It&#39;s common to end configuration files in &#39;rc&#39; - e.g. &#39;.xinitrc&#39;, &#39;.vimrc&#39; and &#39;.bashrc&#39;.

It stems from practice of having your configs executable - they are automatically **R**un at startup and they **C**onfigure your stuff.

This started [long ago](http://www.catb.org/jargon/html/R/rc-file.html), even before Unix:

&gt;[Unix: from runcom files on the CTSS system 1962-63, via the startup script /etc/rc] Script file containing startup instructions for an application program (or an entire operating system), usually a text file containing commands of the sort that might have been invoked manually once the system was running but are to be executed automatically each time the system starts up.

I made a Horizontal RecyclerView and it works fine(thanks to [this][1]) but the direction of scroll and data are expand from left to right; then How can I change the RecyclerView scroll direction like in the picture below?

[![enter image description here][2]][2]


My Code:

    StaggeredGridLayoutManager staggeredGridLayoutManager =
                    new StaggeredGridLayoutManager(
                            2, //The number of Columns in the grid
                            LinearLayoutManager.HORIZONTAL);


  [1]: http://www.truiton.com/2015/02/android-recyclerview-tutorial/
  [2]: https://i.stack.imgur.com/i022L.jpg

Android Horizontal RecyclerView scroll Direction

I am using NodeJs with JsonWebtoken Module. 

I am facing this error when calling sign method of json web token

&gt; ValidationError: &quot;expiresInMinutes&quot; is not allowed



    var jwt = require(&#39;jsonwebtoken&#39;);

    exports.authenticate = function(req, res, next) {
    	var user = {&quot;Name&quot;:&quot;Abdul&quot;} //static data for test purpose.
    	
    	var token = jwt.sign(user, req.app.get(&#39;jwtTokenSecret&#39;), {
              expiresInMinutes: 1440 // expires in 24 hours
            });
    
            // return the information including token as JSON
            res.json({
              success: true,
              message: &#39;Enjoy your token!&#39;,
              token: token
            });
    		
    }

ValidationError: &quot;expiresInMinutes&quot; is not allowed NodeJs JsonWebToken

`matplotlibrc` configuration files are used to customize all kinds of properties in `matplotlib`. One can change the `rc` settings to customize the default parameters e.g:

    matplotlib.rcParams[&#39;font.family&#39;] = &#39;times new roman&#39;

**... but what does &quot;`rc`&quot; stand for?**

I can&#39;t find any explanation in [the docs](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.rc.html)

What does &quot;rc&quot; in matplotlib&#39;s rcParams stand for?

<code>matplotlibrc</code> configuration files are used to customize all kinds of properties in <code>matplotlib</code>. One can change the <code>rc</code> settings to customize the default parameters e.g:
<pre><code class="hljs language-arduino">matplotlib.rcParams['font.family'] = 'times new roman'
</code></pre>
... but what does "<code>rc</code>" stand for?
I can't find any explanation in <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.rc.html" target="_blank" rel="noopener noreferrer">the docs</a>

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

Say now I have a numpy array which is defined as,

    [[1,2,3,4],
    [2,3,NaN,5],
    [NaN,5,2,3]]

Now I want to have a list that contains all the indices of the missing values, which is `[(1,2),(2,0)]` at this case.

Is there any way I can do that?

How to get the indices list of all NaN value in numpy array?

Say I have a color in Matplotlib.  Maybe it&#39;s a string (`&#39;k&#39;`) or an rgb tuple (`(0.5, 0.1, 0.8)`) or even some hex (`#05FA2B`).  Is there a command / convenience function in Matplotlib that would allow me to darken (or lighten) that color.  

I.e. is there `matplotlib.pyplot.darken(c, 0.1)` or something like that?  I guess what I&#39;m hoping for is something that, behind the scenes, would take a color, convert it to HSL, then either multiply the L value by some given factor (flooring at 0 and capping at 1) or explicitly set the L value to a given value and return the modified color.

Darken or lighten a color in matplotlib

I&#39;ve been trying to install both OpenCV and cv2 from both Pycharm and from the terminal as suggested using:

    pip install --user opencv
    pip install --user cv2

but I&#39;m getting the following error for them:

    Collecting opencv
      Could not find a version that satisfies the requirement opencv (from versions: )
    No matching distribution found for opencv

and

    Collecting cv2
      Could not find a version that satisfies the requirement cv2 (from versions: )
    No matching distribution found for cv2

How can I fix these and install the packages properly? I&#39;m using python 3.4.



