System:
You are an experienced data engineer, tasked to clean the bad-data.
Your goal is to analyze the data and locate the bad data in given values. A value can be called as bad data if it is a misspelled name, an outlier (excessively large or small value), or inconsistent units for example temperature column containing most values in Celsius, but few in Fahrenheit, then Fahrenheit values are bad data, and should be converted to Celsius. A bad data can either be corrected or removed if it is not possible to predict the correct value.
User:
Analyze the data and locate bad data from all the values, return the indexes of all the bad data values, and if there is no bad data return [] You must not treat null values, wrong formatting, or values with extra substrings as bad data.
User: 0 0.000042 1 83.000000 2 75.000000 3 38.000000 4 56.000000 5 64.000000 6 74.000000 7 23.000000 8 757483.000000 9 75.000000
You: [0, 8] 0.000042 and 757483.000000 are outliers.
User: 0 453 1 294.8 2 $563.8 3 634.1 4 $645.9
You: [] There is no outlier, and all the values with units have same unit. There are some values with '$' symbol but that is a format issue, not a bad data as per instructions.
User: 0 453 1 294.8 2 $563.8 3 634.1 4 €645.9
You: [4] €645.9 has inconsistent unit. It need to be converted to equivalent dollar amount.
User:
0 $98204 1 21130 2 59231 3 91309 4 39532 5 95297 6 26768 7 78195 8 38557 9 85537 10 66799 11 82237 12 53198 13 92785 14 53149 15 48872 16 86771 17 93421 18 57543 19 73058 20 50237 21 74415 22 63761 23 87787 24 76106 25 41926 26 84097 27 51314 28 27469 29 73199 30 75829 31 40459 32 60116 33 45179 34 81077 35 20793 36 31276 37 77597 38 68535 39 49384 40 $80563 41 73353 42 52478 43 20106 44 94472 45 39264 46 26565 47 43310 48 76247 49 39330 50 92989 51 52340 52 53894 53 69064 54 69291 55 $87000$87000$87000$87000$87000$87000$87000$870... 56 20118 57 93596 58 58713 59 25937 60 67098 61 81715 62 63927 63 50523 64 448420 65 21717 66 96817 67 57262 68 46477 69 72925 70 73763 71 76301 72 $78007 73 84036 74 49645 75 73828 76 23343 77 39054 78 54102 79 96482 80 $74186 81 40946 82 90488 83 47823 84 71658 85 82283 86 82112 87 48538 88 66100 89 52086 90 20573 91 39285 93 52492 94 62577 95 29646 96 36252 97 92795 98 98496 99 44964 Name: income, dtype: object
You:
[0, 40, 55, 72, 80] The values at indexes 0, 40, and 55 contain the '$' symbol, which is inconsistent with the majority of the values that are numeric. The value at index 55 is particularly problematic as it contains repeated '$87000', indicating a formatting issue. Additionally, the value at index 64 (448420) is an outlier, being excessively large compared to the other values.