  I assume I have `import datetime` before running each of the lines of code below

    datetime.datetime.strptime(&quot;2013-1-25&quot;, &#39;%Y-%m-%d&#39;).strftime(&#39;%m/%d/%y&#39;)

prints `&quot;01/25/13&quot;`.

If you can&#39;t live with the leading zero, try this:


    dt = datetime.datetime.strptime(&quot;2013-1-25&quot;, &#39;%Y-%m-%d&#39;)
    print &#39;{0}/{1}/{2:02}&#39;.format(dt.month, dt.day, dt.year % 100)

This prints `&quot;1/25/13&quot;`.

EDIT: This may not work on every platform:

  
    datetime.datetime.strptime(&quot;2013-1-25&quot;, &#39;%Y-%m-%d&#39;).strftime(&#39;%m/%d/%y&#39;)

If you can live with 01 for January instead of 1, then try...

    d = datetime.datetime.strptime(&quot;2013-1-25&quot;, &#39;%Y-%m-%d&#39;)
    print datetime.date.strftime(d, &quot;%m/%d/%y&quot;)

You can check the [docs][1] for other formatting directives.


  [1]: http://docs.python.org/2/library/datetime.html?highlight=strftime#strftime-and-strptime-behavior

I have string like this:

    years&lt;-c(&quot;20 years old&quot;, &quot;1 years old&quot;)

I would like to grep only the numeric number from this vector. Expected output is a vector:

    c(20, 1)

How do I go about doing this?

Extracting numbers from vectors of strings

Based on [this question about heatmaps in matplotlib][1], I wanted to move the x-axis titles to the top of the plot.

    import matplotlib.pyplot as plt
    import numpy as np
    column_labels = list(&#39;ABCD&#39;)
    row_labels = list(&#39;WXYZ&#39;)
    data = np.random.rand(4,4)
    fig, ax = plt.subplots()
    heatmap = ax.pcolor(data, cmap=plt.cm.Blues)
    
    # put the major ticks at the middle of each cell
    ax.set_xticks(np.arange(data.shape[0])+0.5, minor=False)
    ax.set_yticks(np.arange(data.shape[1])+0.5, minor=False)

    # want a more natural, table-like display
    ax.invert_yaxis()
    ax.xaxis.set_label_position(&#39;top&#39;) # &lt;-- This doesn&#39;t work!
    
    ax.set_xticklabels(row_labels, minor=False)
    ax.set_yticklabels(column_labels, minor=False)
    plt.show()

However, calling [matplotlib&#39;s set_label_position][2] (as notated above) doesn&#39;t seem to have the desired effect. Here&#39;s my output:

![enter image description here][3]

What am I doing wrong?


  [1]: https://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor
  [2]: http://matplotlib.org/api/axis_api.html#matplotlib.axis.XAxis.set_label_position
  [3]: http://i.stack.imgur.com/rdgE5.png

Moving x-axis to the top of a plot in matplotlib

I need to convert date **string** &quot;2013-1-25&quot; to **string** &quot;1/25/13&quot; in python.
I looked at the `datetime.strptime` but still can&#39;t find a way for this.



How to convert a date string to different format

I need to convert date string "2013-1-25" to string "1/25/13" in python.
I looked at the <code>datetime.strptime</code> but still can't find a way for this.

I’m still relatively new to Flask, and a bit of a web noob in general, but I’ve had some good results so far. Right now I’ve got a form in which users enter a query, which is given to a function that can take anywhere between 5 and 30 seconds to return a result (looking up data with the Freebase API). 

The problem is that I can’t let the user know that their query is loading during this time, as the results page only loads once the function finishes its work. Is there a way I can display a loading message while that&#39;s going on? I found some Javascript that could display a loading message while page elements are still loading, but my waiting period happens before ‘render_template’.

I knocked together some example code, just to demonstrate my situation:

Python:

    from flask import Flask
    from flask import request
    from flask import render_template
    import time
    
    app = Flask(__name__)
    
    def long_load(typeback):
    	time.sleep(5) #just simulating the waiting period
    	return &quot;You typed: %s&quot; % typeback
    
    @app.route(&#39;/&#39;)
    def home():
    	return render_template(&quot;index.html&quot;)
    
    @app.route(&#39;/&#39;, methods=[&#39;POST&#39;])
    def form(display=None):
    	query = request.form[&#39;anything&#39;]
    	outcome = long_load(query)
    	return render_template(&quot;done.html&quot;, display=outcome)
    
    if __name__ == &#39;__main__&#39;:
    	#app.debug = True
    	app.run()
Excerpt from index.html:
 