Pycharm/Python OpenCV and CV2 install error

I have a data frame like this: 

    print(df)
    
            0          1     2
    0   354.7      April   4.0
    1    55.4     August   8.0
    2   176.5   December  12.0
    3    95.5   February   2.0
    4    85.6    January   1.0
    5     152       July   7.0
    6   238.7       June   6.0
    7   104.8      March   3.0
    8   283.5        May   5.0
    9   278.8   November  11.0
    10  249.6    October  10.0
    11  212.7  September   9.0

As you can see, months are not in calendar order. So I created a second column to get the month number corresponding to each month (1-12). From there, how can I sort this data frame according to  calendar months&#39; order?

how to sort pandas dataframe from one column

Seaborn offers a function called color_palette, which allows you to easily create new color_palettes for plots.

    colors = [&quot;#67E568&quot;,&quot;#257F27&quot;,&quot;#08420D&quot;,&quot;#FFF000&quot;,&quot;#FFB62B&quot;,&quot;#E56124&quot;,&quot;#E53E30&quot;,&quot;#7F2353&quot;,&quot;#F911FF&quot;,&quot;#9F8CA6&quot;]
        
    color_palette = sns.color_palette(colors)

I want to transform color_palette to a cmap, which I can use in matplotlib, but I don&#39;t see how I can do this. 

Sadly just functions like &quot;cubehelix_palette&quot;,&quot;light_palette&quot;,… have an &quot;as_cmap&quot; paramater. &quot;color_palette&quot; doesn&#39;t, unfortunately.

seaborn color_palette as matplotlib colormap

&gt; The information below may be superfluous if you are trying to understand the error message. Please start off by reading [the answer](https://stackoverflow.com/a/37968229/6292000)
 by **@user707650**.

Using MatPlotLib, I wanted a generalizable script that creates the following from my data. 

A window containing ***a*** subplots arranged so that there are ***b*** subplots per column. I want to be able to change the values of ***a*** and ***b***. 

If I have data for ***2a*** subplots, I want 2 windows, each with the previously described &quot;***a*** subplots arranged according to ***b*** subplots per column&quot;.

The x and y data I am plotting are floats stored in np.arrays and are structured as follows:

 - The x data is always the same for all plots and is of length 5.

         &#39;x_vector&#39;: [0.000, 0.005, 0.010, 0.020, 0.030, 0.040]

 - The y data of all plots are stored in **y_vector** where the data for the first plot is stored at indexes 0 through 5. The data for the second plot is stored at indexes 6 through 11. The third plot gets 12-18, the fourth 19-24, and so on.

In total, for this dataset, I have 91 plots (i.e. 91*6 = 546 values stored in y_vector). 

**Attempt:**

    import matplotlib.pyplot as plt
    
    # Options:
    plots_tot = 14 # Total number of plots. In reality there is going to be 7*13 = 91 plots.
    location_of_ydata = 6 # The values for the n:th plot can be found in the y_vector at index &#39;n*6&#39; through &#39;n*6 + 6&#39;.
    plots_window = 7 # Total number of plots per window.
    rows = 2 # Number of rows, i.e. number of subplots per column.
    
    # Calculating number of columns:
    prim_cols = plots_window / rows
    extra_cols = 0
    if plots_window % rows &gt; 0:
    	extra_cols = 1
    cols = prim_cols + extra_cols
    
    print &#39;cols:&#39;, cols
    print &#39;rows:&#39;, rows
    
    # Plotting:
    n=0
    x=0
    fig, ax = plt.subplots(rows, cols)
    while x &lt;= plots_tot:
    	ax[x].plot(x_vector, y_vector[n:(n+location_of_ydata)], &#39;ro&#39;)
    	if x % plots_window == plots_window - 1:
    		plt.show() # New window for every 7 plots.
    	n = n+location_of_ydata
    	x = x+1

**I get the following error:**

    cols: 4
    rows: 2
    Traceback (most recent call last):
      File &quot;Script.py&quot;, line 222, in &lt;module&gt;
        ax[x].plot(x_vector, y_vector[n:(n+location_of_ydata)], &#39;ro&#39;)
    AttributeError: &#39;numpy.ndarray&#39; object has no attribute &#39;plot&#39;

Axes from plt.subplots() is a &quot;numpy.ndarray&quot; object and has no attribute &quot;plot&quot;

I&#39;m kind of confused what is going at the backend when I draw plots in matplotlib, tbh, I&#39;m not clear with the hierarchy of plot, axes and figure. I read the documentation and it was helpful but I&#39;m still confused...

