as explained [here][1]


  [1]: http://widu.tumblr.com/post/43624347354/matplotlib-trendline


With help from numpy one can calculate for example a linear fitting.

    # plot the data itself
    pylab.plot(x,y,&#39;o&#39;)

    # calc the trendline
    z = numpy.polyfit(x, y, 1)
    p = numpy.poly1d(z)
    pylab.plot(x,p(x),&quot;r--&quot;)
    # the line equation:
    print &quot;y=%.6fx+(%.6f)&quot;%(z[0],z[1])

I am trying to add the onElse function to the itterator.firstWhere method but I cannot get the syntax right.

I have tried something like

&lt;!-- language-all: lang-dart --&gt;

    List&lt;String&gt; myList = 
  
    String result = myList.firstWhere((o) =&gt; o.startsWith(&#39;foo&#39;), (o) =&gt; null);

But the compiler has an error of 

&gt; 1 positional arguments expected, but 2 found

I am sure it is a simple syntax problem, but it has me stumped


Adding orElse function to firstWhere method

I want to get the string character from an ObjectId object. I use pymongo.
eg: `ObjectId(&quot;543b591d91b9e510a06a42e2&quot;)`, I want to get `&quot;543b591d91b9e510a06a42e2&quot;`.

I see the doc, It says `ObjectId.toString()`, `ObjectId.valueOf()`. 

So I make this code: `from bson.objectid import ObjectId`. 

But when I use `ObjectId.valueOf()`, It shows: 

&gt; &#39;ObjectId&#39; object has no attribute &#39;valueOf&#39;.

How can I get it? Thanks.

How to convert objectid to string

How could I add trendline to a dot graph drawn using matplotlib.scatter?

How to add trendline in python matplotlib dot (scatter) graphs?

<p>How could I add trendline to a dot graph drawn using matplotlib.scatter?</p>


If I get the number 46 and I want to round up to the nearest ten. How do can I do this in python?

46 goes to 50.

Python - round up to the nearest ten

I&#39;ve frequented used pandas&#39; `agg()` function to run summary statistics on every column of a data.frame.  For example, here&#39;s how you would produce the mean and standard deviation:

    df = pd.DataFrame({&#39;A&#39;: [&#39;group1&#39;, &#39;group1&#39;, &#39;group2&#39;, &#39;group2&#39;, &#39;group3&#39;, &#39;group3&#39;],
                       &#39;B&#39;: [10, 12, 10, 25, 10, 12],
                       &#39;C&#39;: [100, 102, 100, 250, 100, 102]})
    
    &gt;&gt;&gt; df
    [output]
            A   B    C
    0  group1  10  100
    1  group1  12  102
    2  group2  10  100
    3  group2  25  250
    4  group3  10  100
    5  group3  12  102

In both of those cases, the order that individual rows are sent to the agg function does not matter.  But consider the following example, which:

    df.groupby(&#39;A&#39;).agg([np.mean, lambda x: x.iloc[1] ])
    
    [output]
    
            mean  &lt;lambda&gt;  mean  &lt;lambda&gt;
    A                                     
    group1  11.0        12   101       102
    group2  17.5        25   175       250
    group3  11.0        12   101       102

In this case the lambda functions as intended, outputting the second row in each group.  However, I have not been able to find anything in the pandas documentation that implies that this is guaranteed to be true in all cases.  I want use `agg()` along with a weighted average function, so I want to be sure that the rows that come into the function will be in the same order as they appear in the original data frame.

Does anyone know, ideally via somewhere in the docs or pandas source code, if this is guaranteed to be the case?


Python Pandas: Is Order Preserved When Using groupby() and agg()?

A pandas DataFrame column `duration` contains `timedelta64[ns]` as shown. How can you convert them to seconds?

