How to print a linebreak in a python function?
You have your slash backwards, it should be “\n”
Python
CSV new-line character seen in unquoted field error
It’ll be good to see the csv file itself, but this might work for you, give it a try, replace: with: Or, open a file with universal newline mode and pass it to csv.reader, like: Or, use splitlines(), like this:
Convert a 1D array to a 2D array in numpy
You want to reshape the array. where -1 infers the size of the new dimension from the size of the input array.
The view didn’t return an HttpResponse object. It returned None instead
Because the view must return render, not just call it. Change the last line to
Merging two DataFrames
The error message indicates that df2 is of type pd.Series. You need to convert df2 .to_frame() as .merge() needs a pd.DataFrame() input (see docs): while you probably also just could: Alternatively, you can use pd.DataFrame.join() which accepts a pd.Series.
Purpose of #!/usr/bin/python3 shebang
#!/usr/bin/python3 is a shebang line. A shebang line defines where the interpreter is located. In this case, the python3 interpreter is located in /usr/bin/python3. A shebang line could also be a bash, ruby, perl or any other scripting languages’ interpreter, for example: #!/bin/bash. Without the shebang line, the operating system does not know it’s a python script, even if you set the execution flag (chmod … Read more
print(__doc__) in Python 3 script
it seems __doc__ is useful to provide some documentation in, say, functions This is true. In addition to functions, documentation can also be provided in modules. So, if you have a file named mymodule.py like this: You can access its docstrings like this: Now, back to your question: what does print(__doc__) do? Simply put: it prints the module docstring. If no … Read more
How to implement the ReLU function in Numpy
There are a couple of ways. If timing the results with the following code: We get: So the multiplication seems to be the fastest.
Difference between data type ‘datetime64[ns]’ and ‘
datetime64[ns] is a general dtype, while <M8[ns] is a specific dtype. General dtypes map to specific dtypes, but may be different from one installation of NumPy to the next. On a machine whose byte order is little endian, there is no difference between np.dtype(‘datetime64[ns]’) and np.dtype(‘<M8[ns]’): However, on a big endian machine, np.dtype(‘datetime64[ns]’) would equal np.dtype(‘>M8[ns]’). So datetime64[ns] maps to either <M8[ns] or >M8[ns] depending on the endian-ness of the … Read more