Element-wise logical OR in Pandas
Question:
I am aware that AND corresponds to & and NOT, ~. What is the element-wise logical OR operator? I know "or" itself is not what I am looking for.
Answers:
The corresponding operator is |:
df[(df < 3) | (df == 5)]
would elementwise check if value is less than 3 or equal to 5.
If you need a function to do this, we have np.logical_or. For two conditions, you can use
df[np.logical_or(df<3, df==5)]
Or, for multiple conditions use the logical_or.reduce,
df[np.logical_or.reduce([df<3, df==5])]
Since the conditions are specified as individual arguments, parentheses grouping is not needed.
More information on logical operations with pandas can be found here.
To take the element-wise logical OR of two Series a and b just do
a | b
If you operate on the columns of a single dataframe, eval and query are options where or works element-wise. You don’t need to worry about parenthesis either because comparison operators have higher precedence than boolean/bitwise operators. For example, the following query call returns rows where column A values are >1 and column B values are > 2.
df = pd.DataFrame({'A': [1,2,0], 'B': [0,1,2]})
df.query('A > 1 or B > 2') # == df[(df['A']>1) | (df['B']>2)]
# A B
# 1 2 1
or with eval you can return a boolean Series (again or works just fine as element-wise operator).
df.eval('A > 1 or B > 2')
# 0 False
# 1 True
# 2 False
# dtype: bool
I am aware that AND corresponds to & and NOT, ~. What is the element-wise logical OR operator? I know "or" itself is not what I am looking for.
The corresponding operator is |:
df[(df < 3) | (df == 5)]
would elementwise check if value is less than 3 or equal to 5.
If you need a function to do this, we have np.logical_or. For two conditions, you can use
df[np.logical_or(df<3, df==5)]
Or, for multiple conditions use the logical_or.reduce,
df[np.logical_or.reduce([df<3, df==5])]
Since the conditions are specified as individual arguments, parentheses grouping is not needed.
More information on logical operations with pandas can be found here.
To take the element-wise logical OR of two Series a and b just do
a | b
If you operate on the columns of a single dataframe, eval and query are options where or works element-wise. You don’t need to worry about parenthesis either because comparison operators have higher precedence than boolean/bitwise operators. For example, the following query call returns rows where column A values are >1 and column B values are > 2.
df = pd.DataFrame({'A': [1,2,0], 'B': [0,1,2]})
df.query('A > 1 or B > 2') # == df[(df['A']>1) | (df['B']>2)]
# A B
# 1 2 1
or with eval you can return a boolean Series (again or works just fine as element-wise operator).
df.eval('A > 1 or B > 2')
# 0 False
# 1 True
# 2 False
# dtype: bool