in practicemost of the arrays are created during reading in the data we will cover the text data retrieval operations of numpy very briefly as we will try to use pandas generallyfor our data ingestion process (more on this in later part of the one of the functions that we can use to read data from text file to numpy array is genfromtext this function can open text file and read in data delimited by any character (delimiter for comma separated file is ","since it is not our preferred way of retrieving datawe will give brief example of the function here in [ ] bytesio( " , , \ , , \ , , "arr np genfromtxt(bdelimiter=","arr out[ ]array([ ] ] ]]accessing array elements once we have created an array by reading in our datathe next important part is to access that data using wide variety of mechanisms numpy provides lot of ways in which array elements can be accessed we will try to give the most popular useful ways that facilitate this basic indexing and slicing ndarray can leverage the basic indexing operations that are followed by the list classi list object [objif the obj is not an ndarray objectthen the indexing is said to be basic indexing ##note one important point to remember is that basic indexing will always return view of the original array it means that it will only refer to the original array and any change in values will be reflected in the original array also for exampleif we want to access the complete second row of the array in one of the earlier exampleswe can simply refer to it using arr[ in [ ]arr[ out[ ]array([ ]this access becomes interesting in the case of an array having more than two dimensions consider the following code snippet in [ ]arr np arange( reshape( , , in [ ]arr out[ ]array([[ ] ]][ ] ]]]