nextlet' extract some general information soit turns out that there is jufn file that not only contains the movie namesbut also all the genres that each movie belongs tonpwjf%jdu \xjuipqfo %bub djfodfnm  jufn btg ufnq gpsmjofjog gjfmet mjofstusjq = tqmju npwjf*jou gjfmet obnf gjfmet hfosft gjfmet hfosft nbq jouhfosft npwjf%jdu obnfhfosftnpwjf/psnbmj[fe/vn bujohtmpdhfu tj[ npwjf spqfsujftmpd<npwj *%>sbujohhfu nfbo the code above will actually go through each line of jufn we're doing this the hard waywe're not using any pandas functionswe're just going to use straight-up python this time againmake sure you change that path to wherever you installed this information nextwe open our jufn fileand then iterate through every line in the file one at time we strip out the new line at the end and split it based on the pipe-delimiters in this file thenwe extract the npwjf*%the movie name and all of the individual genre fields so basicallythere' bunch of and in different fields in this source datawhere each one of those fields represents given genre we then construct python dictionary in the end that maps movie ids to their namesgenresand then we also fold back in our rating information sowe will have namegenrepopularity on scale of to and the average rating sothat' what this little snippet of code does let' run thatandjust to see what we end up withwe can extract the value for npwjf@jenpwjf%jdu following is the output of the preceding codeentry in our dictionary for npwjf@jehappens to be toy storyan old pixar film from you've probably heard of next is list of all the genreswhere indicates it is not part of that genreand indicates it is part of that genre there is data file in the pwjf-fot dataset that will tell you what each of these genre fields actually corresponds to