I am trying to call the sum_method function from my evaluation class to my main one, however I run into many errors. I want to use the new_data as the data parameter of my sum_method function.
evaluation class:
class evaluation():
def __init__(self, data):
self.data = data
def sum_method(self):
montant_init = self.data.loc[self.data['Initiateur'] == 'Glovoapp', 'Montant (centimes)'].sum()
print(montant_init)
main class:
class main(evaluation):
new_data.to_csv("transactions.csv", index=False)
self.data = new_data
def call_sum(self, new_data):
init_eval = evaluation.sum_method(self=new_data)
print(init_eval)
init_evalobj = main()
init_evalobj.call_sum()
if you use the method in your inherence class just use self
so:
init_eval = self.sum_method()
the self argument is passed in python automaticly as first parameter
update
you also should return a value:
def sum_method(self):
montant_init = self.data.loc[self.data['Initiateur'] == 'Glovoapp', 'Montant (centimes)'].sum()
print(montant_init)
return montant_init
I'd suggest making some changes to the both classes, to encapsulate the .data member variable in the base class. My preference would also be to separate out the calculation from the display, so leave all the print statements in the call_sum() function.
class evaluation:
def __init__(self, data):
self.data = data
def sum_method(self):
montant_init = self.data.loc[self.data['Initiateur'] == 'Glovoapp', 'Montant (centimes)'].sum()
return montant_init
class main(evaluation):
def __init__(self):
# Reduce csv content to what's needed for analysis
data_csv = pd.read_csv('transactions.csv')
# --> removing unnecessary data
new_data = data_csv[['Opération', 'Initiateur', 'Montant (centimes)', 'Monnaie',
'Date', 'Résultat', 'Compte marchand', 'Adresse IP Acheteur', 'Marque de carte']]
# --> saving changes...
new_data.to_csv("transactions.csv", index=False)
super().__init__(new_data) //Initialize the base class
def call_sum(self):
print('Glovoapp "montant" generated')
init_eval = self.sum_method() //Call the method from the base class
print(init_eval)
Related
Instead of using a dict to store and pass data we are going completely OOPS approach of storing the data as class attributes and call the get methods defined according to need.
In Java i was able to achieve this but having some trouble in Python. Any Solution would be helpful.
import json
class InputModel:
def __init__(self, input_payload):
self.id1 = input_payload["id1"]
self.route = RouteModel(input_payload["route"])
self.id2 = input_payload["id2"]
self.id3 = input_payload["id3"]
self.id4 = input_payload["id4"]
self.id5 = input_payload["id5"]
def get_id1(self):
return self.id1
#similar for other ids
class RouteModel:
def __init__(self, input_payload_route):
self.id6 = input_payload_route["id6"]
self.id7 = input_payload_route["id7"]
def get_id6(self):
return self.id6
#similar for other ids
json_str = '{"id1":"string","route":{"id6":"string","id7":"string"},"id2": "string","id3": "string","id4": "string","id5": "string"}'
json_dict = json.loads(json_str)
im = InputModel(json_dict)
print(im.get_id1())
print(im.get_id6())
not able to access the nested class attributes
Seems like you went for 1 extra indent in your class methods, thus you couldn't reach them.
Also, to reach id6 of RouteModel, you had to refer to 'route' first:
import json
class InputModel:
def __init__(self, input_payload):
self.id1 = input_payload["id1"]
self.route = RouteModel(input_payload["route"])
self.id2 = input_payload["id2"]
self.id3 = input_payload["id3"]
self.id4 = input_payload["id4"]
self.id5 = input_payload["id5"]
def get_id1(self):
return self.id1
#similar for other ids
class RouteModel:
def __init__(self, input_payload_route):
self.id6 = input_payload_route["id6"]
self.id7 = input_payload_route["id7"]
def get_id6(self):
return self.id6
#similar for other ids
json_str = '{"id1":"string","route":{"id6":"string","id7":"string"},"id2": "string","id3": "string","id4": "string","id5": "string"}'
json_dict = json.loads(json_str)
im = InputModel(json_dict)
print(im.get_id1())
print(im.route.get_id6())
Output:
string
string
The problem is that you are only defining get_id* in your local scope, you need to assign it to the instance if you insist on defining it inside the __init__ method.
