Use [isin](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.Series.isin.html#pandas.Series.isin)

    df_new[df_new[&#39;l_ext&#39;].isin([31, 22, 30, 25, 64])]

You can use [`pd.DataFrame.query`][1]:

    select_values = [31, 22, 30, 25, 64]
    df_cut = df_new.query(&#39;l_ext in @select_values&#39;)

In the background, this uses the top-level [`pd.eval`][2] function.


  [1]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html
  [2]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.eval.html

I&#39;m new to Android. I&#39;ve spent two hours already for searching. Whatever i try softkeyboard is never shown for my `EditText`. I create it simply:

    EditText editText = (EditText)findViewById(R.id.editText);

I tried:
     
     editText.requestFocus();//i tried without this line too
     InputMethodManager imm = (InputMethodManager)getSystemService(Context.INPUT_METHOD_SERVICE);
     imm.showSoftInput(editText, InputMethodManager.SHOW_IMPLICIT);

and:

    editText.setOnFocusChangeListener(new OnFocusChangeListener() {
         
        	 @Override
             public void onFocusChange(View v, boolean hasFocus) 
        	 {    
                	 
                         InputMethodManager imm = (InputMethodManager)getSystemService(Context.INPUT_METHOD_SERVICE);
                         imm.showSoftInput(editText, InputMethodManager.SHOW_IMPLICIT);
                  
             }
         });

i also tried:

    getWindow().setSoftInputMode(LayoutParams.SOFT_INPUT_STATE_VISIBLE);

i tried putting this line into `AndroidManifest.xml` file:

     android:windowSoftInputMode=&quot;stateVisible|adjustResize&quot;

but all in vain. It just never shows. What am i missing?

Android softkeyboard never shows up in emulator

I have already used glyphicons in bootstrap 2.3 but now I have upgraded to bootstrap 3.0. Now, I am unable to use the icon property.

In bootstrap 2.3, below tag is working

    &lt;i class=&quot;icon-search&quot;&gt;&lt;/i&gt;

In bootstrap 3.0, it&#39;s not working.

How to use glyphicons in bootstrap 3.0

Looks ugly:

    df_cut = df_new[
                 (
                 (df_new[&#39;l_ext&#39;]==31) |
                 (df_new[&#39;l_ext&#39;]==22) |
                 (df_new[&#39;l_ext&#39;]==30) |
                 (df_new[&#39;l_ext&#39;]==25) |
                 (df_new[&#39;l_ext&#39;]==64)
                 )
                ]

Does not work:

    df_cut = df_new[(df_new[&#39;l_ext&#39;] in [31, 22, 30, 25, 64])]

Is there an elegant and working solution of the above &quot;problem&quot;?

How to check if a value is in the list in selection from pandas data frame?

Looks ugly:
<pre><code class="hljs language-css">df_cut = df_new[ ( (df_new['l_ext']==31) |
 (df_new['l_ext']==22) |
 (df_new['l_ext']==30) |
 (df_new['l_ext']==25) |
 (df_new['l_ext']==64)
 )
 ]
</code></pre>
Does not work:
<pre><code class="hljs language-css">df_cut = df_new[(df_new['l_ext'] in [31, 22, 30, 25, 64])]
</code></pre>
Is there an elegant and working solution of the above "problem"?

The problem I am having is when I try to load the **[pickled][1]** object. I have tried using both `pickle.loads` and `pickle.load` Here are the results:

`pickle.loads`:
&gt; TypeError: &#39;str&#39; does not support the buffer interface

`pickle.load`:
&gt; TypeError: file must have &#39;read&#39; and &#39;readline&#39; attributes

Can someone please tell me what I am doing wrong in this process?

    elif str(parser) == &quot;SwissWithdrawn_Parser&quot;:
        # swissprot name changes
        print(&quot;Gathering SwissProt update info...&quot;)
        cache_hits = 0
        cache_misses = 0
        files = set()

        for f in os.listdir(&quot;out/cache/&quot;):
            if os.path.isfile(&quot;out/cache/&quot; + f):
                files.add(f)

        for name in sp_lost_names:

            cached = False
            url = (
                &quot;http://www.uniprot.org/uniprot/?query=mnemonic%3a&quot;
                + name
                + &quot;+active%3ayes&amp;format=tab&amp;columns=entry%20name&quot;
            )
            hashed_url = str(hash(url))

