问题
I'm trying to use objects as column headers in a multi indexed
dataframe but I can't seem to get it to work. __eq__, __hash__
and __str__
only work for simple data frames.
Here is a small example:
class Signal:
def __init__(self, name):
self.name = name
def __eq__(self, other):
try:
return self.name == other or self.name == other.name
except AttributeError as err:
return False
def __str__(self):
return str(self.name)
def __hash__(self):
return hash(self.name)
if __name__ == '__main__':
import pandas as pd
import numpy as np
a = Signal('name')
b = Signal('name2')
c = Signal('something')
data = {
('A', a): np.arange(2),
('A', b): np.ones(2),
('B', c): np.zeros(2)
}
df = pd.DataFrame(data)
print(df)
print('-----------')
print(df['A'])
I also tried implementing __le__, __ge__
and __ne__
. That did not
do anything though. I don't really have a clue what else I could do.
Anybody got some ideas?
回答1:
After defining __lt__
and __gt__
class Signal:
def __init__(self, name):
self.name = name
def __eq__(self, other):
try:
return self.name == other or self.name == other.name
except AttributeError as err:
return False
def __lt__(self, other):
return self.name < other.name
def __gt__(self, other):
return self.name > other.name
def __str__(self):
return str(self.name)
def __hash__(self):
return hash(self.name)
import pandas as pd
import numpy as np
a = Signal('name')
b = Signal('name2')
c = Signal('something')
data = {
('A', a): np.arange(2),
('A', b): np.ones(2),
('B', c): np.zeros(2)
}
df = pd.DataFrame(data)
print(df, df['A'], sep='\n\n')
A B
name name2 something
0 0 1.0 0.0
1 1 1.0 0.0
name name2
0 0 1.0
1 1 1.0
来源:https://stackoverflow.com/questions/49563981/pandas-custom-class-as-column-header-with-multi-indexing