I minimized your code example to isolate your issue.
class RouteModel:
def __init__(self):
self.id6 = "foo"
def get_id6(self_=self):
return self_.id6
self.get_id6 = get_id6
rm = RouteModel()
print(rm.get_id6())
>>> "foo"
If I understand your question correctly, you want to be able to access the ids directly as attributes, no matter how deep they are nested in the dictionary.
This solution creates the attributes recursively:
import json
class InputModel:
def __init__(self, payload):
self.create_attrs(payload)
def create_attrs(self, d):
for key, value in d.items():
# if the value is a dict, call create_attrs recursively
if isinstance(value, dict):
self.create_attrs(value)
else:
# create an attribute key=value, e.g. id1="string"
setattr(self, key, value)
json_str = '{"id1":"string","route":{"id6":"string","id7":"string"},"id2": "string","id3": "string","id4": "string","id5": "string"}'
json_dict = json.loads(json_str)
im = InputModel(json_dict)
print(im.id1)
print(im.id6)
After going through answers provided, mostly have defined instance attributes and not class attributes.
Correct me if I'm wrong here but I think this is how class attributes are defined right?
import json
class InputModel:
def __init__(self, input_payload):
InputModel.id1 = input_payload["id1"]
InputModel.route = RouteModel(input_payload["route"])
InputModel.id2 = input_payload["id2"]
InputModel.id3 = input_payload["id3"]
InputModel.id4 = input_payload["id4"]
InputModel.id5 = input_payload["id5"]
def get_id1():
return InputModel.id1
#OR
##classmethod
#def get_id1(cls):
# return cls.id1
#similar for other ids
class RouteModel:
def __init__(self, input_payload_route):
RouteModel.id6 = input_payload_route["id6"]
RouteModel.id7 = input_payload_route["id7"]
def get_id6():
return RouteModel.id6
#similar for other ids
json_str = '{"id1":"string","route":{"id6":"string","id7":"string"},"id2": "string","id3": "string","id4": "string","id5": "string"}'
json_dict = json.loads(json_str)
InputModel(json_dict)
print(InputModel.get_id1())
print(InputModel.route.get_id6())
print(RouteModel.get_id6())
I've made a class as follow:
class Plugins:
def __init__(self):
pass
def voter_rep(self, loc, start_header, end_header):
self.loc = loc
ocr_xml = AbbyyXML(loc)
xml_doc = XMLDoc(ocr_xml, CONSTANTS)
xml_doc.split_words("", False)
self.start_header = start_header
self.end_header = end_header
header_pages = xml_doc.se_page(start_header, end_header)
## and stuff
voter_dict = {'Voter':[], 'Record_Key':[], 'Comments':[]}
## and stuff
return voter_dict, rep_dict
if I run the method function on its own and outside of the class it works totally fine, namely if I write the function as:
def voter_rep(loc, start_header, end_header):
ocr_xml = AbbyyXML(loc)
xml_doc = XMLDoc(ocr_xml, CONSTANTS)
xml_doc.split_words("", False)
header_pages = xml_doc.se_page(start_header, end_header)
## and stuff
voter_dict = {'Voter':[], 'Record_Key':[], 'Comments':[]}
## and stuff
return voter_dict, rep_dict
in the function alone I get rid of self and will just have voter_rep(loc, start_header, end_header) but when I want to call it from the class I do plugins.voter_rep(loc, start_header, end_header) which does not work, and it returns:
NameError: name 'plugins' is not defined
I wonder why is it that my function works on its own but not callable from the class?
