we can observe from the above output that first column gives the row number which can be very useful for referencing specific observation checking dimensions of data it is always good practice to know how much datain terms of rows and columnswe are having for our ml project the reasons behind aresuppose if we have too many rows and columns then it would take long time to run the algorithm and train the model suppose if we have too less rows and columns then it we would not have enough data to well train the model following is python script implemented by printing the shape property on pandas data frame we are going to implement it on iris data set for getting the total number of rows and columns in it example from pandas import read_csv path " :\iris csvdata read_csv(pathprint(data shapeoutput ( we can easily observe from the output that iris data setwe are going to useis having rows and columns getting each attribute' data type it is another good practice to know data type of each attribute the reason behind is thatas per to the requirementsometimes we may need to convert one data type to another for examplewe may need to convert string into floating point or int for representing categorial or ordinal values we can have an idea about the attribute' data type by looking at the raw databut another way is to use dtypes property of pandas dataframe with