Note **there is no `axis` param for a [`Series.apply`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html) call**, as distinct to a [`DataFrame.apply`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html#pandas.DataFrame.apply) call.


&gt; Series.apply(func, convert_dtype=True, args=(), **kwds)

    func : function
    convert_dtype : boolean, default True
    Try to find better dtype for elementwise function results. If False, leave as dtype=object
    args : tuple
    Positional arguments to pass to function in addition to the value

There is one for a [df](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html#pandas.DataFrame.apply) but it&#39;s unclear how you&#39;re expecting this to work when you&#39;re calling it on a series but you&#39;re expecting it to work on a row?


I am trying to perform a Post to my WebAPI from a c# WPF desktop app.

No matter what I do, I get 

&gt; {&quot;error&quot;:&quot;unsupported_grant_type&quot;}

This is what I&#39;ve tried (and I&#39;ve tried everything I could find):

Also dev web api currently active for testing: http://studiodev.biz/

base http client object:

    
    var client = new HttpClient()
    client.BaseAddress = new Uri(&quot;http://studiodev.biz/&quot;);
    client.DefaultRequestHeaders.Accept.Clear();
    client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue(&quot;application/json&quot;));
    client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue(&quot;text/plain&quot;));
    client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue(&quot;*/*&quot;));

with the following send methods:

    var response = await client.PostAsJsonAsync(&quot;token&quot;, &quot;{&#39;grant_type&#39;=&#39;password&#39;&amp;&#39;username&#39;=&#39;username&#39;&amp;&#39;password&#39;=&#39;password&#39;&quot;);
    var response = await client.PostAsJsonAsync(&quot;token&quot;, &quot;grant_type=password&amp;username=username&amp;password=password&quot;);

After that failed, I did some googling and tried:

    LoginModel data = new LoginModel(username, password);
    string json = JsonConvert.SerializeObject(data);
    await client.PostAsync(&quot;token&quot;, new JsonContent(json));
 same result, so I tried:
                    
    req.Content = new StringContent(json, Encoding.UTF8, &quot;application/x-www-form-urlencoded&quot;);
    await client.SendAsync(req).ContinueWith(respTask =&gt;
    {
     Application.Current.Dispatcher.Invoke(new Action(() =&gt; { label.Content = respTask.Result.ToString(); }));
    });

Note: I can make a successful call with Chrome.

**Update Fiddler Result**

![enter image description here][1]

Could someone please help me make a successful call to the above web api...
Please let me know if I can help clarify.
Thanks!!


  [1]: http://i.stack.imgur.com/qZ9oV.jpg

C# unsupported grant type when calling web api

I have the following entity defined:

    @Entity
    @Table(name = &quot;EmailTemplate&quot;)
    public class EmailTemplate {

Despite the table annotation, I receive `java.sql.SQLException: Invalid object name &#39;email_template&#39;`. How can I prevent an entity class such as EmailTemplate being transformed into email_template table name? 

Edit:

I&#39;m using Spring Boot: start JPA. From my build.gradle file,

    compile(&quot;org.springframework.boot:spring-boot-starter-data-jpa&quot;)

Entity Class name is transformed into SQL table name with underscores

I&#39;m trying to apply a function to all rows of a pandas DataFrame (actually just one column in that DataFrame)

I&#39;m sure this is a syntax error but I&#39;m know sure what I&#39;m doing wrong

    df[&#39;col&#39;].apply(lambda x, y:(x - y).total_seconds(), args=[d1], axis=1)

The `col` column contains a bunch a `datetime.datetime` objects and and `d1` is the earliest of them.  I&#39;m trying to get a column of the total number of seconds for each of the rows

**EDIT** I keep getting the following error

    TypeError: &lt;lambda&gt;() got an unexpected keyword argument &#39;axis&#39;

I don&#39;t understand why `axis` is getting passed to my `lambda` function

**EDIT 2**

I&#39;ve also tried doing

    def diff_dates(d1, d2):
        return (d1-d2).total_seconds()

    df[&#39;col&#39;].apply(diff_dates, args=[d1], axis=1)

And I get the same error

Trouble passing in lambda to apply for pandas DataFrame

I'm trying to apply a function to all rows of a pandas DataFrame (actually just one column in that DataFrame)
I'm sure this is a syntax error but I'm know sure what I'm doing wrong
<pre><code class="hljs language-scss">df['col'].apply(lambda x, y:(x - y).total_seconds(), args=[d1], axis=1)
</code></pre>
The <code>col</code> column contains a bunch a <code>datetime.datetime</code> objects and and <code>d1</code> is the earliest of them. I'm trying to get a column of the total number of seconds for each of the rows
EDIT I keep getting the following error
<pre><code class="hljs language-javascript">TypeError: &#x3C;lambda>() got an unexpected keyword argument 'axis'
</code></pre>
I don't understand why <code>axis</code> is getting passed to my <code>lambda</code> function
EDIT 2
I've also tried doing
<pre><code class="hljs language-python">def diff_dates(d1, d2):
 return (d1-d2).total_seconds()

df['col'].apply(diff_dates, args=[d1], axis=1)
</code></pre>
And I get the same error

