Computer Vision News - April 2021

20 Computer Vision Tool Exploring the data further Let us see the percentage of adults and toddlers having ASD. < class ' pandas .core.frame.DataFrame'> RangeIndex: 1054 entries, 0 to 1053 Data columns (total 19 columns): Case_No 1054 non-null int64 A1 1054 non-null int64 A2 1054 non-null int64 A3 1054 non-null int64 A4 1054 non-null int64 A5 1054 non-null int64 A6 1054 non-null int64 A7 1054 non-null int64 A8 1054 non-null int64 A9 1054 non-null int64 A10 1054 non-null int64 Age_Mons 1054 non-null int64 Qchat- 10 -Score 1054 non-null int64 Sex 1054 non-null object Ethnicity 1054 non-null object Jaundice 1054 non-null object Family_mem_with_ASD 1054 non-null object Who completed the test 1054 non-null object Class/ASD Traits 1054 non-null object dtypes: int64( 13 ), object( 6 ) memory usage: 156.5 + KB sns.set_style('whitegrid') adults = df1[df1['Class/ASD' ]== 'YES'] toddlers = df2[df2['Class/ASD Traits '] == 'Yes'] print ("Adults (%): ", len(adults)/len(df1) * 100 ) print ("Toddlers (%):", len(toddlers)/len(df2) * 100 ) Which returns Adults: 26.84659090909091 Toddlers: 69.07020872865274 Now let’s get the info for the second dataframe (you can also do run the head command like before, to get an idea of what’s in there). df2.info () which returns