            ################### For Testing Only - use cache ##################
            if hashed_url in files:
                cached = True
                cache_hits += 1
                content = pickle.loads(&quot;out/cache/&quot; + hashed_url)  # &lt;-- problematic line
            else:
                cache_misses += 1
                content = urllib.request.urlopen(url)

            # get the contents returned from the HTTPResponse object
            content_list = [x.decode().strip() for x in content.readlines()]
            if not cached:
                with open(&quot;out/cache/&quot; + hashed_url, &quot;wb&quot;) as fp:
                    pickle.dump(content_list, fp)
            ####################################################################

            # no replacement
            if len(content_list) is 0:
                change_log[&quot;swiss-names&quot;] = {name: &quot;withdrawn&quot;}
            # get the new name
            else:
                new_name = content_list[1]
                change_log[&quot;swiss-names&quot;] = {name: new_name}



  [1]: http://docs.python.org/3/library/pickle.html

Cannot load pickled object

I have a python script parse.py, which in the script open a file, say file1, and then do something maybe print out the total number of characters. 

    filename = &#39;file1&#39;
    f = open(filename, &#39;r&#39;)
    content = f.read()
    print filename, len(content)

Right now, I am using stdout to direct the result to my output file - output
    
    python parse.py &gt;&gt; output

However, I don&#39;t want to do this file by file manually, is there a way to take care of every single file automatically? Like
    
    ls | awk &#39;{print}&#39; | python parse.py &gt;&gt; output 

Then the problem is how could I read the file name from standardin? 
or there are already some built-in functions to do the ls and those kind of work easily?

Thanks!


How to open every file in a folder

I am totally new to python and I am trying to implement quicksort in it.
Could someone please help me complete my code?

I do not know how to concatenate the three arrays and printing them. 

    def sort(array=[12,4,5,6,7,3,1,15]):
        less = []
        equal = []
        greater = []
    
        if len(array) &gt; 1:
            pivot = array[0]
            for x in array:
                if x &lt; pivot:
                    less.append(x)
                if x == pivot:
                    equal.append(x)
                if x &gt; pivot:
                    greater.append(x)
                sort(less)
                sort(pivot)
                sort(greater)



Quicksort with Python

How would I create a list with values between two values I put in? 
For example, the following list is generated for values from 11 to 16:

    list = [11, 12, 13, 14, 15, 16]

Python - Create list with numbers between 2 values?

I got this error in **Google App Engine&#39;s Python** have used Google Translate API,
But I don&#39;t know how to fix,

    &lt;module&gt;
    from apiclient.discovery import build
    ImportError: No module named apiclient.discovery

I&#39;ll try to **set environment which indicates to Google App Engine SDK**,
And upload to Google Apps Engine again, **always get the error**,

&gt; Error: Server Error
&gt;
The server encountered an error and could not complete your request.
If the problem persists, please report your problem and mention this error message and the query that caused it.

Please tell me how to fix,

Thanks

**UPDATE : Fixed**
Follow Nijjin&#39;s help,
I fixed problems by adding the following folders,

`apiclient, gflags, httplib2, oauth2client, uritemplate`

*If you still got problem, please consider below Answer of this page to get more info. ex. : Varum answer, etc ...*



Why is my Python App Engine app using the Translate API getting an error of ImportError: No module named apiclient.discovery?

I have the following json file:

    {
        &quot;FOO&quot;: {
            &quot;name&quot;: &quot;Donald&quot;,
            &quot;location&quot;: &quot;Stockholm&quot;
        },
        &quot;BAR&quot;: {
            &quot;name&quot;: &quot;Walt&quot;,
            &quot;location&quot;: &quot;Stockholm&quot;
        },
        &quot;BAZ&quot;: {
            &quot;name&quot;: &quot;Jack&quot;,
            &quot;location&quot;: &quot;Whereever&quot;
        }
    }

I am using jq and want to get the &quot;name&quot; elements of the objects where &#39;location&#39; is &#39;Stockholm&#39;. 

