    import numpy as np
    import seaborn as sns
    
    x = np.random.randn(200)
    kwargs = {&#39;cumulative&#39;: True}
    sns.distplot(x, hist_kws=kwargs, kde_kws=kwargs)

[![enter image description here][1]][1]


  [1]: http://i.stack.imgur.com/eBDry.png

You can get almost the same plot using matplotlib by using `cumulative=True` and `density=True`. 

`plt.hist(x,cumulative=True, density=True, bins=30)`

So recently I had to update to the latest version of Angular2, RC.6. The biggest breaking change seems to be the whole bootstrapping (by &quot;introducing&quot; ngModule).

    @NgModule({
        imports: [HttpModule, BrowserModule, FormsModule],
        schemas: [CUSTOM_ELEMENTS_SCHEMA],
        declarations: [AppComponent, ...],
        providers: [FrameService, Http, { provide: $WINDOW,  useValue: window }],
        bootstrap: [AppComponent]
    })
    class AppModule {
    
    }
    
    platformBrowserDynamic().bootstrapModule(AppModule);

However after a lot of tears, sweat and pleading to all the deities I could come up with... I still remain with what is hopefully the last error in a series of many:

  **No provider for ConnectionBackend!**

At this point I am tearing out the last strains of hair I have left as I am clueless at this point regarding the &quot;what I am missing&quot;.



Kind regards!


No provider for ConnectionBackend

From [[5.3.3/1]](http://eel.is/c++draft/expr.sizeof#1), I found that:

&gt; The&#160;sizeof&#160;operator shall not be applied to an expression that has function or incomplete type

From [[3.9/5]](http://eel.is/c++draft/basic.types#5) I found that:

&gt; Incompletely-defined object types and&#160;cv void&#160;are&#160;incomplete types

Anyway, for `sizeof` does not evaluate it&#39;s operands, I would have said that `sizeof(void())` was a legal expression (actually GCC compiles it and the result is 1).  
On the other side, from [here](http://eel.is/c++draft/expr.sizeof), `void` is not mentioned while discussing `sizeof`, neither when the types having size 1 are mentioned, nor in the list of the ones having an _implementation defined_ size.

The question is thus: is `sizeof(void())` a legal expression?  
Is it guaranteed to have size equal to 1?  
Or is it a legal expression resulting in an UB and that&#39;s all?


Is sizeof(void()) a legal expression?

Is there a way to plot the CDF + cumulative histogram of a Pandas Series in Python using Seaborn only? I have the following:

    import numpy as np
    import pandas as pd
    import seaborn as sns
    s = pd.Series(np.random.normal(size=1000))
    
I know I can plot the cumulative histogram with `s.hist(cumulative=True, normed=1)`, and I know I can then plot the CDF using `sns.kdeplot(s, cumulative=True)`, but I want something that can do both in Seaborn, just like when plotting a distribution with `sns.distplot(s)`, which gives both the kde fit and the histogram. Is there a way?

Plot CDF + cumulative histogram using Seaborn Python

Is there a way to plot the CDF + cumulative histogram of a Pandas Series in Python using Seaborn only? I have the following:
<pre><code class="hljs language-javascript">import numpy as np
import pandas as pd
import seaborn as sns
s = pd.Series(np.random.normal(size=1000))
</code></pre>
I know I can plot the cumulative histogram with <code>s.hist(cumulative=True, normed=1)</code>, and I know I can then plot the CDF using <code>sns.kdeplot(s, cumulative=True)</code>, but I want something that can do both in Seaborn, just like when plotting a distribution with <code>sns.distplot(s)</code>, which gives both the kde fit and the histogram. Is there a way?

I&#39;m using TfidfVectorizer from scikit-learn to do some feature extraction from text data. I have a CSV file with a Score (can be +1 or -1) and a Review (text). I pulled this data into a DataFrame so I can run the Vectorizer.

