product id int quantity purchased int serial no int user id int user type object dtypeobject the column names are clearly listed and have been explained previously upon inspecting the data typeswe can clearly see that the date attribute is represented as an object before we move on to transformations and cleanuplet' dig in further and collect more information to understand and prepare strategy of required tasks for dataset wrangling the following snippet helps get information related to attributes/columns containing missing valuescount of rowsand indices that have missing values in them print("columns with missing values::",df columns[df isnull(any()tolist()print("number of rows with missing values::",len(pd isnull(dfany( nonzero()[ tolist())print("sample indices with missing data::",pd isnull(dfany( nonzero()[ tolist()[ : with pandassubscripting works with both rows and columns (see for detailswe use isnull(to identify columns containing missing values the utilities any(and nonzero(provide nice abstractions to identify any row/column conforming to condition (in this case pointing to rows/columns having missing valuesthe output is as follows columns with missing values:['date''price''user type'number of rows with missing values: sample indices with missing data:[ llet' also do quick fact checking to get details on non-null rows for each column and the amount of memory consumed by this dataframe we also get some basic summary statistics like minmaxand so onthese will be useful in coming tasks for the first taskwe use the info(utility while the summary statistics are provided by the describe(function the following snippet does this print("general stats::"print(df info()print("summary stats::print(df describe()the following is the output generated using the info(and describe(utilities it shows date and price both have about non-null rowswhile the dataset consumes close to kb of memory the summary stats are self-explanatory and drop the non-numeric columns like date and user type from the output general stats:rangeindex entries to data columns (total columns)date non-null object price non-null float product id non-null int quantity purchased non-null int