The below code draws the same plot in three different ways - 

    #creating the arrays for testing
    x = np.arange(1, 100)
    y = np.sqrt(x)
    #1st way
    plt.plot(x, y)
    #2nd way
    ax = plt.subplot()
    ax.plot(x, y)
    #3rd way
    figure = plt.figure()
    new_plot = figure.add_subplot(111)
    new_plot.plot(x, y)

Now my question is -

1. What is the difference between all the three, I mean what is going under the hood when any of the 3 methods are called? 

2. Which method should be used when and what are the pros and cons of using any on those?  

What is the difference between drawing plots using plot, axes or figure in matplotlib?

I am attempting to recreate the following plot from the book Introduction to Statistical learning using seaborn [![enter image description here][1]][1]

I specifically want to recreate this using seaborn&#39;s `lmplot` to create the first two plots and `boxplot` to create the second. The main problem is that lmplot creates a `facetgrid` according [to this answer][2] which forces me to hackily add another matplotlib axes for the boxplot. I was wondering if there was an easier way to achieve this. Below, I have to do quite a bit of manual manipulation to get the desired plot.

    seaborn_grid = sns.lmplot(&#39;value&#39;, &#39;wage&#39;, col=&#39;variable&#39;, hue=&#39;education&#39;, data=df_melt, sharex=False)
    seaborn_grid.fig.set_figwidth(8)
    
    left, bottom, width, height = seaborn_grid.fig.axes[0]._position.bounds
    left2, bottom2, width2, height2 = seaborn_grid.fig.axes[1]._position.bounds
    left_diff = left2 - left
    seaborn_grid.fig.add_axes((left2 + left_diff, bottom, width, height))
    
    sns.boxplot(&#39;education&#39;, &#39;wage&#39;, data=df_wage, ax = seaborn_grid.fig.axes[2])
    ax2 = seaborn_grid.fig.axes[2]
    ax2.set_yticklabels([])
    ax2.set_xticklabels(ax2.get_xmajorticklabels(), rotation=30)
    ax2.set_ylabel(&#39;&#39;)
    ax2.set_xlabel(&#39;&#39;);
    
    leg = seaborn_grid.fig.legends[0]
    leg.set_bbox_to_anchor([0, .1, 1.5,1])

Which yields[![enter image description here][3]][3]


Sample data for DataFrames: 

    df_melt = {&#39;education&#39;: {0: &#39;1. &lt; HS Grad&#39;,
      1: &#39;4. College Grad&#39;,
      2: &#39;3. Some College&#39;,
      3: &#39;4. College Grad&#39;,
      4: &#39;2. HS Grad&#39;},
     &#39;value&#39;: {0: 18, 1: 24, 2: 45, 3: 43, 4: 50},
     &#39;variable&#39;: {0: &#39;age&#39;, 1: &#39;age&#39;, 2: &#39;age&#39;, 3: &#39;age&#39;, 4: &#39;age&#39;},
     &#39;wage&#39;: {0: 75.043154017351497,
      1: 70.476019646944508,
      2: 130.982177377461,
      3: 154.68529299562999,
      4: 75.043154017351497}}

    df_wage={&#39;education&#39;: {0: &#39;1. &lt; HS Grad&#39;,
      1: &#39;4. College Grad&#39;,
      2: &#39;3. Some College&#39;,
      3: &#39;4. College Grad&#39;,
      4: &#39;2. HS Grad&#39;},
     &#39;wage&#39;: {0: 75.043154017351497,
      1: 70.476019646944508,
      2: 130.982177377461,
      3: 154.68529299562999,
      4: 75.043154017351497}}


  [1]: http://i.stack.imgur.com/FegIV.png
  [2]: https://stackoverflow.com/questions/23969619/plotting-with-seaborn-using-the-matplotlib-object-oriented-interface
  [3]: http://i.stack.imgur.com/tY2iI.png

Plotting multiple different plots in one figure using Seaborn

ASP.NET Core support a new configuration system as seen here:
https://docs.asp.net/en/latest/fundamentals/configuration.html

Is this model also supported in .NET Core console applications?