This is my code: 

    import pandas as pd
    import numpy as np
    from sklearn.feature_extraction.text import TfidfVectorizer

    df = pd.read_csv(&quot;train_new.csv&quot;,
                 names = [&#39;Score&#39;, &#39;Review&#39;], sep=&#39;,&#39;)

    # x = df[&#39;Review&#39;] == np.nan
    #
    # print x.to_csv(path=&#39;FindNaN.csv&#39;, sep=&#39;,&#39;, na_rep = &#39;string&#39;, index=True)
    #
    # print df.isnull().values.any()

    v = TfidfVectorizer(decode_error=&#39;replace&#39;, encoding=&#39;utf-8&#39;)
    x = v.fit_transform(df[&#39;Review&#39;])

This is the traceback for the error I get: 


    Traceback (most recent call last):
      File &quot;/home/PycharmProjects/Review/src/feature_extraction.py&quot;, line 16, in &lt;module&gt;
    x = v.fit_transform(df[&#39;Review&#39;])
     File &quot;/home/b/hw1/local/lib/python2.7/site-   packages/sklearn/feature_extraction/text.py&quot;, line 1305, in fit_transform
       X = super(TfidfVectorizer, self).fit_transform(raw_documents)
     File &quot;/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py&quot;, line 817, in fit_transform
    self.fixed_vocabulary_)
     File &quot;/home/b/work/local/lib/python2.7/site- packages/sklearn/feature_extraction/text.py&quot;, line 752, in _count_vocab
       for feature in analyze(doc):
     File &quot;/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py&quot;, line 238, in &lt;lambda&gt;
    tokenize(preprocess(self.decode(doc))), stop_words)
     File &quot;/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py&quot;, line 118, in decode
     raise ValueError(&quot;np.nan is an invalid document, expected byte or &quot;
     ValueError: np.nan is an invalid document, expected byte or unicode string.



I checked the CSV file and DataFrame for anything that&#39;s being read as NaN but I can&#39;t find anything. There are 18000 rows, none of which return `isnan` as True. 


This is what `df[&#39;Review&#39;].head()` looks like: 

      0    This book is such a life saver.  It has been s...
      1    I bought this a few times for my older son and...
      2    This is great for basics, but I wish the space...
      3    This book is perfect!  I&#39;m a first time new mo...
      4    During your postpartum stay at the hospital th...
      Name: Review, dtype: object

TfidfVectorizer in scikit-learn : ValueError: np.nan is an invalid document

I am new to Python OpenCV. I have read some documents and answers [here](https://stackoverflow.com/questions/19363293/whats-the-fastest-way-to-increase-color-image-contrast-with-opencv-in-python-c) but I am unable to figure out what the following code means:

    if (self.array_alpha is None):
        self.array_alpha = np.array([1.25])
        self.array_beta = np.array([-100.0])

    # add a beta value to every pixel 
    cv2.add(new_img, self.array_beta, new_img)                    

    # multiply every pixel value by alpha
    cv2.multiply(new_img, self.array_alpha, new_img)  

I have come to know that `Basically, every pixel can be transformed as X = aY + b where a and b are scalars.`. Basically, I have understood this. However, I did not understand the code and how to increase contrast with this.

Till now, I have managed to simply read the image using `img = cv2.imread(&#39;image.jpg&#39;,0)`

Thanks for your help

How do I increase the contrast of an image in Python OpenCV

I&#39;m able to find a bevy of information online (on Stack&amp;nbsp;Overflow and otherwise) about how it&#39;s a very inefficient and bad practice to use `+` or `+=` for concatenation in Python.

I can&#39;t seem to find WHY `+=` is so inefficient. Outside of a mention [here][1] that &quot;it&#39;s been optimized for 20% improvement in certain cases&quot; (still not clear what those cases are), I can&#39;t find any additional information.

What is happening on a more technical level that makes `&#39;&#39;.join()` superior to other Python concatenation methods?

  [1]: https://stackoverflow.com/a/1350289/3903011 &quot;What is the most efficient string concatenation method in python?&quot;


Why is &#39;&#39;.join() faster than += in Python?

