TypeError: 'method' object is not subscriptable

[rghelichi]

running the first example provided in github page:
Python 3.7.2 (default, Dec 29 2018, 00:00:04)
Type 'copyright', 'credits' or 'license' for more information
IPython 6.5.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import dowhy.api
...: import dowhy.datasets
...:
...: data = dowhy.datasets.linear_dataset(beta=5,
...: num_common_causes=1,
...: num_instruments = 0,
...: num_samples=1000,
...: treatment_is_binary=True)
...:
...: # data['df'] is just a regular pandas.DataFrame
...: data['df'].causal.do(x='v',
...: variable_types={'v': 'b', 'y': 'c', 'X0': 'c'},
...: outcome='y',
...: common_causes=['X0']).groupby('v').mean().plot(y='y', kind='bar')
...:
...:
WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.

TypeError Traceback (most recent call last)
in ()
12 variable_types={'v': 'b', 'y': 'c', 'X0': 'c'},
13 outcome='y',
---> 14 common_causes=['X0']).groupby('v').mean().plot(y='y', kind='bar')
15
16

~/Documents/projects/dowhy/dowhy/api/causal_data_frame.py in do(self, x, method, num_cores, variable_types, outcome, params, dot_graph, common_causes, estimand_type, proceed_when_unidentifiable, stateful)
88 instruments=None,
89 estimand_type=estimand_type,
---> 90 proceed_when_unidentifiable=proceed_when_unidentifiable)
91 #self._identified_estimand = self._causal_model.identify_effect()
92 if not self._sampler:

~/Documents/projects/dowhy/dowhy/do_why.py in init(self, data, treatment, outcome, graph, common_causes, instruments, estimand_type, proceed_when_unidentifiable, **kwargs)
79 self._outcome,
80 common_cause_names=self._common_causes,
---> 81 observed_node_names=self._data.columns.tolist()
82 )
83 elif instruments is not None:

~/Documents/projects/dowhy/dowhy/causal_graph.py in init(self, treatment_name, outcome_name, graph, common_cause_names, instrument_names, observed_node_names)
65 raise ValueError
66
---> 67 self._graph = self.add_node_attributes(observed_node_names)
68 self._graph = self.add_unobserved_common_cause(observed_node_names)
69

~/Documents/projects/dowhy/dowhy/causal_graph.py in add_node_attributes(self, observed_node_names)
116 for node_name in self._graph:
117 if node_name in observed_node_names:
--> 118 self._graph.nodes[node_name]["observed"] = "yes"
119 else:
120 self._graph.nodes[node_name]["observed"] = "no"

TypeError: 'method' object is not subscriptable

[GithubUsr140906]

I have the exact same issue

[rghelichi]

so it seems it works fine with Python 3.6 and it gives me an error with Python 3.7. the other issue is with networkx it has to be 2.x version to work. which I suggest adding the version of dependencies in the "requirement" file the version of dependencies.
I have added the versions of the packages in the virtual env that dowhy works.
requirements.txt

[amit-sharma]

Thanks for your patience @rghelichi . You are right---this is fixed when network is 2.x. I have updated the requirements.txt now.

[Shanzaay]

I have tried many solutions but can't seem to solve the problem. This code is in Python 2.7 and I am using Python 3.7. While searching I find out that this gives an error in Python 3.7. Can anyone help me how to remove this error in Python 3.7?

    print('Loading c3d features ...')
            features = h5py.File(self._options['feature_data_path'], 'r') 
            self._feature_ids = features.keys()
            self._features = {video_id:np.asarray(features[video_id].values() [0]) for video_id in 
            self._feature_ids}

This is the error I am getting:

           E:\jupyter book\data_provider.py in <dictcomp>(.0)
         ---> 68         self._features 
           {video_id:np.asarray(features[video_id].values()[0]) for 
           video_id in self._feature_ids}
         69 
          70 
         TypeError: 'ValuesViewHDF5' object is not subscriptable