I am getting an exception as I try to slice with a logical expression my Pandas dataframe.
My data have the following form:
df
GDP_norm SP500_Index_deflated_norm
Year
1980 2.121190 0.769400
1981 2.176224 0.843933
1982 2.134638 0.700833
1983 2.233525 0.829402
1984 2.395658 0.923654
1985 2.497204 0.922986
1986 2.584896 1.09770
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 38 entries, 1980 to 2017
Data columns (total 2 columns):
GDP_norm 38 non-null float64
SP500_Index_deflated_norm 38 non-null float64
dtypes: float64(2)
memory usage: 912.0 bytes
The command is the following:
df[((df['GDP_norm'] >=3.5 & df['GDP_norm'] <= 4.5) & (df['SP500_Index_deflated_norm'] > 3)) | (
(df['GDP_norm'] >= 4.0 & df['GDP_norm'] <= 5.0) & (df['SP500_Index_deflated_norm'] < 3.5))]
The error message is the following:
TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool]
I suggest create boolean masks separately for better readibility and also easier error handling.
Here are missing () in m1 and m2 code, problem is in operator precedence:
docs - 6.16. Operator precedence where see & have higher priority as >=:
Operator Description
lambda Lambda expression
if – else Conditional expression
or Boolean OR
and Boolean AND
not x Boolean NOT
in, not in, is, is not, Comparisons, including membership tests
<, <=, >, >=, !=, == and identity tests
| Bitwise OR
^ Bitwise XOR
& Bitwise AND
(expressions...), [expressions...], Binding or tuple display, list display,
{key: value...}, {expressions...} dictionary display, set display
m1 = (df['GDP_norm'] >=3.5) & (df['GDP_norm'] <= 4.5)
m2 = (df['GDP_norm'] >= 4.0) & (df['GDP_norm'] <= 5.0)
m3 = m1 & (df['SP500_Index_deflated_norm'] > 3)
m4 = m2 & (df['SP500_Index_deflated_norm'] < 3.5)
df[m3 | m4]