Till now, I used to end my Tkinter programs with: `tk.mainloop()`, or nothing would show up! See example:

    from Tkinter import *
    import random
    import time
    
    tk = Tk()
    tk.title = &quot;Game&quot;
    tk.resizable(0,0)
    tk.wm_attributes(&quot;-topmost&quot;, 1)
    
    canvas = Canvas(tk, width=500, height=400, bd=0, highlightthickness=0)
    canvas.pack()
    
    class Ball:
        def __init__(self, canvas, color):
            self.canvas = canvas
            self.id = canvas.create_oval(10, 10, 25, 25, fill=color)
            self.canvas.move(self.id, 245, 100)
        def draw(self):
            pass
    
    ball = Ball(canvas, &quot;red&quot;)
    
    tk.mainloop()

However, when tried the next step in this program (making the ball move by time), the book am reading from, says to do the following. So I changed the draw function to:

    def draw(self):
        self.canvas.move(self.id, 0, -1)

and add the following code to my program:

    while 1:
        ball.draw()
        tk.update_idletasks()
        tk.update()
        time.sleep(0.01)

But I noticed that adding this block of code, made the use of `tk.mainloop()` useless, since everything would show up even without it!!!

*At this moment I should mention that my book never talks about `tk.mainloop()` (maybe because it uses Python 3) but I learned about it searching the web since my programs didn&#39;t work by copying book&#39;s code!*

So I tried doing the following that would not work!!!

    while 1:
        ball.draw()
        tk.mainloop()
        time.sleep(0.01)

What&#39;s going on? What does `tk.mainloop()`? What does `tk.update_idletasks()` and `tk.update()` do and how that differs from `tk.mainloop()`? Should I use the above loop?`tk.mainloop()`? or both in my programs?



Tkinter understanding mainloop

What is the difference between `one` and `first` methods in SQLAlchemy

What is the difference between .one() and .first()

I am using the new [`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html) module (which also has a Python 2 backport) to do some simple multithreaded I/O. I am having trouble understanding how to cleanly kill tasks started using this module.

Check out the following Python 2/3 script, which reproduces the behavior I&#39;m seeing:

    #!/usr/bin/env python
    from __future__ import print_function
    
    import concurrent.futures
    import time
    
    
    def control_c_this():
        with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
            future1 = executor.submit(wait_a_bit, name=&quot;Jack&quot;)
            future2 = executor.submit(wait_a_bit, name=&quot;Jill&quot;)
            
            for future in concurrent.futures.as_completed([future1, future2]):
                future.result()
            
            print(&quot;All done!&quot;)
    
    
    def wait_a_bit(name):
        print(&quot;{n} is waiting...&quot;.format(n=name))
        time.sleep(100)
    
    
    if __name__ == &quot;__main__&quot;:
        control_c_this()

While this script is running it appears impossible to kill cleanly using the regular Control-C keyboard interrupt. I am running on OS X.

* On Python 2.7 I have to resort to `kill` from the command line to kill the script. Control-C is just ignored.
* On Python 3.4, Control-C works if you hit it twice, but then a lot of strange stack traces are dumped.

Most documentation I&#39;ve found online talks about how to cleanly kill threads with the old `threading` module. None of it seems to apply here.

And all the methods provided within the `concurrent.futures` module to stop stuff (like `Executor.shutdown()` and `Future.cancel()`) only work when the Futures haven&#39;t started yet or are complete, which is pointless in this case. I want to interrupt the Future immediately.

My use case is simple: When the user hits Control-C, the script should exit immediately like any well-behaved script does. That&#39;s all I want.

So what&#39;s the proper way to get this behavior when using `concurrent.futures`?

How do you kill Futures once they have started?

I am working with this Pandas DataFrame in Python.

