It looks like either your `linewidth` was set to zero or your `edgecolor` was set to `&#39;none&#39;`.  Matplotlib changed the defaults for these in 2.0.  Try using: 

    plt.hist(gaussian_numbers, edgecolor=&#39;black&#39;, linewidth=1.2)

[![enter image description here][1]][1]


  [1]: https://i.stack.imgur.com/jKQMo.png

I use KerasClassifier to train the classifier.

The code is below:

    import numpy
    from pandas import read_csv
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.wrappers.scikit_learn import KerasClassifier
    from keras.utils import np_utils
    from sklearn.model_selection import cross_val_score
    from sklearn.model_selection import KFold
    from sklearn.preprocessing import LabelEncoder
    from sklearn.pipeline import Pipeline
    # fix random seed for reproducibility
    seed = 7
    numpy.random.seed(seed)
    # load dataset
    dataframe = read_csv(&quot;iris.csv&quot;, header=None)
    dataset = dataframe.values
    X = dataset[:,0:4].astype(float)
    Y = dataset[:,4]
    # encode class values as integers
    encoder = LabelEncoder()
    encoder.fit(Y)
    encoded_Y = encoder.transform(Y)
    #print(&quot;encoded_Y&quot;)
    #print(encoded_Y)
    # convert integers to dummy variables (i.e. one hot encoded)
    dummy_y = np_utils.to_categorical(encoded_Y)
    #print(&quot;dummy_y&quot;)
    #print(dummy_y)
    # define baseline model
    def baseline_model():
        # create model
        model = Sequential()
        model.add(Dense(4, input_dim=4, init=&#39;normal&#39;, activation=&#39;relu&#39;))
        #model.add(Dense(4, init=&#39;normal&#39;, activation=&#39;relu&#39;))
        model.add(Dense(3, init=&#39;normal&#39;, activation=&#39;softmax&#39;))
        # Compile model
        model.compile(loss=&#39;categorical_crossentropy&#39;, optimizer=&#39;adam&#39;, metrics=[&#39;accuracy&#39;])
        
        return model
    
    estimator = KerasClassifier(build_fn=baseline_model, nb_epoch=200, batch_size=5, verbose=0)
    #global_model = baseline_model()
    kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
    results = cross_val_score(estimator, X, dummy_y, cv=kfold)
    print(&quot;Accuracy: %.2f%% (%.2f%%)&quot; % (results.mean()*100, results.std()*100))



But How to save the final model for future prediction?

I usually use below code to save model:

    # serialize model to JSON
    model_json = model.to_json()
    with open(&quot;model.json&quot;, &quot;w&quot;) as json_file:
        json_file.write(model_json)
    # serialize weights to HDF5
    model.save_weights(&quot;model.h5&quot;)
    print(&quot;Saved model to disk&quot;)

But I don&#39;t know how to insert the saving model&#39;s code into KerasClassifier&#39;s code.

Thank you.

How to save final model using keras?

In a react app, I have a method being called to bring a particular node into view as follows.

    scrollToQuestionNode(id) {
            const element = document.getElementById(id);
            element.scrollIntoView(false);
    }

The scroll happens fine, but the scroll action is a little jerky. How can I make it smooth? I don&#39;t see any options which I can give to scrollIntoView for the same.

how to make javascript scrollIntoView smooth?

I have plotted a histogram in Jupyter (Python 2) and was expecting to see the outlines of my bars but this is not the case. 

[![enter image description here][1]][1]

I&#39;m using the following code:

    import matplotlib.pyplot as plt
    from numpy.random import normal
    gaussian_numbers = normal(size=1000)
    plt.hist(gaussian_numbers)
    plt.title(&quot;Gaussian Histogram&quot;)
    plt.xlabel(&quot;Value&quot;)
    plt.ylabel(&quot;Frequency&quot;)
    plt.show()