I know I can get all names by

    cat json | jq .[] | jq .&quot;name&quot;
    &quot;Jack&quot;
    &quot;Walt&quot;
    &quot;Donald&quot;

But I can&#39;t figure out how to print only certain objects, given the value of a sub key (here: `&quot;location&quot; : &quot;Stockholm&quot;`). 

Select objects based on value of variable in object using jq

I have a value in a table that was changed unexpectedly. The column in question is `CreatedDate`: this is set when my item is created, but it&#39;s being changed by a stored procedure.

Could I write some type of `SELECT` statement to get all the procedure names that reference this column from my table?

Find all stored procedures that reference a specific column in some table

I want to convert selected values into a comma separated string in MySQL.

My initial code is as follows:

    SELECT id
    FROM table_level
    WHERE parent_id = 4;

Which produces:
    
    &#39;5&#39;
    &#39;6&#39;
    &#39;9&#39;
    &#39;10&#39;
    &#39;12&#39;
    &#39;14&#39;
    &#39;15&#39;
    &#39;17&#39;
    &#39;18&#39;
    &#39;779&#39;

My desired output would look like this:
    
    &quot;5,6,9,10,12,14,15,17,18,779&quot;


Comma separated string of selected values in MySQL

I am using Sybase and I am doing a select which returns me a column called &quot;iftype&quot;, but its type is int and I need to convert into varchar. When I try to do the select without the convert function I get this error:

&gt;Error code 257, SQL state 37000: Implicit conversion from datatype &#39;VARCHAR&#39; to &#39;INT&#39; is not allowed.  Use the CONVERT function to run this query.

I dont know how to implement the function `CONVERT`. Can anyone help me, please ?

Convert INT to VARCHAR SQL

I was writing some tasks yesterday and it struck me that I don&#39;t really know **THE PROPER and ACCEPTED way** of checking if row exists in table when I&#39;m using  PL/SQL. 

For examples sake let&#39;s use table: 

    PERSON (ID, Name); 

Obviously I **can&#39;t do** (unless there&#39;s some secret method) something like:

    BEGIN 
      IF EXISTS SELECT id FROM person WHERE ID = 10; 
        -- do things when exists
      ELSE
        -- do things when doesn&#39;t exist
      END IF;
    END;
So my standard way of solving it was:

    DECLARE
      tmp NUMBER;
    BEGIN 
      SELECT id INTO tmp FROM person WHERE id = 10; 
      --do things when record exists
    EXCEPTION
      WHEN no_data_found THEN
      --do things when record doesn&#39;t exist
    END; 

However I don&#39;t know if it&#39;s accepted way of doing it, or if there&#39;s any better way of checking, I would really apprieciate if someone could share their wisdom with me. 

Proper way of checking if row exists in table in PL/SQL block

Lets start with three arrays of `dtype=np.double`. Timings are performed on a intel CPU using numpy 1.7.1 compiled with `icc` and linked to intel&#39;s `mkl`. A AMD cpu with numpy 1.6.1 compiled with `gcc` without `mkl` was also used to verify the timings. Please note the timings scale nearly linearly with system size and are not due to the small overhead incurred in the numpy functions `if` statements these difference will show up in microseconds not milliseconds:

    arr_1D=np.arange(500,dtype=np.double)
    large_arr_1D=np.arange(100000,dtype=np.double)
    arr_2D=np.arange(500**2,dtype=np.double).reshape(500,500)
    arr_3D=np.arange(500**3,dtype=np.double).reshape(500,500,500)

First lets look at the `np.sum` function:

    np.all(np.sum(arr_3D)==np.einsum(&#39;ijk-&gt;&#39;,arr_3D))
    True
    
    %timeit np.sum(arr_3D)
    10 loops, best of 3: 142 ms per loop

    %timeit np.einsum(&#39;ijk-&gt;&#39;, arr_3D)
    10 loops, best of 3: 70.2 ms per loop