    File    heat	Farheit	Temp_Rating
       1    YesQ	     75		    N/A
       1    NoR	        115		    N/A
       1    YesA	     63		    N/A
       1    NoT	         83		     41
       1    NoY	        100   		 80
       1    YesZ	     56		     12
       2    YesQ	    111		    N/A
       2    NoR    	     60	        N/A
       2    YesA	     19	        N/A
       2    NoT	        106	         77
       2    NoY    	     45     	 21
       2    YesZ	     40		     54
       3    YesQ	     84		    N/A
       3    NoR	         67		    N/A
       3    YesA	     94		    N/A
       3    NoT	         68		     39
       3    NoY    	     63		     46
       3    YesZ	     34		     81

I need to replace all NaNs in the `Temp_Rating` column with the value from the `Farheit` column.

This is what I need:

    File        heat	Temp_Rating
       1        YesQ	         75
       1	     NoR            115
       1	    YesA	         63
       1	    YesQ	         41
       1	     NoR             80
       1	    YesA	         12
       2	    YesQ	        111
       2	     NoR             60
       2	    YesA	         19
       2	     NoT   	         77
       2	     NoY             21
       2	    YesZ	         54
       3	    YesQ	         84
       3    	 NoR             67
       3	    YesA	         94
       3	     NoT             39
       3	     NoY             46
       3	    YesZ	         81

If I do a Boolean selection, I can pick out only one of these columns at a time. The problem is if I then try to join them, I am not able to do this while preserving the correct order.

How can I only find `Temp_Rating` rows with the `NaN`s and replace them with the value in the same row of the `Farheit` column?

Python Pandas replace NaN in one column with value from corresponding row of second column

I am trying to set the format to two decimal numbers in a matplotlib subplot environment. Unfortunately, I do not have any idea how to solve this task.

To prevent using scientific notation on the y-axis I used `ScalarFormatter(useOffset=False)` as you can see in my snippet below. I think my task should be solved by passing further options/arguments to the used formatter. However, I could not find any hint in matplotlib&#39;s documentation.

How can I set two decimal digits or none (both cases are needed)? I am not able to provide sample data, unfortunately.

---
-- SNIPPET --

    f, axarr = plt.subplots(3, sharex=True)

    data = conv_air
    x = range(0, len(data))

    axarr[0].scatter(x, data)
    axarr[0].set_ylabel(&#39;$T_\mathrm{air,2,2}$&#39;, size=FONT_SIZE)
    axarr[0].yaxis.set_major_locator(MaxNLocator(5))
    axarr[0].yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
    axarr[0].tick_params(direction=&#39;out&#39;, labelsize=FONT_SIZE)
    axarr[0].grid(which=&#39;major&#39;, alpha=0.5)
    axarr[0].grid(which=&#39;minor&#39;, alpha=0.2)

    data = conv_dryer
    x = range(0, len(data))

    axarr[1].scatter(x, data)
    axarr[1].set_ylabel(&#39;$T_\mathrm{dryer,2,2}$&#39;, size=FONT_SIZE)
    axarr[1].yaxis.set_major_locator(MaxNLocator(5))
    axarr[1].yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
    axarr[1].tick_params(direction=&#39;out&#39;, labelsize=FONT_SIZE)
    axarr[1].grid(which=&#39;major&#39;, alpha=0.5)
    axarr[1].grid(which=&#39;minor&#39;, alpha=0.2)

    data = conv_lambda
    x = range(0, len(data))

    axarr[2].scatter(x, data)
    axarr[2].set_xlabel(&#39;Iterationsschritte&#39;, size=FONT_SIZE)
    axarr[2].xaxis.set_major_locator(MaxNLocator(integer=True))
    axarr[2].set_ylabel(&#39;$\lambda$&#39;, size=FONT_SIZE)
    axarr[2].yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
    axarr[2].yaxis.set_major_locator(MaxNLocator(5))
    axarr[2].tick_params(direction=&#39;out&#39;, labelsize=FONT_SIZE)
    axarr[2].grid(which=&#39;major&#39;, alpha=0.5)
    axarr[2].grid(which=&#39;minor&#39;, alpha=0.2)

Matplotlib: Specify format of floats for tick labels

I have created a TimeSeries in pandas:

    In [346]: from datetime import datetime
    
    In [347]: dates = [datetime(2011, 1, 2), datetime(2011, 1, 5), datetime(2011, 1, 7),
    
     .....: datetime(2011, 1, 8), datetime(2011, 1, 10), datetime(2011, 1, 12)]
    
    In [348]: ts = Series(np.random.randn(6), index=dates)
    
    In [349]: ts
    
    Out[349]: 
    
    2011-01-02 0.690002
    
    2011-01-05 1.001543
    
    2011-01-07 -0.503087
    
    2011-01-08 -0.622274
    
    2011-01-10 -0.921169
    
    2011-01-12 -0.726213

I&#39;m following on the example from &#39;Python for Data Analysis&#39; book. 