  [1]: https://i.stack.imgur.com/r97fv.png

Python histogram outline

I have plotted a histogram in Jupyter (Python 2) and was expecting to see the outlines of my bars but this is not the case.
<a href="https://i.stack.imgur.com/r97fv.png" target="_blank" rel="noopener noreferrer"><img src="https://i.stack.imgur.com/r97fv.png" alt="enter image description here"></a>
I'm using the following code:
<pre><code class="hljs language-scss">import matplotlib.pyplot as plt
from numpy.random import normal
gaussian_numbers = normal(size=1000)
plt.hist(gaussian_numbers)
plt.title("Gaussian Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()
</code></pre>

I have two lists:

* a list of about 750K ***&quot;sentences&quot;*** (long strings)
* a list of about 20K ***&quot;words&quot;*** that I would like to delete from my 750K sentences

So, I have to loop through 750K *sentences* and perform about 20K replacements, **but ONLY if my words are actually *&quot;words&quot;* and are not part of a larger string of characters.**

I am doing this by pre-compiling my *words* so that they are flanked by the `\b` word-boundary metacharacter:

    compiled_words = [re.compile(r&#39;\b&#39; + word + r&#39;\b&#39;) for word in my20000words]

Then I loop through my *&quot;sentences&quot;*:

    import re
    
    for sentence in sentences:
      for word in compiled_words:
        sentence = re.sub(word, &quot;&quot;, sentence)
      # put sentence into a growing list

This nested loop is processing about **50 sentences per second**, which is nice, but it still takes several hours to process all of my sentences.

* Is there a way to using the `str.replace` method (which I believe is faster), but still requiring that replacements only happen at **word boundaries**?

* Alternatively, is there a way to speed up the `re.sub` method? I have already improved the speed marginally by skipping over `re.sub` if the length of my word is &gt; than the length of my sentence, but it&#39;s not much of an improvement.

I&#39;m using Python 3.5.2

Speed up millions of regex replacements in Python 3

Conda env is activated using **source activate env_name**. 

How can I activate the environment in pycharm ?

Use Conda environment in pycharm

I have been examining some of my string format options using the new f-string format.  I routinely need to unpack lists and other iterables of unknown length. Currently I use the following...

    &gt;&gt;&gt; a = [1, &#39;a&#39;, 3, &#39;b&#39;]
    &gt;&gt;&gt; (&quot;unpack a list: &quot; + &quot; {} &quot;*len(a)).format(*a)
    &#39;unpack a list:  1  a  3  b &#39;
This, albeit a bit cumbersome, does the job using pre-3.6 .format notation.
The new f-string format option is interesting given runtime string concatenation.  It is the replication of the number of {} that I am having problems with.  In my previous example, I simply created the necessary structure and unpacked within the .format() section.

Attempts to do this yielded one variant that worked, however:

### 1) Both curly brackets together doesn&#39;t unpack...

    &gt;&gt;&gt; &#39;unpack a list&#39;  f&#39; {{*a}}&#39;
    &#39;unpack a list {*a}&#39;

### 2) Adding spaces around the interior {} pair:

This works but leaves opening and closing braces {, } present:

    &gt;&gt;&gt; &#39;unpack a list&#39;  f&#39; { {*a} }&#39;
    &quot;unpack a list {1, 3, &#39;a&#39;, &#39;b&#39;}&quot;

### 2b) Concatenating the variants into one f-string

This made the look and syntax better, since the evaluation, apparently, is from left to right.  This, however, still left the enclosing curly brackets present:

    &gt;&gt;&gt; f&#39;unpack a list { {*a} }&#39;
    &quot;unpack a list {1, 3, &#39;a&#39;, &#39;b&#39;}&quot;

### 3) Tried automatic unpacking with just `{a}`
Perhaps, I was overthinking the whole procedure and hoping for some form of automatic unpacking.  This simply yielded the list representation with the curly brackets being replaced with `[]` :

    &gt;&gt;&gt; f&#39;unpack a list {a}&#39;
    &quot;unpack a list [1, &#39;a&#39;, 3, &#39;b&#39;]&quot;
    
What is required to suppress the curly brackets in variant (2) above, or must I keep using the existing `.format()` method?  I want to keep it simple and use the new capabilities offered by the f-string and not revert back beyond the python versions which pre-date what I am currently comfortable with.  I am beginning to suspect that f&#39;strings&#39; do not offer a complete coverage of what is offered by its `.format()` sibling.  I will leave it at that for now, since I haven&#39;t even ventured into the escape encoding and the inability to use \ in an f-string.  I have read the PEP and search widely, however, I feel I am missing the obvious or what I wish for is currently not possible.