If not what is alternate to the previous `app.config` and `ConfigurationManager` model?

ASP.NET Core configuration for .NET Core console application

I want to add `custom_config.php` to *app/config* directory of my Laravel 5 project, but can&#39;t find any article explaining this process. How is this done?

How to add custom config file to app/config in Laravel 5?

I have ASP.NET Core application (Web Api). The documentation has explained [working with multiple environments][1], however it failed to explain how to set `aspnetcore_environment` when publishing the web site.     

So lets say if i have 3 environments `Development`, `Staging` and `Production`   

  1. In classic ASP.NET Web Application i used to create 3 build configurations. `Development`, `Staging` and `Production` ( Like shown in picture below).  and then 3 `.pubxml` files, one for each configuration. Do i need to use the same approach for `ASP.NET Core` application as well?

  2. How do i set `aspnetcore_environment` in `.pubxml` file?  

  3. If the approach specified in Question 1 is obsolete, then what&#39;s the alternate approach? ( I use Jenkins for CI)

[![enter image description here][2]][2]
    
**Update 1**
 
I understand that I have to set `ASPNETCORE_ENVIRONMENT` however I am not able to understand where do we set this? During development I can set this in profile in `launchSettings.json`, however question was how do we set this when publishing to staging or production? do we set environment variable on the target server itself? 

[![enter image description here][3]][3]

**Update 2**  
I found article [here][4] that explains different ways of setting environment variable. This partially answered my question. However when I publish the application, the publish process does not honor the environment variable while publishing `appsettings.{env.EnvironmentName}.json`  
I have created separate [post][5] for that question


  [1]: https://docs.asp.net/en/latest/fundamentals/environments.html
  [2]: http://i.stack.imgur.com/8K8Zs.png
  [3]: http://i.stack.imgur.com/55kOO.png
  [4]: http://andrewlock.net/how-to-set-the-hosting-environment-in-asp-net-core/
  [5]: https://stackoverflow.com/questions/39256057/how-to-publish-environment-specific-appsettings-in-net-core-app

How to set aspnetcore_environment in publish file?

I installed a new fresh copy of Laravel 5.3 using composer but I&#39;m getting this error:

&gt; The only supported ciphers are AES-128-CBC and AES-256-CBC with the
&gt; correct key lengths. Even though my app.php file in config directory specify    
&gt;  &#39;cipher&#39; =&gt; &#39;AES-128-CBC&#39;

The only supported ciphers are AES-128-CBC and AES-256-CBC with the correct key lengths. laravel 5.3

I feel like I&#39;m missing something really obvious here. I have classes that require injecting of options using the .NET Core `IOptions` pattern(?). When I unit test that class, I want to mock various versions of the options to validate the functionality of the class. Does anyone know how to correctly mock/instantiate/populate `IOptions&lt;T&gt;` outside of the Startup class? 


Here are some samples of the classes I&#39;m working with:

Settings/Options Model   
 
    using System;
    using System.Collections.Generic;
    using System.Linq;
    using System.Threading.Tasks;
    
    namespace OptionsSample.Models
    {
        public class SampleOptions
        {
            public string FirstSetting { get; set; }
            public int SecondSetting { get; set; }
        }
    }

 
Class to be tested which uses the Settings:


    using System;
    using System.Collections.Generic;
    using System.Linq;
    using System.Threading.Tasks;
    using OptionsSample.Models
    using System.Net.Http;
    using Microsoft.Extensions.Options;
    using System.IO;
    using Microsoft.AspNetCore.Http;
    using System.Xml.Linq;
    using Newtonsoft.Json;
    using System.Dynamic;
    using Microsoft.Extensions.Logging;
    
    namespace OptionsSample.Repositories
    {
        public class SampleRepo : ISampleRepo
        {
            private SampleOptions _options;
            private ILogger&lt;AzureStorageQueuePassthru&gt; _logger;
    