Powers:

    np.allclose(arr_3D*arr_3D*arr_3D,np.einsum(&#39;ijk,ijk,ijk-&gt;ijk&#39;,arr_3D,arr_3D,arr_3D))
    True

    %timeit arr_3D*arr_3D*arr_3D
    1 loops, best of 3: 1.32 s per loop
    
    %timeit np.einsum(&#39;ijk,ijk,ijk-&gt;ijk&#39;, arr_3D, arr_3D, arr_3D)
    1 loops, best of 3: 694 ms per loop

Outer product:

    np.all(np.outer(arr_1D,arr_1D)==np.einsum(&#39;i,k-&gt;ik&#39;,arr_1D,arr_1D))
    True
    
    %timeit np.outer(arr_1D, arr_1D)
    1000 loops, best of 3: 411 us per loop
    
    %timeit np.einsum(&#39;i,k-&gt;ik&#39;, arr_1D, arr_1D)
    1000 loops, best of 3: 245 us per loop


All of the above are twice as fast with `np.einsum`. These should be apples to apples comparisons as everything is specifically of `dtype=np.double`. I would expect the speed up in an operation like this:

    np.allclose(np.sum(arr_2D*arr_3D),np.einsum(&#39;ij,oij-&gt;&#39;,arr_2D,arr_3D))
    True

    %timeit np.sum(arr_2D*arr_3D)
    1 loops, best of 3: 813 ms per loop
    
    %timeit np.einsum(&#39;ij,oij-&gt;&#39;, arr_2D, arr_3D)
    10 loops, best of 3: 85.1 ms per loop
    

Einsum seems to be at least twice as fast for `np.inner`, `np.outer`, `np.kron`, and `np.sum` regardless of `axes` selection. The primary exception being `np.dot`  as it calls DGEMM from a BLAS library. So why is `np.einsum` faster that other numpy functions that are equivalent?

The DGEMM case for completeness:

    np.allclose(np.dot(arr_2D,arr_2D),np.einsum(&#39;ij,jk&#39;,arr_2D,arr_2D))
    True
    
    %timeit np.einsum(&#39;ij,jk&#39;,arr_2D,arr_2D)
    10 loops, best of 3: 56.1 ms per loop
    
    %timeit np.dot(arr_2D,arr_2D)
    100 loops, best of 3: 5.17 ms per loop


----------

The leading theory is from @sebergs comment that `np.einsum` can make use of [SSE2][1], but numpy&#39;s ufuncs will not until numpy 1.8 (see the [change log][2]). I believe this is the correct answer, but have *not* been able to confirm it. Some limited proof can be found by changing the dtype of input array and observing speed difference and the fact that not everyone observes the same trends in timings.


  [1]: http://en.wikipedia.org/wiki/SSE2
  [2]: https://github.com/numpy/numpy/blob/master/doc/release/1.8.0-notes.rst

Why is numpy&#39;s einsum faster than numpy&#39;s built in functions?

We initialize a numpy array with zeros as bellow:

    np.zeros((N,N+1))

But how do we check whether all elements in a given n*n numpy array matrix is zero.  
The method just need to return a True if all the values are indeed zero.


Test if numpy array contains only zeros

I have the following data set.  I would like to use Python or Gnuplot to plot the data.  The tuples are of the form `(x, y)`.  The Y-axis should be a log axis, that is, `log(y)`.  A scatter plot or line plot would be ideal.

How can this be done?

     [(0, 6.0705199999997801e-08), (1, 2.1015700100300739e-08), 
     (2, 7.6280656623374823e-09), (3, 5.7348209304555086e-09), 
     (4, 3.6812203579604238e-09), (5, 4.1572516753310418e-09)]



How do I plot list of tuples in Python?

Given two ndarrays

    old_set = [[0, 1], [4, 5]]
    new_set = [[2, 7], [0, 1]]

I&#39;m looking to get the mean of the respective values between the two arrays so that the data ends up something like:

    end_data = [[1, 4], [2, 3]]

basically it would apply something like 

    for i in len(old_set):
        end_data[i] = (old_set[i]+new_set[i])/2

But I&#39;m unsure what syntax to use.. Thanks for the help in advance!