EDIT several hours later:

### 4) Use subscripting to manually slice off the brackets: `str(a)[1:-2]`

I did find this variant which will serve for some cases that I need

    f&#39;unpack a list: {str(a)[1:-2]}&#39;
    &quot;unpack a list: 1, &#39;a&#39;, 3, &#39;b&quot;

But the slicing is little more than a convenience and still leaves the string quotes around the resultant.

### 5)  and the final solution from @SenhorLucas

     a = np.arange(10)
    
    print(f&quot;{*a,}&quot;)
    (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)

Unpacking with trailing comma.

f-string syntax for unpacking a list with brace suppression

This is the table partition key setting
[![enter image description here][1]][1]

The table content
[![enter image description here][2]][2]

When I tried to get an item from the table, it prints this error 

&gt; botocore.exceptions.ClientError: An error occurred
&gt; (ValidationException) when calling the GetItem operation: The provided
&gt; key element does not match the schema

This is my code

    dynamodb = boto3.resource(&#39;dynamodb&#39;)
    table = dynamodb.Table(&#39;testDynamodb&#39;)
    response = table.get_item(Key={&#39;userId&#39;: &quot;user2873&quot;})
    item = response[&#39;Item&#39;]
    print(item)

Any ideas? thanks.

  [1]: https://i.stack.imgur.com/RPnjj.png
  [2]: https://i.stack.imgur.com/kbKPU.png

&quot;The provided key element does not match the schema&quot; error when getting an item from DynamoDB

I would like to change the thickness/width of the line samples featured in the pyplot legend. 

Line width of line samples within legend are the same as the lines they represent in the plot (so if line `y1` has `linewidth=7.0`, the legend&#39;s corresponding `y1` label will also have `linewidth=7.0`). 

I would like the legend lines to be thicker than lines featured in the plot.


For example, the following code generates the following image:

    import numpy as np
    import matplotlib.pyplot as plt
    
    # make some data
    x = np.linspace(0, 2*np.pi)
    
    y1 = np.sin(x)
    y2 = np.cos(x)
    
    # plot sin(x) and cos(x)
    fig = plt.figure()
    ax  = fig.add_subplot(111)
    ax.plot(x, y1, c=&#39;b&#39;, label=&#39;y1&#39;,linewidth=7.0)
    ax.plot(x, y2, c=&#39;r&#39;, label=&#39;y2&#39;)
    
    leg = plt.legend()
    plt.show()

[![example code plot][1]][1]

I want to set the `y1` label in the legend to have `linewidth=7.0`, while the `y1` line featured in the plot has a different width (`linewidth=1.0`).

 

I was unsuccessful in finding a solution online. Related issues had answers for changing the line width of the legend bounding box through `leg.get_frame().set_linewidth(7.0)`. This does not change line width of the lines *within* the legend.
 


  [1]: https://i.stack.imgur.com/sEPCp.png

Change line width of lines in matplotlib pyplot legend

I am a newbie to keras, and when I tried to run my first keras program on my linux, something just didn&#39;t go as I wish.
Here is my python code:

    import numpy as np
    np.random.seed(123)
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Activation, Flatten
    from keras.layers import Convolution2D, MaxPooling2D
    from keras.utils import np_utils
    from keras.datasets import mnist
    (X_train,y_train),(X_test,y_test) = mnist.load_data()
    print X_train.shape
    from matplotlib import pyplot as plt
    plt.imshow(X_train[0])

The last sentence doesn&#39;t display anything. I copied those codes from a tutorial with out any modification. And there is nothing wrong with the backend of matplotlib on my computer. I have tested that through the code below.