In the following paragraph, the author checks the index type:

    In [353]: ts.index.dtype
    
    Out[353]: dtype(&#39;datetime64[ns]&#39;)

When I do exactly the same operation in the console I get:

    ts.index.dtype
    dtype(&#39;&lt;M8[ns]&#39;)

What is the difference between two types `&#39;datetime64[ns]&#39;` and `&#39;&lt;M8[ns]&#39;` ?

And why do I get a different type?

Difference between data type &#39;datetime64[ns]&#39; and &#39;&lt;M8[ns]&#39;?

I have a dataframe that looks like the following

       color  x   y
    0    red  0   0
    1    red  1   1
    2    red  2   2
    3    red  3   3
    4    red  4   4
    5    red  5   5
    6    red  6   6
    7    red  7   7
    8    red  8   8
    9    red  9   9
    10  blue  0   0
    11  blue  1   1
    12  blue  2   4
    13  blue  3   9
    14  blue  4  16
    15  blue  5  25
    16  blue  6  36
    17  blue  7  49
    18  blue  8  64
    19  blue  9  81

I ultimately want two lines, one blue, one red.  The red line should essentially be y=x and the blue line should be y=x^2

When I do the following:

    df.plot(x=&#39;x&#39;, y=&#39;y&#39;)

The output is this:

![][1]

Is there a way to make pandas know that there are two sets?  And group them accordingly.  I&#39;d like to be able to specify the column `color` as the set differentiator


  [1]: http://i.stack.imgur.com/SlPtV.png

Plotting multiple lines, in different colors, with pandas dataframe

I&#39;ve been looking around for ways to select columns through the python documentation and the forums but every example on indexing columns are too simplistic. 

Suppose I have a 10 x 10 dataframe

    df = DataFrame(randn(10, 10), index=range(0,10), columns=[&#39;A&#39;, &#39;B&#39;, &#39;C&#39;, &#39;D&#39;,&#39;E&#39;,&#39;F&#39;,&#39;G&#39;,&#39;H&#39;,&#39;I&#39;,&#39;J&#39;])

So far, all the documentations gives is just a simple example of indexing like

    subset = df.loc[:,&#39;A&#39;:&#39;C&#39;]

or

    subset = df.loc[:,&#39;C&#39;:]

But I get an error when I try index multiple, non-sequential columns, like this

    subset = df.loc[:,(&#39;A&#39;:&#39;C&#39;, &#39;E&#39;)]

How would I index in Pandas if I wanted to select column A to C, E, and G to I? It appears that this logic will not work
    
    subset = df.loc[:,(&#39;A&#39;:&#39;C&#39;, &#39;E&#39;, &#39;G&#39;:&#39;I&#39;)]


I feel that the solution is pretty simple, but I can&#39;t get around this error. Thanks!

Select multiple columns by labels in pandas

For pandas, would anyone know, if any datatype apart from

(i) `float64`, `int64`  (and other variants of `np.number` like `float32`, `int8` etc.)

(ii)  `bool`

(iii) `datetime64`, `timedelta64`

such as string columns, always have a `dtype` of `object` ? 

Alternatively, I want to know, if there are any datatype apart from (i), (ii) and (iii) in the list above that `pandas` does not make it&#39;s `dtype` an `object`?

what are all the dtypes that pandas recognizes?

I&#39;m working on a data frame that has non-detects which are coded with &#39;&lt;&#39;.  Sometimes there is a space after the &#39;&lt;&#39; and sometimes not e.g. &#39;&lt;2&#39; or &#39;&lt; 2&#39;.  I&#39;d like to remove every occurrence of the space.

Example:
    
    data &lt;- data.frame(name = rep(letters[1:3], each = 3), var1 = rep(&#39;&lt; 2&#39;, 9), var2 = rep(&#39;&lt;3&#39;, 9))
 
      name var1 var2 
    1    a  &lt; 2   &lt;3
    2    b  &lt; 2   &lt;3
    3    c  &lt; 2   &lt;3
This is where I&#39;ve got to:

I can extract all the values and make the new strings but I can&#39;t put them back in the data frame.

    index &lt;- str_detect(unlist(data), &#39;&lt;&#39;)
    index &lt;- matrix(index, nrow = 3)

    data[index] 
    #[1] &quot;&lt; 2&quot; &quot;&lt; 2&quot; &quot;&lt; 2&quot; &quot;&lt;3&quot;  &quot;&lt;3&quot;  &quot;&lt;3&quot; 

    replacements &lt;- str_replace_all(data[index], &quot;&lt;[ ]+&quot;,&quot;&lt;&quot;) 
    replacements
    #[1] &quot;&lt;2&quot; &quot;&lt;2&quot; &quot;&lt;2&quot; &quot;&lt;3&quot; &quot;&lt;3&quot; &quot;&lt;3&quot;

    data[index] &lt;- replacements

    #Error in `[&lt;-.data.frame`(`*tmp*`, index, value = c(&quot;&lt;2&quot;, &quot;&lt;2&quot;, &quot;&lt;2&quot;,  : 
    #  unsupported matrix index in replacement



Replace all occurrences of a string in a data frame

I have a `pd.DataFrame` that was created by parsing some excel spreadsheets. A column of which has empty cells. For example, below is the output for the frequency of that column, 32320 records have missing values for *Tenant*.