I&#39;m loading in a color image in Python OpenCV and plotting the same. However, the image I get has it&#39;s colors all mixed up. 

Here is the code:

    import cv2
	import numpy as np
	from numpy import array, arange, uint8 
	from matplotlib import pyplot as plt


	img = cv2.imread(&#39;lena_caption.png&#39;, cv2.IMREAD_COLOR)
	bw_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

	images = []
	images.append(img)
	images.append(bw_img)

	titles = [&#39;Original Image&#39;,&#39;BW Image&#39;]
	 
	for i in xrange(len(images)):
	    plt.subplot(1,2,i+1),plt.imshow(images[i],&#39;gray&#39;)
	    plt.title(titles[i])
	    plt.xticks([]),plt.yticks([])

	plt.show()


Here is the original image:
[![enter image description here][1]][1]

And here is the plotted image:
[![enter image description here][2]][2]


  [1]: http://i.stack.imgur.com/o9G7e.png
  [2]: http://i.stack.imgur.com/KagWq.png

OpenCV giving wrong color to colored images on loading

I import tqdm as this:

    import tqdm

I am using tqdm to show progress in my python3 code, but I have the following error:

    Traceback (most recent call last):
      File &quot;process.py&quot;, line 15, in &lt;module&gt;
        for dir in tqdm(os.listdir(path), desc = &#39;dirs&#39;):
    TypeError: &#39;module&#39; object is not callable

Here is the code:

    path = &#39;../dialogs&#39;
    dirs = os.listdir(path)
            
    for dir in tqdm(dirs, desc = &#39;dirs&#39;):
    	print(dir)




tqdm: &#39;module&#39; object is not callable

In my code, I have several variables which can either contain a pandas DataFrame or nothing at all.  Let&#39;s say I want to test and see if a certain DataFrame has been created yet or not.  My first thought would be to test for it like this:

    if df1:
        # do something

However, that code fails in this way:

    ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().


Fair enough.  Ideally, I would like to have a presence test that works for either a DataFrame or Python None.  

Here is one way this can work:

    if not isinstance(df1, type(None)):
        # do something

However, testing for type is really slow.

    t = timeit.Timer(&#39;if None: pass&#39;)
    t.timeit()
    # approximately 0.04
    t = timeit.Timer(&#39;if isinstance(x, type(None)): pass&#39;, setup=&#39;x=None&#39;)
    t.timeit()
    # approximately 0.4

Ouch.  Along with being slow, testing for NoneType isn&#39;t very flexible, either.  

A different solution would be to initialize `df1` as an empty DataFrame, so that the type would be the same in both the null and non-null cases.  I could then just test using `len()`, or `any()`, or something like that.  Making an empty DataFrame seems kind of silly and wasteful, though.

Another solution would be to have an indicator variable: `df1_exists`, which is set to False until `df1` is created.  Then, instead of testing `df1`, I would be testing `df1_exists`.  But this doesn&#39;t seem all that elegant, either.

Is there a better, more Pythonic way of handling this issue?  Am I missing something, or is this just an awkward side effect all the awesome things about pandas? 



Testing if a pandas DataFrame exists

I have a column `Date_Time` that I wish to groupby date time without creating a new column. Is this possible the current code I have does not work.

    df = pd.groupby(df,by=[df[&#39;Date_Time&#39;].date()])



Python Pandas Group by date using datetime data

I have a dataframe that may look like this:

    A        B        C
    foo      bar      foo bar
    bar foo  foo      bar

I want to look through every element of each row (or every element of each column) and apply the following function to get the subsequent DF:

    def foo_bar(x):
        return x.replace(&#39;foo&#39;, &#39;wow&#39;)

    A        B        C
    wow      bar      wow bar
    bar wow  wow      bar

Is there a simple one-liner that can apply a function to each cell? 

This is a simplistic example so there may be an easier way to execute this specific example other than applying a function, but what I am really asking about is how to apply a function in every cell within a dataframe. 