    import matplotlib.pyplot as plt
    
    data = [[0, 0.25], [0.5, 0.75]]
    
    fig, ax = plt.subplots()
    im = ax.imshow(data, cmap=plt.get_cmap(&#39;hot&#39;), interpolation=&#39;nearest&#39;,
                   vmin=0, vmax=1)
    fig.colorbar(im)
    plt.show()

And then I got a image like that:
[![enter image description here][1]][1]


  [1]: https://i.stack.imgur.com/2r6Lw.png

&lt;br&gt;
Moreover, I can get X_train[0] printed and it seems nothing wrong.&lt;br&gt;
So what could be the reason for that? Why the imshow() function in my first code didn&#39;t display anything?

Why plt.imshow() doesn&#39;t display the image?

I found the following example on matplotlib:

    import numpy as np
    import matplotlib.pyplot as plt


    x1 = np.linspace(0.0, 5.0)
    x2 = np.linspace(0.0, 2.0)

    y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
    y2 = np.cos(2 * np.pi * x2)

    plt.subplot(2, 1, 1)
    plt.plot(x1, y1, &#39;ko-&#39;)
    plt.title(&#39;A tale of 2 subplots&#39;)
    plt.ylabel(&#39;Damped oscillation&#39;)


    plt.subplot(2, 1, 2)
    plt.plot(x2, y2, &#39;r.-&#39;)
    plt.xlabel(&#39;time (s)&#39;)
    plt.ylabel(&#39;Undamped&#39;)

    plt.show()


My question is: What do i need to change, to have the plots side-by-side?



How to make two plots side-by-side using Python?

I&#39;m trying to share two subplots axis, but I need to share x axis after the figure was created.
So, for instance, I create this figure:

    import numpy as np
    import matplotlib.pyplot as plt

    t= np.arange(1000)/100.
    x = np.sin(2*np.pi*10*t)
    y = np.cos(2*np.pi*10*t)
    
    fig=plt.figure()
    ax1 = plt.subplot(211)
    plt.plot(t,x)
    ax2 = plt.subplot(212)
    plt.plot(t,y)
    
    # some code to share both x axis

    plt.show()
Instead of the comment I would insert some code to share both x axis.
I didn&#39;t find any clue how i can do that. There are some attributes 
`_shared_x_axes` and `_shared_x_axes` when i check to figure axis (`fig.get_axes()`) but I don&#39;t know how to link them.

How to share x axes of two subplots after they have been created

What exactly is the use of `%matplotlib inline`? 

Purpose of &quot;%matplotlib inline&quot;

I am using seaborn to plot a distribution plot. I would like to plot multiple distributions on the same plot in different colors:

Here&#39;s how I start the distribution plot:

    import numpy as np
    import pandas as pd
    from sklearn.datasets import load_iris
    iris = load_iris()
    iris = pd.DataFrame(data= np.c_[iris[&#39;data&#39;], iris[&#39;target&#39;]],columns= iris[&#39;feature_names&#39;] + [&#39;target&#39;])

       sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target
    0                5.1               3.5                1.4               0.2     0.0
    1                4.9               3.0                1.4               0.2     0.0
    2                4.7               3.2                1.3               0.2     0.0
    3                4.6               3.1                1.5               0.2     0.0
    4                5.0               3.6                1.4               0.2     0.0
    
    sns.distplot(iris[[&#39;sepal length (cm)&#39;]], hist=False, rug=True);

[![enter image description here][1]][1]

The `&#39;target&#39;` column contains 3 values: 0, 1, 2.

I would like to see one distribution plot for sepal length, where `target ==0`, `target ==1`, and `target ==2`, for a total of 3 plots. 

  [1]: https://i.stack.imgur.com/JlzKB.png

Content Type	Original Author	Original Content on Stackoverflow
Question	Brad Reed	View Question on Stackoverflow
Solution 1 - Python	James	View Answer on Stackoverflow

Python histogram outline

Python Problem Overview

Python Solutions

Solution 1 - Python

how to make javascript scrollIntoView smooth?

How to save final model using keras?

Attributions