First of all, you&#39;re better off accessing both the row and column indices from the `.loc`:

    x.loc[x[&#39;A&#39;] == 2, &#39;B&#39;]

Second, you can always get at the underlying numpy matrix using `.values` on a series or dataframe:

    In : x.loc[x[&#39;A&#39;] == 2, &#39;B&#39;].values[0]
    Out: 6

Finally, if you&#39;re not interested in the original question&#39;s &quot;conditional indexing&quot;, there are also specific accessors designed to get a single scalar value from a DataFrame: [`dataframe.at[index, column]`][1] or [`dataframe.iat[i, j]`][2] (these are similar to `.loc[]` and `.iloc[]` but designed for quick access to a single value).


  [1]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.at.html#pandas.DataFrame.at
  [2]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iat.html

elaborating on @ShineZhang comment:

`x.set_index(&#39;A&#39;).at[2, &#39;B&#39;]`

6

`pd.__version__`

u&#39;0.22.0&#39;


I am working on an Android App, in which the server sends an OTP and the user needs to enter this OTP in the App, to SignUp for my App. What I want is, that my App should be able to automatically read the OTP sent by the server. How can I achieve this? Any help or guidance in this regard would be highly appreciated.

OTP (token) should be automatically read from the message

We have models for converting words to vectors (for example the word2vec model). Do similar models exist which convert sentences/documents into vectors, using perhaps the vectors learnt for the individual words?

How can a sentence or a document be converted to a vector?

I have the dataframe shown below. I need to get the scalar value of column B, dependent on the value of A (which is a variable in my script). I&#39;m trying the loc() function but it returns a Series instead of a scalar value. How do I get the scalar value()?


    &gt;&gt;&gt; x = pd.DataFrame({&#39;A&#39; : [0,1,2], &#39;B&#39; : [4,5,6]})
    &gt;&gt;&gt; x
       A  B
    0  0  4
    1  1  5
    2  2  6

    &gt;&gt;&gt; x.loc[x[&#39;A&#39;] == 2][&#39;B&#39;]
    2    6
    Name: B, dtype: int64

    &gt;&gt;&gt; type(x.loc[x[&#39;A&#39;] == 2][&#39;B&#39;])
    &lt;class &#39;pandas.core.series.Series&#39;&gt;



How to get scalar value on a cell using conditional indexing

I have the dataframe shown below. I need to get the scalar value of column B, dependent on the value of A (which is a variable in my script). I'm trying the loc() function but it returns a Series instead of a scalar value. How do I get the scalar value()?
<pre><code class="hljs language-python-repl">>>> x = pd.DataFrame({'A' : [0,1,2], 'B' : [4,5,6]})
>>> x
 A B
0 0 4
1 1 5
2 2 6

>>> x.loc[x['A'] == 2]['B']
2 6
Name: B, dtype: int64

>>> type(x.loc[x['A'] == 2]['B'])
&#x3C;class 'pandas.core.series.Series'>
</code></pre>

I see that I&#39;m not the first one to ask the question but there was no clear answer to this:  
  
How to use pdb with docker-composer in Python development?  
  
When you ask uncle Google about `django docker` you get awesome docker-composer examples and tutorials and I have an environment working - I can run `docker-compose up` and I have a neat developer environment **but the PDB is not working** (which is very sad).  

I can settle with running `docker-compose run my-awesome-app python app.py 0.0.0.0:8000` but then I can access my application over http://127.0.0.1:8000 from the host (I can with `docker-compose up`) and it seems that each time I use `run` new containers are made like: `dir_app_13` and `dir_db_4` which I don&#39;t desire at all.  

People of good will please aid me.

PS  
I&#39;m using pdb++ for that example and a basic docker-compose.yml from [this django example](https://docs.docker.com/compose/django/). Also I experimented but nothing seems to help me. And I&#39;m using docker-composer [1.3.0rc3](https://github.com/docker/compose/tree/1.3.0rc3) as it has Dockerfile pointing support.