    &lt;body&gt;
    	&lt;h3&gt;Type anything:&lt;/h3&gt;
    	&lt;p&gt;
    	&lt;form action=&quot;.&quot; method=&quot;POST&quot;&gt;
            &lt;input type=&quot;text&quot; name=&quot;anything&quot; placeholder=&quot;Type anything here&quot;&gt;
            &lt;input type=&quot;submit&quot; name=&quot;anything_submit&quot; value=&quot;Submit&quot;&gt;
        &lt;/form&gt;
        &lt;/p&gt;	
    &lt;/body&gt;

Excerpt from done.html:

    &lt;body&gt;
    	&lt;h3&gt;Results:&lt;/h3&gt;
    	&lt;p&gt;
    		{{ display }}
    	&lt;/p&gt;
    &lt;/body&gt;
Any help would be greatly appreciated, I hope this example helps.

Display a ‘loading’ message while a time consuming function is executed in Flask

The [docs][1] show how to apply multiple functions on a groupby object at a time using a dict with the output column names as the keys:

    In [563]: grouped[&#39;D&#39;].agg({&#39;result1&#39; : np.sum,
       .....:                   &#39;result2&#39; : np.mean})
       .....:
    Out[563]: 
          result2   result1
    A                      
    bar -0.579846 -1.739537
    foo -0.280588 -1.402938

However, this only works on a Series groupby object. And when a dict is similarly passed to a groupby DataFrame, it expects the keys to be the column names that the function will be applied to.

What I want to do is apply multiple functions to several columns (but certain columns will be operated on multiple times). Also, *some functions will depend on other columns in the groupby object* (like sumif functions). My current solution is to go column by column, and doing something like the code above, using lambdas for functions that depend on other rows. But this is taking a long time, (I think it takes a long time to iterate through a groupby object). I&#39;ll have to change it so that I iterate through the whole groupby object in a single run, but I&#39;m wondering if there&#39;s a built in way in pandas to do this somewhat cleanly.

For example, I&#39;ve tried something like 

    grouped.agg({&#39;C_sum&#39; : lambda x: x[&#39;C&#39;].sum(),
                 &#39;C_std&#39;: lambda x: x[&#39;C&#39;].std(),
                 &#39;D_sum&#39; : lambda x: x[&#39;D&#39;].sum()},
                 &#39;D_sumifC3&#39;: lambda x: x[&#39;D&#39;][x[&#39;C&#39;] == 3].sum(), ...)

but as expected I get a KeyError (since the keys have to be a column if `agg` is called from a DataFrame).

Is there any built in way to do what I&#39;d like to do, or a possibility that this functionality may be added, or will I just need to iterate through the groupby manually?


  [1]: http://pandas.pydata.org/pandas-docs/dev/groupby.html#applying-multiple-functions-at-once

Apply multiple functions to multiple groupby columns

I tried searching internet but could not find the meaning of hashable.

When they say objects are `hashable` or `hashable objects` what does it mean?

What does &quot;hashable&quot; mean in Python?

I am reading a text file with floating point numbers, all with either 1 or 2 decimal points. I am using `float()` to convert a line into a float, and raising a `ValueError` if that fails. I am storing all floats in a list. When printing it out, I&#39;d like to print it out as a 2 decimal places floating point.

Assume I have a text file with the numbers -3,65, 9,17, 1. I read each one, and once I convert them to float and append them to a list. Now in Python 2, calling `float(-3.65)` returns `-3.65`. In Python 3 however, `float(-3.65) returns `-3.6499999999999999` which loses its precision.

I want to print the list of floats, `[-3.6499999999999999, 9.1699999999999999, 1.0]` with 2 decimal points only. Doing something along the lines of `&#39;%.1f&#39; % round(n, 1)` would return a string. How can I return a list of all two decimal points of floats, and not strings? So far, I rounded it using `[round(num, 2) for num in list]` but would need to set the decimal points / precision instead of `round()`.


Python 3 Float Decimal Points/Precision

How are variables and memory managed in Python? Does it have a stack and a heap and what algorithm is used to manage memory? Given this knowledge are there any recommendations on memory management for large number/data crunching?

Content Type	Original Author	Original Content on Stackoverflow
Question	Chamith Malinda	View Question on Stackoverflow
Solution 1 - Python	eumiro	View Answer on Stackoverflow
Solution 2 - Python	Alexander	View Answer on Stackoverflow

How to convert a date string to different format

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

Moving x-axis to the top of a plot in matplotlib

Extracting numbers from vectors of strings

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