You can do
plugins = Plugins()
loc = #some val
start_header = #some val
end_header = #some val
plugins.voter_rep(loc, start_header, end_header)
As the error message shows, you are using small 'p' instead of capital. Also since it is not a static function, so it is not good to call it via class name.
Plugins.voter_rep(loc, start_header, end_header)
Take note of the capital letter.
The first step of my program is to get the data.
After that I am finding myself passing this data to all the different classes (with also a config dictionary variable) over and over again.
So I am wondering if there is a better way to just store the data somewhere and make it available to all classes and functions, without passing them as a parameter.
Thank you
Edit: here is a code example
go.py
config = {
'mode' : 'single',
'data' : { },
'strategy' : { },
'view' : { }
}
stratego.start(config)
stratego.py
def start(config):
data = dt.Data(config['data'])
if (config['data']['type'] == 'yahoo'):
df = data.get_yahoo_data()
elif (config['data']['type'] == 'csv'):
df = data.get_csv_data()
else:
return False
trades = str.Strategy(df, config['strategy'])
tradeBook = trades.run()
but then I am realising that the problem is my main function (start). If I run the main code not in a function I have all my instances available in the global. Is that right? Is it correct to do this way or it is better to wrap the program in a main function?
If really you don't want to pass it as an argument you could define it as a variable in a python file and import this variable where you define your fonction. You should be able to use this variable in the function without passing it in argument.
EDIT: Refactored code according to code update by OP
Ok since you use a strategy pattern you can actually do that using a strategy like design pattern
stratego.py
def start(*strategies):
for strategy in strategies:
strategy.run()
go.py
from functools import lru_cache, wraps
from abc import ABC, abstractmethod
import stratego
#lru_cache()
def get_data(filepath):
# Load data from filepath
data = ...
return data
#lru_cache()
def get_data_with_config(**data_config):
# Load data based on data_config
data = get_data(data_config['filepath'])
if (data_config['type'] == 'yahoo'):
df = data.get_yahoo_data()
elif (data_config['type'] == 'csv'):
df = data.get_csv_data()
...
return df
class Strategy(ABC):
def __init__(self, config):
self.config = config
#abstractmethod
def run(self):
pass
class YahooStrategy(Strategy):
def __init__(self, config):
config = config.copy()
config['data']['type'] = 'yahoo'
super().__init__(config)
def run(self):
df = get_data_with_config(**self.config['data'])
# Do sth with data
class CsvStrategy(Strategy):
def __init__(self, config):
config = config.copy()
config['data']['type'] = 'csv'
super().__init__(config)
def run(self):
df = get_data_with_config(**self.config['data'])
# Do sth with data
class FunctionStrategy(Strategy):
def __init__(self, config, func):
super().__init__(config)
self.func = func
def run(self):
return self.func(self.config)
def strategy_decorator(func):
#wraps(func)
def wrapper(config):
return FunctionStrategy(config, func)
return wrapper
#strategy_decorator
def some_strategy_function(config):
df = get_data_with_config(**config['data'])
# Do smth with data
# With one strategy
strategy = YahooStrategy({'data': {'filepath': 'data.csv', ...}})
stratego.run(strategy)
# Multiple strategies
strategies = [
YahooStrategy({'data': {'filepath': 'data.csv', ...}}),
CsvStrategy({'data': {'filepath': 'data2.csv', ...}}),
some_strategy_function({'data': {'filepath': 'data4.csv', ...}})
]
stratego.run(*strategies)
If you're thinking pass by reference vs pass by value then I would suspect you are newer to Python. To my understanding, all variables are passed by reference. That is, you aren't copying the actual data every time you call a function with parameters.
If you're thinking more along the lines of global variables, you can do something like this:
globvar = 0
def set_globvar_to_one():
global globvar # Needed to modify global copy of globvar
globvar = 1
def print_globvar():
print(globvar) # No need for global declaration to read value of globvar
First time poster and python newbie here, this question is probably asked before, but I am not able to find any answer.
