numpy has an `insert` function that&#39;s accesible via `np.insert` with [documentation][1].

You&#39;ll want to use it in this case like so:

    X = np.insert(X, 0, 6., axis=0)

the first argument `X` specifies the object to be inserted into.

The second argument `0` specifies where.

The third argument `6.` specifies what is to be inserted.

The fourth argument `axis=0` specifies that the insertion should happen at position `0` for every column.  We could&#39;ve chosen rows but your X is a columns vector, so I figured we&#39;d stay consistent.

  [1]: https://numpy.org/doc/stable/reference/generated/numpy.insert.html

I just wrote some code that does this operation ~100,000 times, so I needed to figure out the fastest way to do this. I&#39;m not an expert in code efficiency by any means, but I could figure some things out by using the `%%timeit` magic function in a jupyter notebook.

My findings:

`np.concatenate(([number],array))`
requires the least time. Let&#39;s call it 1x time.

`np.asarray([number] + list(array))`
comes in at ~2x.

`np.r_[number,array]`
is ~4x.

`np.insert(array,0,number)`
appears to be the worst option here at 8x.

I have no idea how this changes with the size of `array` (I used a shape (15,) array) and most of the options I suggested only work if you want to put the number at the beginning. However, since that&#39;s what the question is asking about, I figure this is a good place to make these comparisons.

You can try the following

    X = np.append(arr = np.array([[6]]), values = X, axis= 0)

Instead of inserting 6 to the existing X, let append 6 by X.

So, first argument `arr` is numpy array of scalar 6, second argument is your array to be added, and third is the place where we want to add

I know this is a fairly old one, but a short solution is using numpy slicing tricks:

    np.r_[[[6.]], X]

If you need to do it in a second dimension you can use np.c_.

I think this is the least cluttered version I can think of



i wonder if there is a proper way to inject interfaces in Angular2? (cf. below)

I think this is related with the missing @Injectable() decorator on the interface, but it seems this is not allowed.

Regards.

When CoursesServiceInterface is implemented as an interface, the TypeScript compiler complains &quot;CoursesServiceInterface cannot find name&quot;:

    import {CoursesServiceInterface} from &#39;./CoursesService.interface&#39;;
    import {CoursesService} from &#39;./CoursesService.service&#39;;
    import {CoursesServiceMock} from &#39;./CoursesServiceMock.service&#39;;
    bootstrap(AppComponent, [
      ROUTER_PROVIDERS, 
      GlobalService,
      provide(CoursesServiceInterface, { useClass: CoursesServiceMock })
      ]);


but with CoursesServiceInterface as an interface:

    import {Injectable} from &#39;angular2/core&#39;;
    import {Course} from &#39;./Course.class&#39;;
    //@Injectable()
    export interface CoursesServiceInterface {
    	getAllCourses(): Promise&lt;Course[]&gt;;//{ return null; };
    	getCourse(id: number): Promise&lt;Course&gt;;// { return null; };
    	remove(id: number): Promise&lt;{}&gt;;// { return null; };
    }

When service is a class, the TypeScript compiler doesn&#39;t complain anymore:

    import {Injectable} from &#39;angular2/core&#39;;
    import {Course} from &#39;./Course.class&#39;;
    @Injectable()
    export class CoursesServiceInterface {	
    	getAllCourses() : Promise&lt;Course[]&gt; { return null; };
    	getCourse(id: number) :Promise&lt;Course&gt; { return null; };
    	remove (id: number) : Promise&lt;{}&gt; { return null; };
    }


Is it possible to inject interface with angular2?

In python there&#39;s a built-in [`heapq`](https://docs.python.org/3/library/heapq.html) algorithm that gives you `push`, `pop`, `nlargest`, `nsmallest`... etc that you can apply to lists. However, there&#39;s also the [`queue.PriorityQueue`](https://docs.python.org/3/library/queue.html#queue.PriorityQueue) class that seems to support more or less the same functionality. What&#39;s the difference, and when would you use one over the other?

