Explanation of why query breaks when I add a second condition?
My query works as long as I only query in one column. It breaks when I try to add a query for the second column in my dataframe. I am using python3 and pandas.
I get back all the rows with 'MTC' in my 'Person' column. However, when I add an 'and' to get narrow down those rows, it turns into a boolean condition and I get an error message. How can I pull out all the rows with 'MTC' in the 'Person' column, that also have 3 in the 'ExpNum' column?
Works great:
print(df.loc[df['Person'] == 'MTC'])
OrigID Person Date Threshold RevNum WL RevPos ExpNum \
5032 3027 MTC 5/24/2018 0 1 440 16.881222 3
5033 3032 MTC 5/24/2018 0 2 440 22.881222 3
5034 3059 MTC 5/24/2018 0 3 440 6.431222 3
5035 3064 MTC 5/24/2018 0 4 440 12.431222 3
5036 3070 MTC 5/24/2018 0 5 440 12.431222 3
5037 3072 MTC 5/24/2018 0 6 440 13.931222 3
5038 3118 MTC 5/24/2018 0 7 440 5.731222 3
5039 3119 MTC 5/24/2018 0 8 440 7.231222 3
5040 3130 MTC 5/24/2018 0 9 440 2.311222 3
5041 3131 MTC 5/24/2018 0 10 440 5.311222 3
5042 3132 MTC 5/24/2018 0 11 440 5.311222 3
5043 3133 MTC 5/24/2018 0 12 440 6.811222 3
5044 3023 MTC 5/24/2018 0 1 452 26.063219 3
5045 3026 MTC 5/24/2018 0 2 452 22.773219 3
5046 3039 MTC 5/24/2018 0 3 452 40.773219 3
5047 3127 MTC 5/24/2018 0 4 452 19.453219 3
5048 3128 MTC 5/24/2018 0 5 452 20.953219 3
5049 3135 MTC 5/24/2018 0 6 452 12.753219 3
5050 3136 MTC 5/24/2018 0 7 452 14.253219 3
5051 3137 MTC 5/24/2018 0 8 452 14.253219 3
5052 3139 MTC 5/24/2018 0 9 452 15.753219 3
5053 3140 MTC 5/24/2018 0 10 452 11.653219 3
5054 3141 MTC 5/24/2018 0 11 452 13.153219 3
5055 3142 MTC 5/24/2018 0 12 452 9.873219 3
5056 3029 MTC 5/24/2018 0 1 464 18.288780 3
5057 3034 MTC 5/24/2018 0 2 464 30.288780 3
5058 3053 MTC 5/24/2018 0 3 464 0.000000 3
5059 3057 MTC 5/24/2018 0 4 464 6.000000 3
5060 3058 MTC 5/24/2018 0 5 464 6.000000 3
5061 3061 MTC 5/24/2018 0 6 464 7.500000 3
... ... ... ... ... ... ... ... ...
