The documentation for this function doesn’t tell much in fact. So let me try to clarify it a little. First, the sizes of the images are irrelevant to whether the function works or not, it internally checks for a size that both images fit. Now, when can you actually compare the images by using the … Read more
Assuming your input from above is given as a string in raw: import networkx as nx import re regex = re.compile(r’^([A-Z]+)::Requires\s+=\s([A-Z”]+)$’) G = nx.DiGraph() roots = set() for l in raw.splitlines(): if len(l): target, prereq = regex.match(l).groups() if prereq == ‘””‘: roots.add(target) else: G.add_edge(prereq, target) Now print the tree(s): for s in roots: print s … Read more
There is no longer a NoneType reference in the types modules. You should just check for identity with None directly, i.e. obj is None. An alternative way, if you really need the NoneType, would be to get it using: NoneType = type(None) This is actually the exact same way types.NoneType was previously defined, before it … Read more
The return values of plt.hist are: Returns: tuple : (n, bins, patches) or ([n0, n1, …], bins, [patches0, patches1,…]) So all you need to do is capture the return values appropriately. For example: import numpy as np import matplotlib.pyplot as plt # generate some uniformly distributed data x = np.random.rand(1000) # create the histogram (n, … Read more
I made my own google translate function for python 😉 try it https://github.com/mouuff/Google-Translate-API
I think you were very close. Use groupby and to_dict: df = df.groupby(‘Name’)[[‘Chain’,’Food’,’Healthy’]] .apply(lambda x: x.set_index(‘Chain’).to_dict(orient=”index”)) .to_dict() print (df) {‘George’: {‘KFC’: {‘Healthy’: False, ‘Food’: ‘chicken’}, ‘McDonalds’: {‘Healthy’: False, ‘Food’: ‘burger’}}, ‘John’: {‘McDonalds’: {‘Healthy’: True, ‘Food’: ‘salad’}, ‘Wendys’: {‘Healthy’: False, ‘Food’: ‘burger’}}}
Since you aren’t performing an aggregation, pd.DataFrame.pivot should be preferred to groupby / pivot_table: res = df.pivot(index=’date’, columns=”name”, values=”quantity”) print(res) name A B C date 1/1/2018 5 6 7 1/2/2018 9 8 6 If you wish you can use reset_index to elevate date to a column.
You need to perform initialization after installation: $ export AIRFLOW_HOME=some/dir $ airflow db init # or `airflow initdb` for the legacy 1.X If AIRFLOW_HOME is unset, ~/airflow/ will be created and used. This is where the config and logs will be stored; if you want to reset the configuration, remove the dir stored in AIRFLOW_HOME … Read more
Numpy is optimised for large amounts of data. Give it a tiny 3 length array and, unsurprisingly, it performs poorly. Consider a separate test import timeit reps = 100 pythonTest = timeit.Timer(‘a = [0.] * 1000000’) numpyTest = timeit.Timer(‘a = numpy.zeros(1000000)’, setup=’import numpy’) uninitialised = timeit.Timer(‘a = numpy.empty(1000000)’, setup=’import numpy’) # empty simply allocates the … Read more
Please refer to http://rkulla.blogspot.kr/2014/03/the-path-to-homebrew.html After upgrading openssl to 1.0.1j by homebrew on MAC, but system python still referred to old version 0.9.8. It turned out the python referred to openssl. So I have installed new python with brewed openssl and finished this issue on Mac, not yet Ubuntu. On Mac OS X version 10.10 and … Read more