What&#39;s the difference between heapq and PriorityQueue in python?

I have the following numpy array

    import numpy as np
    
    X = np.array([[5.], [4.], [3.], [2.], [1.]])
    
I want to insert `[6.]` at the beginning.
I&#39;ve tried:

    X = X.insert(X, 0)

how do I insert into X?

Prepend element to numpy array

I have the following numpy array
<pre><code class="hljs language-css">import numpy as np

X = np.array([[5.], [4.], [3.], [2.], [1.]])
</code></pre>
I want to insert <code>[6.]</code> at the beginning.
I've tried:
<pre><code class="hljs language-ini">X = X.insert(X, 0)
</code></pre>
how do I insert into X?

How can I upload a file to [Google Cloud Storage](https://cloud.google.com/storage/) from Python 3? Eventually Python 2, if it&#39;s infeasible from Python 3.

I&#39;ve looked and looked, but haven&#39;t found a solution that actually works. I tried [boto](https://github.com/boto/boto), but when I try to generate the necessary .boto file through `gsutil config -e`, it keeps saying that I need to configure authentication through `gcloud auth login`. However, I have done the latter a number of times, without it helping.


How to upload a file to Google Cloud Storage on Python 3?

I know how to install packages in Anaconda using `conda install` and also how to install packages that are on [PyPi][1] which is described in the [manual][2].

But how can I permanently include packages/folders into the `PYTHONPATH` of an Anaconda environment so that code that I am currently working on can be imported and is still available after a reboot?

My current approach is to use `sys`:

    import sys
    sys.path.append(r&#39;/path/to/my/package&#39;)

which is not really convenient.

Any hints?

Thanks in advance!

  [1]: https://pypi.python.org/pypi
  [2]: http://conda.pydata.org/docs/build_tutorials/pkgs.html

Anaconda: Permanently include external packages (like in PYTHONPATH)

&lt;p&gt;I am new in python programming and i am a bit confused. I try to get the bytes from a string to hash and encrypt but i got&lt;/p&gt; 

    b&#39;...&#39;

&lt;p&gt;b character in front of string just like the below example. Is any way avoid this?.Can anyone give a solution? Sorry for this silly question&lt;/p&gt;

    import hashlib
    
    text = &quot;my secret data&quot;
    pw_bytes = text.encode(&#39;utf-8&#39;)
    print(&#39;print&#39;,pw_bytes)
    m = hashlib.md5()
    m.update(pw_bytes)


OUTPUT:
   

     print b&#39;my secret data&#39;


Remove &#39;b&#39; character do in front of a string literal in Python 3

I did not find any best practice about what should be documented in the classes and `__init__` docstrings. Sometimes I find that the constructor arguments are already documented in the classes docstring, sometimes the are described in the `__init__` docstring. I prefer describing the construction within the classes docstring, as this is what you call when creating a new instance. But what should be documented in the `__init__` methods docstring then?

***********
_edit:_

I know about the [google styleguide][1] and the [google docstring style example][2], but both do not answer my question. The docstring style example does say 

&gt; The `__init__` method may be documented in either the class level
        docstring, or as a docstring on the `__init__` method itself.
Either form is acceptable, but the two should not be mixed. Choose one
        convention to document the `__init__` method and be consistent with it.

But if I choose to put the docstring of the `__init__` function into the class level docstring, what should the `__init__` docstring contain?

  [1]: https://google.github.io/styleguide/pyguide.html?showone=Comments#Comments
  [2]: https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html

Is there a consensus what should be documented in the classes and __init__ docstrings?

I&#39;m trying out Python&#39;s type annotations with abstract base classes to write some interfaces. Is there a way to annotate the possible types of `*args` and `**kwargs`?