Average values in two Numpy arrays

I&#39;m trying to multiply each of the terms in a 2D array by the corresponding terms in a 1D array. This is very easy if I want to multiply every column by the 1D array, as shown in the [numpy.multiply](http://docs.scipy.org/doc/numpy/reference/generated/numpy.multiply.html) function. But I want to do the opposite, multiply each term in the row.
In other words I want to multiply:


    [1,2,3]   [0]
    [4,5,6] * [1]
    [7,8,9]   [2]


and get


    [0,0,0]
    [4,5,6]
    [14,16,18]


but instead I get


    [0,2,6]
    [0,5,12]
    [0,8,18]

Does anyone know if there&#39;s an elegant way to do that with numpy?
Thanks a lot,
Alex

Multiplying across in a numpy array

Lets say I have a MultiIndex Series `s`:

    &gt;&gt;&gt; s
         values
    a b
    1 2  0.1 
    3 6  0.3
    4 4  0.7

and I want to apply a function which uses the index of the row:

    def f(x):
       # conditions or computations using the indexes
       if x.index[0] and ...: 
       other = sum(x.index) + ...
       return something

How can I do `s.apply(f)` for such a function? What is the recommended way to make this kind of operations? I expect to obtain a new Series with the values resulting from this function applied on each row and the same MultiIndex.

Access index in pandas.Series.apply

I know this is a very basic question but for some reason I can&#39;t find an answer. How can I get the index of certain element of a Series in python pandas? (first occurrence would suffice)

I.e., I&#39;d like something like:

    import pandas as pd
    myseries = pd.Series([1,4,0,7,5], index=[0,1,2,3,4])
    print myseries.find(7) # should output 3

Certainly, it is possible to define such a method with a loop:

    def find(s, el):
        for i in s.index:
            if s[i] == el: 
                return i
        return None
   
    print find(myseries, 7)

but I assume there should be a better way. Is there?

Find element&#39;s index in pandas Series

I&#39;m probably using poor search terms when trying to find this answer. Right now, before indexing a DataFrame, I&#39;m getting a list of values in a column this way...

     list = list(df[&#39;column&#39;]) 

...then I&#39;ll `set_index` on the column. This seems like a wasted step. When trying the above on an index, I get a key error.

How can I grab the values in an index (both single and multi) and put them in a list or a list of tuples?

Get row-index values of Pandas DataFrame as list?

I think I misunderstand the intention of read_csv. If I have a file &#39;j&#39; like

    # notes
    a,b,c
    # more notes
    1,2,3

How can I pandas.read_csv this file, skipping any &#39;#&#39; commented lines? I see in the help &#39;comment&#39; of lines is not supported but it indicates an empty line should be returned. I see an error

    df = pandas.read_csv(&#39;j&#39;, comment=&#39;#&#39;)

CParserError: Error tokenizing data. C error: Expected 1 fields in line 2, saw 3

I&#39;m currently on 

    In [15]: pandas.__version__
    Out[15]: &#39;0.12.0rc1&#39;

On version&#39;0.12.0-199-g4c8ad82&#39;:

    In [43]: df = pandas.read_csv(&#39;j&#39;, comment=&#39;#&#39;, header=None)

CParserError: Error tokenizing data. C error: Expected 1 fields in line 2, saw 3

pandas.read_csv: how to skip comment lines

I have a DataFrame with many missing values in columns which I wish to groupby:

    import pandas as pd
    import numpy as np
    df = pd.DataFrame({&#39;a&#39;: [&#39;1&#39;, &#39;2&#39;, &#39;3&#39;], &#39;b&#39;: [&#39;4&#39;, np.NaN, &#39;6&#39;]})
    
    In [4]: df.groupby(&#39;b&#39;).groups
    Out[4]: {&#39;4&#39;: [0], &#39;6&#39;: [2]}

see that Pandas has dropped the rows with NaN target values. (I want to include these rows!)

