Final productYour final product will be a pandas dataframe of college names sort
Final productYour final product will be a pandas dataframe of college names sorted in descending order by the percent difference between in-state and out-of-state tuitionRaw data sourcesYour “raw” data sources are both from https://collegescorecard.ed.gov, with some modifications to optimize the data for this midterm:a slightly massaged version of the “CollegeScorecardDataDictionary.xlsx” that has been converted to a tab-delimited text file and reshaped This ~140MB file has the data you need to produce your final product. Unfortunately, it hasCollegeScorecardDataDictionary_wide.txtTo help you decipher the contents of the data file, the “Data Dictionary” is provided. This is NOT a python dictionary. Rather, it is a tab-delimited table that describes the column labels in the data file, as well as a “decoder ring” for the numeric codes used in some of the data columns.Unfortunately, it hasdef tab_import(fname): df=pd.read_csv(fname,sep=’t’) return df#TESTdd_df = tab_import(‘../resource/lib/publicdata/m2p1/CollegeScorecardDataDictionary_wide.txt’)function that def split_by_coded(full_df): ### ### YOUR CODE HERE ### return coded_df, not_coded_dfdef melt_together(coded_df, not_coded_df): ### ### YOUR CODE HERE ### return melted_df#TESTm_df= melt_together(c_df, n_c_df)
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