange( eye_coo sparse coo_matrix((data(row_indicescol_indices))print("coo representation:\ {}format(eye_coo)out[ ]coo representation( ( ( ( more details on scipy sparse matrices can be found in the scipy lecture notes matplotlib matplotlib is the primary scientific plotting library in python it provides functions for making publication-quality visualizations such as line chartshistogramsscatter plotsand so on visualizing your data and different aspects of your analysis can give you important insightsand we will be using matplotlib for all our visualizations when working inside the jupyter notebookyou can show figures directly in the browser by using the %matplotlib notebook and %matplotlib inline commands we recommend using %matplotlib notebookwhich provides an interactive environment (though we are using %matplotlib inline to produce this bookfor examplethis code produces the plot in figure - in[ ]%matplotlib inline import matplotlib pyplot as plt generate sequence of numbers from - to with steps in between np linspace(- create second array using sine np sin(xthe plot function makes line chart of one array against another plt plot(xymarker=" "essential libraries and tools