For example, how would one express that the sensible arguments to a function are either an `int` or two `int`s? `type(args)` gives `Tuple` so my guess was to annotate the type as `Union[Tuple[int, int], Tuple[int]]`, but this doesn&#39;t work.

    from typing import Union, Tuple
    
    def foo(*args: Union[Tuple[int, int], Tuple[int]]):
        try:
            i, j = args
            return i + j
        except ValueError:
            assert len(args) == 1
            i = args[0]
            return i

    # ok
    print(foo((1,)))
    print(foo((1, 2)))
    # mypy does not like this
    print(foo(1))
    print(foo(1, 2))

Error messages from mypy:

    t.py: note: In function &quot;foo&quot;:
    t.py:6: error: Unsupported operand types for + (&quot;tuple&quot; and &quot;Union[Tuple[int, int], Tuple[int]]&quot;)
    t.py: note: At top level:
    t.py:12: error: Argument 1 to &quot;foo&quot; has incompatible type &quot;int&quot;; expected &quot;Union[Tuple[int, int], Tuple[int]]&quot;
    t.py:14: error: Argument 1 to &quot;foo&quot; has incompatible type &quot;int&quot;; expected &quot;Union[Tuple[int, int], Tuple[int]]&quot;
    t.py:15: error: Argument 1 to &quot;foo&quot; has incompatible type &quot;int&quot;; expected &quot;Union[Tuple[int, int], Tuple[int]]&quot;
    t.py:15: error: Argument 2 to &quot;foo&quot; has incompatible type &quot;int&quot;; expected &quot;Union[Tuple[int, int], Tuple[int]]&quot;

It makes sense that mypy doesn&#39;t like this for the function call because it expects there to be a `tuple` in the call itself. The addition after unpacking also gives a typing error that I don&#39;t understand.

How does one annotate the sensible types for `*args` and `**kwargs`?

Type annotations for *args and **kwargs

I have some images I want to work with, the problem is that there are two kinds of images both are 106 x 106 pixels, some are in color and some are black and white. 

one with only two (2) dimensions:

(106,106)

and one with three (3)

(106,106,3)

Is there a way I can strip this last dimension?

I tried np.delete, but it did not seem to work.

    np.shape(np.delete(Xtrain[0], [2] , 2))
    Out[67]: (106, 106, 2)



Numpy remove a dimension from np array

I am trying to run the following simple code 

    import scipy
    scipy.test()

But I am getting the following error

    Traceback (most recent call last):
      File &quot;&lt;stdin&gt;&quot;, line 1, in &lt;module&gt;
      File &quot;C:\Python27\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py&quot;, line 586, in runfile
        execfile(filename, namespace)
      File &quot;C:/Users/Mustafa/Documents/My Python Code/SpectralGraphAnalysis/main.py&quot;, line 8, in &lt;module&gt;
        import scipy
      File &quot;C:\Python27\lib\site-packages\scipy\__init__.py&quot;, line 61, in &lt;module&gt;
        from numpy._distributor_init import NUMPY_MKL  # requires numpy+mkl
    ImportError: cannot import name NUMPY_MKL

I am using python 2.7 under windows 10.

I have installed `scipy` but that does not seem to solve the problem

Any help is appreciated.

ImportError: cannot import name NUMPY_MKL

I have a dataframe in pandas and I&#39;m trying to figure out what the types of its values are. I am unsure what the type is of column `&#39;Test&#39;`. However, when I run `myFrame[&#39;Test&#39;].dtype`, I get;

    dtype(&#39;O&#39;)

What does this mean?

What is dtype(&#39;O&#39;), in pandas?

I need to test whether a variable is of type `int`, or any of `np.int*`, `np.uint*`, preferably using a single condition (*i.e.* no `or`).

After some tests, I guess that:

- `isinstance(n, int)` will only match `int` and `np.int32` (or `np.int64` depending on plateform),
- `np.issubdtype(type(n), int)` seems to match all `int` and `np.int*`, but doesn’t match `np.uint*`. 