Docker-compose and pdb

I&#39;ve recently switched to IPython Notebook 3 (3.1.0-cbccb68 to be exact), the Anaconda version. Previously when I typed a function and opened a parenthesis like this:

    time.sleep()

and if the cursor was between the parentheses then I would get a contextual overlay menu that displayed the function arguments. Now I don&#39;t see it, and although I&#39;ve searched, I can&#39;t find out how I can turn on this functionality. 

How can I see function arguments in IPython Notebook Server 3?

When using panda&#39;s `resample` function on a DataFrame in order to convert tick data to OHLCV, a resampling error is encountered.

How should we solve the error?

	data = pd.read_csv(&#39;tickdata.csv&#39;, header=None, names=[&#39;Timestamp&#39;,&#39;Price&#39;,&#39;Volume&#39;]).set_index(&#39;Timestamp&#39;)
	data.head()

![enter image description here][1]

	# Resample data into 30min bins
	ticks = data.ix[:, [&#39;Price&#39;, &#39;Volume&#39;]]
	bars = ticks.Price.resample(&#39;30min&#39;, how=&#39;ohlc&#39;)
	volumes = ticks.Volume.resample(&#39;30min&#39;, how=&#39;sum&#39;)

This gives the error:

	TypeError: Only valid with DatetimeIndex or PeriodIndex


  [1]: http://i.stack.imgur.com/FvhPN.png

Pandas Resampling error: Only valid with DatetimeIndex or PeriodIndex

Running flexget Python script in Ubuntu, I get an error:

    $ flexget series forget &quot;Orange is the new black&quot; s03e01
    Traceback (most recent call last):
    File &quot;/usr/local/bin/flexget&quot;, line 7, in &lt;module&gt;
    from flexget import main
    File &quot;/usr/local/lib/python2.7/dist-packages/flexget/__init__.py&quot;, line 11, in &lt;module&gt;
    from flexget.manager import Manager
    File &quot;/usr/local/lib/python2.7/dist-packages/flexget/manager.py&quot;, line 21, in &lt;module&gt;
    from sqlalchemy.ext.declarative import declarative_base
    File &quot;/usr/local/lib/python2.7/dist-packages/sqlalchemy/ext/declarative/__init__.py&quot;, line 8, in &lt;module&gt;
    from .api import declarative_base, synonym_for, comparable_using, \
    File &quot;/usr/local/lib/python2.7/dist-packages/sqlalchemy/ext/declarative/api.py&quot;, line 11, in &lt;module&gt;
    from ...orm import synonym as _orm_synonym, \
    File &quot;/usr/local/lib/python2.7/dist-packages/sqlalchemy/orm/__init__.py&quot;, line 17, in &lt;module&gt;
    from .mapper import (
    File &quot;/usr/local/lib/python2.7/dist-packages/sqlalchemy/orm/mapper.py&quot;, line 27, in &lt;module&gt;
    from . import properties
    ValueError: bad marshal data (unknown type code)


How to fix Python ValueError:bad marshal data?

While I am aware of the option of [installing Pip from source](https://pip.pypa.io/en/latest/installing.html), I&#39;m trying to avoid going down that path so that updates to Pip will be managed by Cygwin&#39;s package management.

I&#39;ve [recently learned](https://stackoverflow.com/a/12476379/2489598) that the latest versions of Python include Pip.  However, even though I have recently installed the latest versions of Python from the Cygwin repos, Bash doesn&#39;t recognize a valid Pip install on the system.

    896/4086 MB RAM 0.00 0.00 0.00 1/12 Tue, Jun 16, 2015 ( 3:53:22am CDT) [0 jobs]
    [ethan@firetail: +2] ~ $ python -V
    Python 2.7.10
    892/4086 MB RAM 0.00 0.00 0.00 1/12 Tue, Jun 16, 2015 ( 3:53:27am CDT) [0 jobs]
    [ethan@firetail: +2] ~ $ python3 -V
    Python 3.4.3
    883/4086 MB RAM 0.00 0.00 0.00 1/12 Tue, Jun 16, 2015 ( 3:53:34am CDT) [0 jobs]
    [ethan@firetail: +2] ~ $ pip
    bash: pip: command not found
    878/4086 MB RAM 0.00 0.00 0.00 1/12 Tue, Jun 16, 2015 ( 3:53:41am CDT) [0 jobs]
    [ethan@firetail: +2] ~ $ pip2
    bash: pip2: command not found
    876/4086 MB RAM 0.00 0.00 0.00 1/12 Tue, Jun 16, 2015 ( 3:53:42am CDT) [0 jobs]
    [ethan@firetail: +2] ~ $ pip3
    bash: pip3: command not found

Note that the installed Python 2.7.10 and Python 3.4.3 are both recent enough that they should include Pip.