	0   00:20:32
	1   00:23:10
	2   00:24:55
	3   00:13:17
	4   00:18:52
	Name: duration, dtype: timedelta64[ns]

I tried the following

	print df[:5][&#39;duration&#39;] / np.timedelta64(1, &#39;s&#39;)

but got the error

	Traceback (most recent call last):
	  File &quot;test.py&quot;, line 16, in &lt;module&gt;
	    print df[0:5][&#39;duration&#39;] / np.timedelta64(1, &#39;s&#39;)
	  File &quot;C:\Python27\lib\site-packages\pandas\core\series.py&quot;, line 130, in wrapper
	    &quot;addition and subtraction, but the operator [%s] was passed&quot; % name)
	TypeError: can only operate on a timedeltas for addition and subtraction, but the operator [__div__] was passed

Also tried

	print df[:5][&#39;duration&#39;].astype(&#39;timedelta64[s]&#39;)

but received the error

	Traceback (most recent call last):
	  File &quot;test.py&quot;, line 17, in &lt;module&gt;
	    print df[:5][&#39;duration&#39;].astype(&#39;timedelta64[s]&#39;)
	  File &quot;C:\Python27\lib\site-packages\pandas\core\series.py&quot;, line 934, in astype
	    values = com._astype_nansafe(self.values, dtype)
	  File &quot;C:\Python27\lib\site-packages\pandas\core\common.py&quot;, line 1653, in _astype_nansafe
	    raise TypeError(&quot;cannot astype a timedelta from [%s] to [%s]&quot; % (arr.dtype,dtype))
	TypeError: cannot astype a timedelta from [timedelta64[ns]] to [timedelta64[s]]

Convert timedelta64[ns] column to seconds in Python Pandas DataFrame

I have a freshly installed Ubuntu on a freshly built computer. I just installed python-pip using apt-get. Now when I try to pip install Numpy and Pandas, it gives the following error.

I&#39;ve seen this error mentioned in quite a few places on SO and Google, but I haven&#39;t been able to find a solution. Some people mention it&#39;s a bug, some threads are just dead... What&#39;s going on?


    Traceback (most recent call last):
      File &quot;/usr/bin/pip&quot;, line 9, in &lt;module&gt;
        load_entry_point(&#39;pip==1.5.4&#39;, &#39;console_scripts&#39;, &#39;pip&#39;)()
      File &quot;/usr/lib/python2.7/dist-packages/pip/__init__.py&quot;, line 185, in main
        return command.main(cmd_args)
      File &quot;/usr/lib/python2.7/dist-packages/pip/basecommand.py&quot;, line 161, in main
        text = &#39;\n&#39;.join(complete_log)
    UnicodeDecodeError: &#39;ascii&#39; codec can&#39;t decode byte 0xe2 in position 72: ordinal not in range(128)



PIP Install Numpy throws an error &quot;ascii codec can&#39;t decode byte 0xe2&quot;

I use linear SVM from scikit learn (LinearSVC) for binary classification problem. I understand that LinearSVC can give me the predicted labels, and the decision scores but I wanted probability estimates (confidence in the label). I want to continue using LinearSVC because of speed (as compared to sklearn.svm.SVC with linear kernel) Is it reasonable to use a logistic function to convert the decision scores to probabilities? 

    import sklearn.svm as suppmach
    # Fit model:
    svmmodel=suppmach.LinearSVC(penalty=&#39;l1&#39;,C=1)
    predicted_test= svmmodel.predict(x_test)
    predicted_test_scores= svmmodel.decision_function(x_test) 

I want to check if it makes sense to obtain Probability estimates simply as [1 / (1 + exp(-x)) ]  where x is the decision score. 


Alternately, are there other options wrt classifiers that I can use to do this efficiently? 

Thanks. 



Content Type	Original Author	Original Content on Stackoverflow
Question	user3476791	View Question on Stackoverflow
Solution 1 - Python	martinenzinger	View Answer on Stackoverflow

How to add trendline in python matplotlib dot (scatter) graphs?

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Solution 1 - Python

How to convert objectid to string

Adding orElse function to firstWhere method

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