This leads to two questions: will `np.issubdtype` match **any** kind of signed ints? Can determine in a single check whether a number is any kind of signed or unsigned int?

This is about testing for **integers**, the test should return `False` for float-likes. 

How to determine if a number is any type of int (core or numpy, signed or not)?

I am importing data from a MySQL database into a Pandas data frame. The following excerpt is the code that I am using:

    import mysql.connector as sql
    import pandas as pd
    
    db_connection = sql.connect(host=&#39;hostname&#39;, database=&#39;db_name&#39;, user=&#39;username&#39;, password=&#39;password&#39;)
    db_cursor = db_connection.cursor()
    db_cursor.execute(&#39;SELECT * FROM table_name&#39;)

    table_rows = db_cursor.fetchall()

    df = pd.DataFrame(table_rows)

When I print the data frame it does properly represent the data but my question is, is it possible to also keep the column names? Here is an example output:

                              0   1   2     3     4     5     6     7     8
    0  :ID[giA0CqQcx+(9kbuSKV== NaN NaN  None  None  None  None  None  None
    1  lXB+jIS)DN!CXmj&gt;0(P8^]== NaN NaN  None  None  None  None  None  None   
    2  lXB+jIS)DN!CXmj&gt;0(P8^]== NaN NaN  None  None  None  None  None  None   
    3  lXB+jIS)DN!CXmj&gt;0(P8^]== NaN NaN  None  None  None  None  None  None   
    4  lXB+jIS)DN!CXmj&gt;0(P8^]== NaN NaN  None  None  None  None  None  None   

What I would like to do is keep the column name, which would replace the pandas column indexes. For example, instead of having 0, the column name would be: &quot;First_column&quot; as in the MySQL table. Is there a good way to go about this? or is there a more efficient approach of importing data from MySQL into a Pandas data frame than mine?

Importing data from a MySQL database into a Pandas data frame including column names

    INSERT into Group (Name,CreatedDate) VALUES (&#39;Test&#39;,UTC_TIMESTAMP(), 1);

This is the query I have used for mysql to insert current date time.
When I am using this in postgresql, I am getting below error.

        HINT:  No function matches the given name and argument types. You might need to add explicit type casts.
    ********** Error **********
    
    ERROR: function utc_timestamp() does not exist
    SQL state: 42883

I have tried like below using `now()`, however it is inserting like `&quot;2016-07-07 17:01:18.410677&quot;`. I need to insert in `&#39;yyyymmdd hh:mi:ss tt&#39;` format.

    INSERT into Group (Name,CreatedDate) VALUES (&#39;Test&#39;,UTC_TIMESTAMP(), 1);

How to insert current date time in insert query of postgresql in above format ?

How to insert current datetime in postgresql insert query

I am just creating a simple ToDo App in Flutter. I am managing all the todo tasks on the list. I want to add any new todo tasks at the beginning of the list. I am able to use this workaround kind of thing to achieve that. Is there any better way to do this?

    void _addTodoInList(BuildContext context){
        String val = _textFieldController.text;
    
        final newTodo = {
          &quot;title&quot;: val,
          &quot;id&quot;: Uuid().v4(),
          &quot;done&quot;: false
        };
    
        final copiedTodos = List.from(_todos);
    
        _todos.removeRange(0, _todos.length);
        setState(() {
          _todos.addAll([newTodo, ...copiedTodos]);
        });
    
        Navigator.pop(context);
      }

Content Type	Original Author	Original Content on Stackoverflow
Question	piRSquared	View Question on Stackoverflow
Solution 1 - Python	Rosey	View Answer on Stackoverflow
Solution 2 - Python	jbf81tb	View Answer on Stackoverflow
Solution 3 - Python	Okroshiashvili	View Answer on Stackoverflow
Solution 4 - Python	T0eJam	View Answer on Stackoverflow

Prepend element to numpy array

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What's the difference between heapq and PriorityQueue in python?

Is it possible to inject interface with angular2?

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