I have a Class that reads robot status data, this works fine and I am able to dive my data into methods that are working fine when i call them. But I would like to divide my class even more, so the data is structured better. for example
I have some methods the reads and return target_data
def target_joint_positions(self):
t_j_p = self.read_data()[1:7]
return t_j_p
def target_joint_velocities(self):
t_j_v = self.read_data()[7:13]
return t_j_v
def target_joint_currents(self):
t_j_c = self.read_data()[19:25]
return t_j_c
And similar methods returning actual_data:
def actual_joint_positions(self):
a_j_p = self.read_data()[31:37]
return a_j_p
def actual_joint_velocities(self):
a_j_v = self.read_data()[37:43]
return a_j_v
def actual_joint_currents(self):
a_j_c = self.read_data()[43:49]
return a_j_c
So what I would like to accomplish is that when i make a instance of my class, instead of getting all the methods i would like something like this:
inst = Class_Name()
inst.target. (list of target methods)
inst.actual. (list of actual methods)
I have looked into nested classes and inheritance but I have not been successful in achieving my goal. Thanks for any pointers.
Welcome!
You can do the following:
class TargetMetrics:
def __init__(self, data):
self.data = data
def joint_positions(self):
return self.data[1:7]
...
class ActualMetrics:
def __init__(self, data):
self.data = data
def joint_positions(self):
return self.data[31:37]
...
class RobotMetrics:
def __init__(self):
data = read_data()
self.actual = ActualMetrics(data)
self.target = TargetMetrics(data)
...
Given a class with class methods that contain only self input:
class ABC():
def __init__(self, input_dict)
self.variable_0 = input_dict['variable_0']
self.variable_1 = input_dict['variable_1']
self.variable_2 = input_dict['variable_2']
self.variable_3 = input_dict['variable_3']
def some_operation_0(self):
return self.variable_0 + self.variable_1
def some_operation_1(self):
return self.variable_2 + self.variable_3
First question: Is this very bad practice? Should I just refactor some_operation_0(self) to explicitly take the necessary inputs, some_operation_0(self, variable_0, variable_1)? If so, the testing is very straightforward.
Second question: What is the correct way to setup my unit test on the method some_operation_0(self)?
Should I setup a fixture in which I initialize input_dict, and then instantiate the class with a mock object?
#pytest.fixture
def generator_inputs():
f = open('inputs.txt', 'r')
input_dict = eval(f.read())
f.close()
mock_obj = ABC(input_dict)
def test_some_operation_0():
assert mock_obj.some_operation_0() == some_value
(I am new to both python and general unit testing...)
Those methods do take an argument: self. There is no need to mock anything. Instead, you can simply create an instance, and verify that the methods return the expected value when invoked.
For your example:
def test_abc():
a = ABC({'variable_0':0, 'variable_1':1, 'variable_2':2, 'variable_3':3))
assert a.some_operation_0() == 1
assert a.some_operation_1() == 5
If constructing an instance is very difficult, you might want to change your code so that the class can be instantiated from standard in-memory data structures (e.g. a dictionary). In that case, you could create a separate function that reads/parses data from a file and uses the "data-structure-based" __init__ method, e.g. make_abc() or a class method.
If this approach does not generalize to your real problem, you could imagine providing programmatic access to the key names or other metadata that ABC recognizes or cares about. Then, you could programmatically construct a "defaulted" instance, e.g. an instance where every value in the input dict is a default-constructed value (such as 0 for int):
class ABC():
PROPERTY_NAMES = ['variable_0', 'variable_1', 'variable_2', 'variable_3']
def __init__(self, input_dict):
# implementation omitted for brevity
pass
def some_operation_0(self):
return self.variable_0 + self.variable_1
def some_operation_1(self):
return self.variable_2 + self.variable_3
def test_abc():
a = ABC({name: 0 for name in ABC.PROPERTY_NAMES})
assert a.some_operation_0() == 0
assert a.some_operation_1() == 0