CODEWITHSUNDEEP
pythonpandasslicelogical-operatorsboolean-expression
user8270077
user8270077

Reputation: 5071

Slicing with a logical (boolean) expression a Pandas Dataframe

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]

Upvotes: 5

Views: 8650

Answers (2)

jpp
jpp

Reputation: 164613

You are suffering from the effects of chained comparisons. What's happening is the expression df['GDP_norm'] >=3.5 & df['GDP_norm'] <= 4.5 is evaluated as something like:

df['GDP_norm'] >= (3.5 & df['GDP_norm']) <= 4.5

Of course, this fails since float cannot be compared with bool, as described in your error message. Instead, use parentheses to isolate each Boolean mask and assign to variables:

m1 = (df['GDP_norm'] >= 3.5) & (df['GDP_norm'] <= 4.5)
m2 = df['SP500_Index_deflated_norm'] > 3

m3 = (df['GDP_norm'] >= 4.0) & (df['GDP_norm'] <= 5.0)
m4 = df['SP500_Index_deflated_norm'] < 3.5

res = df[(m1 & m2) | (m3 & m4)]

Upvotes: 1

jezrael
jezrael

Reputation: 862406

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]

Upvotes: 7

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