*Since I need many such operations (many cols have missing values), and use more complicated functions than just medians (typically random forests), I want to avoid writing too complicated pieces of code.*

Any suggestions? Should I write a function for this or is there a simple solution?

pandas GroupBy columns with NaN (missing) values

I have a data.frame as shown below:

    task    measure
    right   m1
    left    m2
    up      m3
    down    m4
    front   m5
    back    m6
    .
    .
    .

The task column takes only six different values, which are treated as factors, and are ordered by R as: &quot;back&quot;, &quot;down&quot;, &quot;front&quot;, &quot;left&quot;, &quot;right&quot;, &quot;up&quot;.

How ever, I need them ordered as: &quot;up&quot;, &quot;down&quot;, &quot;left&quot;, &quot;right&quot;, &quot;front&quot;, &quot;back&quot;. So that when I use this data in ggplot, the related tasks (such as &quot;up&quot; and &quot;down&quot;) are plotted next to each other.

How can change the ordering of the levels of the factor &quot;task&quot;?

Re-ordering factor levels in data frame

I want to use the apply function on a dataframe, but only apply the function to the last 5 columns.

    B&lt;- by(wifi,(wifi$Room),FUN=function(y){apply(y, 2, A)})

This applies A to all the columns of y

    B&lt;- by(wifi,(wifi$Room),FUN=function(y){apply(y[4:9], 2, A)})

This applies A only to columns 4-9 of y, but the total return of B strips off the first 3 columns... I still want those, I just don&#39;t want A applied to them.

    wifi[,1:3]+B 

also does not do what I expected/wanted.

R Apply() function on specific dataframe columns

I have a data frame with 10 columns, collecting actions of &quot;users&quot;, where one of the columns contains an ID (not unique, identifying user)(column 10). the length of the data frame is about 750000 rows. I am trying to extract individual data frames (so getting a list or vector of data frames) split by the column containing the &quot;user&quot; identifier, to isolate the actions of a single actor.

    ID | Data1 | Data2 | ... | UserID
    1  | aaa   | bbb   | ... | u_001
    2  | aab   | bb2   | ... | u_001
    3  | aac   | bb3   | ... | u_001
    4  | aad   | bb4   | ... | u_002

resulting into 

    list(
    ID | Data1 | Data2 | ... | UserID
    1  | aaa   | bbb   | ... | u_001
    2  | aab   | bb2   | ... | u_001
    3  | aac   | bb3   | ... | u_001
    ,
    4  | aad   | bb4   | ... | u_002
    ...)

The following works very well for me on a small sample (1000 rows):

    paths = by(smallsampleMat, smallsampleMat[,&quot;userID&quot;], function(x) x)

and then accessing the element I want by paths[1] for instance.

When applying on the original large data frame or even a matrix representation, this chokes my machine ( 4GB RAM, MacOSX 10.6, R 2.15) and never completes (I know that a newer R version exists, but I believe this is not the main problem). 

It seems that split is more performant and after a long time completes, but I do not know ( inferior R knowledge) how to piece the resulting list of vectors into a vector of matrices.

    path = split(smallsampleMat, smallsampleMat[,10]) 

I have considered also using `big.matrix` etc, but without much success that would speed up the process.


Split a large dataframe into a list of data frames based on common value in column

How can I print a pandas dataframe as a nice text-based table, like the following?

    +------------+---------+-------------+
    | column_one | col_two |   column_3  |
    +------------+---------+-------------+
    |          0 |  0.0001 | ABCD        |
    |          1 |  1e-005 | ABCD        |
    |          2 |  1e-006 | long string |
    |          3 |  1e-007 | ABCD        |
    +------------+---------+-------------+


Pretty Printing a pandas dataframe

I have a dataframe with two columns and intend to convert it to a dictionary. The first column will be the key and the second will be the value.

Dataframe:

        id    value
    0    0     10.2
    1    1      5.7
    2    2      7.4

How can I do this?

Content Type	Original Author	Original Content on Stackoverflow
Question	Roman	View Question on Stackoverflow
Solution 1 - Python	waitingkuo	View Answer on Stackoverflow
Solution 2 - Python	jpp	View Answer on Stackoverflow

How to check if a value is in the list in selection from pandas data frame?

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Android softkeyboard never shows up in emulator

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