Is there something that I might have overlooked?  Could there be a new install of Pip that isn&#39;t in the standard binary directories referenced in the $PATH?  If the Cygwin packages of Python do in fact lack an inclusion of Pip, is that something that&#39;s notable enough to warrant a bug report to the Cygwin project?

Installing new versions of Python on Cygwin does not install Pip?

I have a dataframe that has two columns, `user_id` and `item_bought`.
Here `user_id` is the index of the dataframe. I want to group by both `user_id` and `item_bought` and get the item wise count for the user. 

How do I do that?


Group by index + column in pandas

How do I add multiple empty columns to a `DataFrame` from a list?

I can do:
```python
    df[&quot;B&quot;] = None
    df[&quot;C&quot;] = None
    df[&quot;D&quot;] = None
```
But I can&#39;t do:
```python
    df[[&quot;B&quot;, &quot;C&quot;, &quot;D&quot;]] = None
```

&gt; `KeyError: &quot;[&#39;B&#39; &#39;C&#39; &#39;D&#39;] not in index&quot;`


Add multiple empty columns to pandas DataFrame

I would like to see if a particular string exists in a particular column within my dataframe. 

I&#39;m getting the error 

&gt; ValueError: The truth value of a Series is ambiguous. Use a.empty,
&gt; a.bool(), a.item(), a.any() or a.all().

    import pandas as pd
    
    BabyDataSet = [(&#39;Bob&#39;, 968), (&#39;Jessica&#39;, 155), (&#39;Mary&#39;, 77), (&#39;John&#39;, 578), (&#39;Mel&#39;, 973)]
    
    a = pd.DataFrame(data=BabyDataSet, columns=[&#39;Names&#39;, &#39;Births&#39;])
    
    if a[&#39;Names&#39;].str.contains(&#39;Mel&#39;):
        print (&quot;Mel is there&quot;)

Check if string is in a pandas dataframe

I have the following dataframe. 

    df.head(30)

         struct_id  resNum score_type_name  score_value
    0   4294967297       1           omega     0.064840
    1   4294967297       1          fa_dun     2.185618
    2   4294967297       1      fa_dun_dev     0.000027
    3   4294967297       1     fa_dun_semi     2.185591
    4   4294967297       1             ref    -1.191180
    5   4294967297       2            rama    -0.795161
    6   4294967297       2           omega     0.222345
    7   4294967297       2          fa_dun     1.378923
    8   4294967297       2      fa_dun_dev     0.028560
    9   4294967297       2      fa_dun_rot     1.350362
    10  4294967297       2         p_aa_pp    -0.442467
    11  4294967297       2             ref     0.249477
    12  4294967297       3            rama     0.267443
    13  4294967297       3           omega     0.005106
    14  4294967297       3          fa_dun     0.020352
    15  4294967297       3      fa_dun_dev     0.025507
    16  4294967297       3      fa_dun_rot    -0.005156
    17  4294967297       3         p_aa_pp    -0.096847
    18  4294967297       3             ref     0.979644
    19  4294967297       4            rama    -1.403292
    20  4294967297       4           omega     0.212160
    21  4294967297       4          fa_dun     4.218029
    22  4294967297       4      fa_dun_dev     0.003712
    23  4294967297       4     fa_dun_semi     4.214317
    24  4294967297       4         p_aa_pp    -0.462765
    25  4294967297       4             ref    -1.960940
    26  4294967297       5            rama    -0.600053
    27  4294967297       5           omega     0.061867
    28  4294967297       5          fa_dun     3.663050
    29  4294967297       5      fa_dun_dev     0.004953

According to the pivot documentation, I should be able to reshape this on the score_type_name using the pivot function. 

    df.pivot(columns=&#39;score_type_name&#39;,values=&#39;score_value&#39;,index=[&#39;struct_id&#39;,&#39;resNum&#39;])

But, I get the following. 