            public SampleRepo(IOptions&lt;SampleOptions&gt; options)
            {
                _options = options.Value;
            }
    
            public async Task Get()
            {
            }
        }
    }

Unit test in a different assembly from the other classes:

    using OptionsSample.Repositories;
    using OptionsSample.Models;
    using System;
    using System.Collections.Generic;
    using System.Linq;
    using System.Threading.Tasks;
    using Xunit;
    using Microsoft.Extensions.Logging;
    using Microsoft.AspNetCore.Http;
    using Microsoft.Extensions.Options;
    using Microsoft.Extensions.DependencyInjection;
    using Microsoft.Extensions.Configuration;
    
    namespace OptionsSample.Repositories.Tests
    {
        public class SampleRepoTests
        {
            private IOptions&lt;SampleOptions&gt; _options;
            private SampleRepo _sampleRepo;
    
    
            public SampleRepoTests()
            {
    			//Not sure how to populate IOptions&lt;SampleOptions&gt; here
                _options = options;
    
    			_sampleRepo = new SampleRepo(_options);
            }
        }
    }



.NET Core Unit Testing - Mock IOptions&lt;T&gt;

I want to write a function in Go to insert a document into a collection in a MongoDB database. Which way to name the function is better, 

- `writeToMongoDB` or 
- `WriteToMongoD`? 

The second is CamelCase, while I saw someone using the style of the first one, so I am not sure which one is more appropriate. Thanks.

Which way to name a function in Go, CamelCase or Semi-CamelCase?

Is there a recommended naming convention for key names within a const object in es6? I haven&#39;t been able to find a resource which states if they should be uppercase or lowercase.


    const COLOR_CODES = {
      BLUE: 1,
      RED: 1
    };
vs

    const COLOR_CODES = {
      blue: 1,
      red: 1
    };

[The examples](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/const) from this MDN article show both styles, so maybe both are acceptable.

Naming convention for const object keys in ES6

I constantly forget which is Camel case and which is Pascal case. So I thought that maybe a little history will help. Where do the names of these conventions come from? Is there some history behind their names?

Camel case and Pascal case mistake

I know that classes in Python are typically cased using camelCase.

Is it also the normal convention to have the file that contains the class also be camelCase&#39;d especially if the file only contains the class?

For example, should class `className` also be stored in `className.py` instead of `class_name.py`?

Should Python class filenames also be camelCased?

Is it possible to build (install, go get, etc) an executable with the name `foobar` if my Golang package name is one of the following:

 - `github.com/username/go-foobar`
 - `github.com/username/foobar-tools`

and has `main.go` in the package root?

How to build executable with name other than Golang package

I have a config with around 100 rules, and running eslint on my project with all these rules takes around 10 seconds. I&#39;d like to identify the slowest rules and eliminate some of them. How do I do this? Is there any profiler tool for eslint?

Which eslint rules in my config are slow?

I recently installed version 2017.2 of IntelliJ IDEA. I would like to keep my settings from my old 2017.1 version. IntelliJ&#39;s [support site](https://www.jetbrains.com/help/idea/install-and-set-up-intellij-idea.html#r_Install_and_set_up__product_.xmld320216e288) shows the following dialog box that is supposed to show up when you run the newly installed version for the first time.

[![Complete Installation Dialog][1]][1]


  [1]: https://i.stack.imgur.com/gLJa2.png

However, neither when I was installing the new version, nor when I ran the new version for the first time, was I ever shown this dialog box. I attempted to import settings from my old version via  the `File | Import Settings` menu option, but it expects a `.jar` file that would have been exported from an old version by going to `File | Export Settings`, which I did not do in the old version before installing the new one.

I do still have all of the config from my old version in `%MY_HOME_DIR%/.IntelliJIdea2017.1`.
How can I import those settings in the same way that the above dialog box would have done had it shown up when I opened the new version for the first time?

Content Type	Original Author	Original Content on Stackoverflow
Question	ztl	View Question on Stackoverflow
Solution 1 - Python	Daerdemandt	View Answer on Stackoverflow

What does "rc" in matplotlib's rcParams stand for?

Python Problem Overview

Python Solutions

Solution 1 - Python

ValidationError: "expiresInMinutes" is not allowed NodeJs JsonWebToken

Android Horizontal RecyclerView scroll Direction

Attributions