6413 3208 MTC 5/24/2018 1 7 464 -23.402228 4
6414 3215 MTC 5/24/2018 1 8 464 -24.902228 4
6415 3250 MTC 5/24/2018 1 9 464 -20.802228 4
6416 3251 MTC 5/24/2018 1 10 464 -22.302228 4
6417 3256 MTC 5/24/2018 1 11 464 -21.482228 4
6418 3257 MTC 5/24/2018 1 12 464 -24.482228 4
6419 3156 MTC 5/24/2018 1 1 476 -19.485844 4
6420 3157 MTC 5/24/2018 1 2 476 -25.485844 4
6421 3160 MTC 5/24/2018 1 3 476 -22.195844 4
6422 3161 MTC 5/24/2018 1 4 476 -28.195844 4
6423 3188 MTC 5/24/2018 1 5 476 -24.915844 4
6424 3196 MTC 5/24/2018 1 6 476 -26.415844 4
6425 3204 MTC 5/24/2018 1 7 476 -26.415844 4
6426 3211 MTC 5/24/2018 1 8 476 -29.415844 4
6427 3230 MTC 5/24/2018 1 9 476 -29.415844 4
6428 3241 MTC 5/24/2018 1 10 476 -30.915844 4
6429 3248 MTC 5/24/2018 1 11 476 -30.095844 4
6430 3252 MTC 5/24/2018 1 12 476 -33.095844 4
6431 3150 MTC 5/24/2018 1 1 488 -28.084528 4
6432 3153 MTC 5/24/2018 1 2 488 -24.794528 4
6433 3154 MTC 5/24/2018 1 3 488 -30.794528 4
6434 3167 MTC 5/24/2018 1 4 488 -28.334528 4
6435 3172 MTC 5/24/2018 1 5 488 -29.834528 4
6436 3176 MTC 5/24/2018 1 6 488 -29.014528 4
6437 3191 MTC 5/24/2018 1 7 488 -35.014528 4
6438 3203 MTC 5/24/2018 1 8 488 -34.194528 4
6439 3205 MTC 5/24/2018 1 9 488 -35.694528 4
6440 3213 MTC 5/24/2018 1 10 488 -34.054528 4
6441 3219 MTC 5/24/2018 1 11 488 -35.554528 4
6442 3221 MTC 5/24/2018 1 12 488 -35.554528 4
ThExp
5032 Inc-Exp3
5033 Inc-Exp3
5034 Inc-Exp3
5035 Inc-Exp3
5036 Inc-Exp3
5037 Inc-Exp3
5038 Inc-Exp3
5039 Inc-Exp3
5040 Inc-Exp3
5041 Inc-Exp3
5042 Inc-Exp3
5043 Inc-Exp3
5044 Inc-Exp3
5045 Inc-Exp3
5046 Inc-Exp3
5047 Inc-Exp3
5048 Inc-Exp3
5049 Inc-Exp3
5050 Inc-Exp3
5051 Inc-Exp3
5052 Inc-Exp3
5053 Inc-Exp3
5054 Inc-Exp3
5055 Inc-Exp3
5056 Inc-Exp3
5057 Inc-Exp3
5058 Inc-Exp3
5059 Inc-Exp3
5060 Inc-Exp3
5061 Inc-Exp3
... ...
6413 Dec-Exp4
6414 Dec-Exp4
6415 Dec-Exp4
6416 Dec-Exp4
6417 Dec-Exp4
6418 Dec-Exp4
6419 Dec-Exp4
6420 Dec-Exp4
6421 Dec-Exp4
6422 Dec-Exp4
6423 Dec-Exp4
6424 Dec-Exp4
6425 Dec-Exp4
6426 Dec-Exp4
6427 Dec-Exp4
6428 Dec-Exp4
6429 Dec-Exp4
6430 Dec-Exp4
6431 Dec-Exp4
6432 Dec-Exp4
6433 Dec-Exp4
6434 Dec-Exp4
6435 Dec-Exp4
6436 Dec-Exp4
6437 Dec-Exp4
6438 Dec-Exp4
6439 Dec-Exp4
6440 Dec-Exp4
6441 Dec-Exp4
6442 Dec-Exp4
[283 rows x 9 columns]
Does not work:
print(df.loc[df['Person'] == 'MTC' and df['ExpNum'] == '3'])
ValueError Traceback (most recent call last)
<ipython-input-32-3a09e9a5facb> in <module>()
----> 1 print(df.loc[df['Person'] == 'MTC' and df['ExpNum'] == '3'])
c:\users\rijekah\appdata\local\programs\python\python35\lib\site-packages\pandas\core\generic.py in __nonzero__(self)
1571 raise ValueError("The truth value of a {0} is ambiguous. "
1572 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
-> 1573 .format(self.__class__.__name__))
1574
1575 __bool__ = __nonzero__
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
I am using python 3
I think the error message is telling me that my query is a boolean true false and that it cannot respond??
How should I change my code so that it only brings back rows where Person == MTC and ExpNum == 3 and WL == 440 ?