![enter image description here][2]

However, pivot_table function seems to work:

    pivoted = df.pivot_table(columns=&#39;score_type_name&#39;,
                             values=&#39;score_value&#39;,
                             index=[&#39;struct_id&#39;,&#39;resNum&#39;])

![enter image description here][3]

But it does not lend itself, for me atleast, to further analysis. I want it to just have the struct_id, resNum, and score_type_name as columns instead of stacking the score_type_name on top of the other columns. Additionally, I want the struct_id to be for every row, and not aggregate in a joined row like it does for the table.

So can anyone tell me how I can get a nice Dataframe like I want using pivot? Additionally, from the documentation, I can&#39;t tell why pivot_table works and pivot doesn&#39;t. If I look at the first example of pivot, it looks like exactly what I need.

P.S. 
I did post a question in reference to this problem, but I did such a poor job of demonstrating the output, I deleted it and tried again using ipython notebook. I apologize in advance if you are seeing this twice. 

[Here is the notebook for your full reference][4]

EDIT - My desired results would look like this (made in excel):

    StructId	resNum	pdb_residue_number	chain_id	name3	fa_dun	fa_dun_dev	fa_dun_rot	fa_dun_semi	omega	p_aa_pp	rama	ref
    4294967297	1	99	A	ASN	2.1856	0.0000		2.1856	0.0648			-1.1912
    4294967297	2	100	A	MET	1.3789	0.0286	1.3504		0.2223	-0.4425	-0.7952	0.2495
    4294967297	3	101	A	VAL	0.0204	0.0255	-0.0052		0.0051	-0.0968	0.2674	0.9796
    4294967297	4	102	A	GLU	4.2180	0.0037		4.2143	0.2122	-0.4628	-1.4033	-1.9609
    4294967297	5	103	A	GLN	3.6630	0.0050		3.6581	0.0619	-0.2759	-0.6001	-1.5172
    4294967297	6	104	A	MET	1.5175	0.2206	1.2968		0.0504	-0.3758	-0.7419	0.2495
    4294967297	7	105	A	HIS	3.6987	0.0184		3.6804	0.0547	0.4019	-0.1489	0.3883
    4294967297	8	106	A	THR	0.1048	0.0134	0.0914		0.0003	-0.7963	-0.4033	0.2013
    4294967297	9	107	A	ASP	2.3626	0.0005		2.3620	0.0521	0.1955	-0.3499	-1.6300
    4294967297	10	108	A	ILE	1.8447	0.0270	1.8176		0.0971	0.1676	-0.4071	1.0806
    4294967297	11	109	A	ILE	0.1276	0.0092	0.1183		0.0208	-0.4026	-0.0075	1.0806
    4294967297	12	110	A	SER	0.2921	0.0342	0.2578		0.0342	-0.2426	-1.3930	0.1654
    4294967297	13	111	A	LEU	0.6483	0.0019	0.6464		0.0845	-0.3565	-0.2356	0.7611
    4294967297	14	112	A	TRP	2.5965	0.1507		2.4457	0.5143	-0.1370	-0.5373	1.2341
    4294967297	15	113	A	ASP	2.6448	0.1593			0.0510		-0.5011	

  [1]: http://i.stack.imgur.com/A9fIv.png
  [2]: http://i.stack.imgur.com/YcwZB.png
  [3]: http://i.stack.imgur.com/MBn7K.png
  [4]: http://nbviewer.ipython.org/gist/jwillis0720/073ff14fd8f5630b42eb


Content Type	Original Author	Original Content on Stackoverflow
Question	user4979733	View Question on Stackoverflow
Solution 1 - Python	Noah	View Answer on Stackoverflow
Solution 2 - Python	ihadanny	View Answer on Stackoverflow

How to get scalar value on a cell using conditional indexing

Python Problem Overview

Python Solutions

Solution 1 - Python

Solution 2 - Python

How can a sentence or a document be converted to a vector?

OTP (token) should be automatically read from the message

Attributions