Apply function to each cell in DataFrame

I have a dataframe extracted from Kaggle&#39;s San Fransico Salaries: https://www.kaggle.com/kaggle/sf-salaries
and I wish to create a set of the values of a column, for instance &#39;Status&#39;.

This is what I have tried but it brings a list of all the records instead of the set (sf is how I name the data frame).

    a=set(sf[&#39;Status&#39;])
    print a

According to this webpage, this should work.
https://stackoverflow.com/questions/15768757/how-to-construct-a-set-out-of-list-items

Create a set from a series in pandas

I am using matlibplot and I would like to manually add items to the legend that are a color and a label. I am adding data to to the plot to specifying there would lead to a lot of duplicates.

My thought was to do:

		ax2.legend(self.labels,colorList[:len(self.labels)])
        plt.legend()

Where self.labels is the number of items I want legend lables for that takes a subset of the large color list. However this yields nothing when I run it.

Am I missing anything?

Thanks

How to manually create a legend

I have a dataframe like this

    import seaborn as sns
    import pandas as pd
    %pylab inline

    df = pd.DataFrame({&#39;a&#39; :[&#39;one&#39;,&#39;one&#39;,&#39;two&#39;,&#39;two&#39;,&#39;one&#39;,&#39;two&#39;,&#39;one&#39;,&#39;one&#39;,&#39;one&#39;,&#39;two&#39;], 
                       &#39;b&#39;: [1,2,1,2,1,2,1,2,1,1], 
                       &#39;c&#39;: [1,2,3,4,6,1,2,3,4,6]})

A single boxplot is OK:

    sns.boxplot(y=&quot;b&quot;, x=&quot;a&quot;, data=df, orient=&#39;v&#39;)

But I want to build a subplot for all variables. I tried:

    names = [&#39;b&#39;, &#39;c&#39;]
    plt.subplots(1,2)
    sub = []

    for name in names:
        ax = sns.boxplot(  y=name, x= &quot;a&quot;, data=df,  orient=&#39;v&#39; )
        sub.append(ax)

but it outputs:

[![enter image description here][1]][1]


  [1]: https://i.stack.imgur.com/tEqqC.png

Subplot for seaborn boxplot

Why do you always import seaborn as `sns` and not with the letters of the name as `sbn`?

Is `sns` an acronym for something? 

Or is it some kind of joke?

**EDIT:**

As [cel](https://stackoverflow.com/users/2272172/cel) commented, someone put this as an issue [in github](https://github.com/mwaskom/seaborn/issues/229).   
There they do not answer the question but say that it is a joke related to the original name of the library.  
I was unsuccessful in trying to find that original name.

Seaborn - Why import as sns?

If I have the following data and Seaborn Heatmap:
 
    import pandas as pd
    data = pd.DataFrame({&#39;x&#39;:(1,2,3,4),&#39;y&#39;:(1,2,3,4),&#39;z&#39;:(14,15,23,2)})
   
    sns.heatmap(data.pivot_table(index=&#39;y&#39;, columns=&#39;x&#39;, values=&#39;z&#39;))

How do I add a label to the colour bar?


How to add a label to Seaborn Heatmap color bar?

Seems pretty Googleable but haven&#39;t been able to find something online that works.

I&#39;ve tried both `sns.boxplot(&#39;Day&#39;, &#39;Count&#39;, data= gg).title(&#39;lalala&#39;)` and `sns.boxplot(&#39;Day&#39;, &#39;Count&#39;, data= gg).suptitle(&#39;lalala&#39;)`. None worked. I think it might be because I&#39;m also working with matplotlib.

Content Type	Original Author	Original Content on Stackoverflow
Question	Michael	View Question on Stackoverflow
Solution 1 - Python	mwaskom	View Answer on Stackoverflow
Solution 2 - Python	Sarah	View Answer on Stackoverflow

Plot CDF + cumulative histogram using Seaborn Python

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

Is sizeof(void()) a legal expression?

No provider for ConnectionBackend

Attributions