    &gt;&gt;&gt; value_counts(Tenant, normalize=False)
                                  32320
        Thunderhead                8170
        Big Data Others            5700
        Cloud Cruiser              5700
        Partnerpedia               5700
        Comcast                    5700
        SDP                        5700
        Agora                      5700
        dtype: int64

I am trying to drop rows where Tenant is missing, however `.isnull()` option does not recognize the missing values. 

    &gt;&gt;&gt; df[&#39;Tenant&#39;].isnull().sum()
        0

The column has data type &quot;Object&quot;. What is happening in this case? How can I drop records where *Tenant* is missing?

Drop rows containing empty cells from a pandas DataFrame

I am creating a DataFrame from a csv as follows:

    stock = pd.read_csv(&#39;data_in/&#39; + filename + &#39;.csv&#39;, skipinitialspace=True)

The DataFrame has a date column. Is there a way to create a new DataFrame (or just overwrite the existing one) which only contains rows with date values that fall within a specified date range or between two specified date values?


Select DataFrame rows between two dates

I have a pandas dataframe and I want to filter the whole df based on the value of two columns in the data frame.  I want to get back all rows and columns where IBRD or IMF != 0.  

    alldata_balance = alldata[(alldata[IBRD] !=0) or (alldata[IMF] !=0)]

but this gives me a ValueError

&gt; ValueError: The truth value of a Series is ambiguous. Use a.empty,
&gt; a.bool(),       a.item(), a.any() or a.all().

                                           
So I know I am not using the or statement correctly, is there a way to do this?


Filtering Pandas Dataframe using OR statement

I have the following sample code:

    System.out.println(
           &quot;Result: &quot; +
            Stream.of(1, 2, 3)
                    .filter(i -&gt; {
                        System.out.println(i);
                        return true;
                    })
                    .findFirst()
                    .get()
    );
    System.out.println(&quot;-----------&quot;);
    System.out.println(
           &quot;Result: &quot; +
            Stream.of(1, 2, 3)
                    .flatMap(i -&gt; Stream.of(i - 1, i, i + 1))
                    .flatMap(i -&gt; Stream.of(i - 1, i, i + 1))
                    .filter(i -&gt; {
                        System.out.println(i);
                        return true;
                    })
                    .findFirst()
                    .get()
    );

The output is as follows:

    1
    Result: 1
    -----------
    -1
    0
    1
    0
    1
    2
    1
    2
    3
    Result: -1

From here I see that in first case `stream` really behaves lazily - we use `findFirst()` so once we have first element our filtering lambda is not invoked.
However, in second case which uses `flatMap`s we see that despite first element which fulfils the filter condition is found (it&#39;s just any first element as lambda always returns true) further contents of the stream are still being fed through filtering function.

I am trying to understand why it behaves like this rather than giving up after first element is calculated as in the first case.
Any helpful information would be appreciated.



Why filter() after flatMap() is &quot;not completely&quot; lazy in Java streams?

The following Java code fails to compile:

    @FunctionalInterface
    private interface BiConsumer&lt;A, B&gt; {
        void accept(A a, B b);
    }
    
    private static void takeBiConsumer(BiConsumer&lt;String, String&gt; bc) { }
    
    public static void main(String[] args) {
        takeBiConsumer((String s1, String s2) -&gt; new String(&quot;hi&quot;)); // OK
        takeBiConsumer((String s1, String s2) -&gt; &quot;hi&quot;); // Error
    }

The compiler reports:

    Error:(31, 58) java: incompatible types: bad return type in lambda expression
        java.lang.String cannot be converted to void

The weird thing is that the line marked &quot;OK&quot; compiles fine, but the line marked &quot;Error&quot; fails. They seem essentially identical.

Why does this Java 8 lambda fail to compile?

What does the following code do?

    a = lambda _:True

 

From what I read and tested in the interactive prompt, it seems to be a function that returns always `True`.

Am I understanding this correctly? I hope to understand why an underscore (`_`) was used as well. 


Python&#39;s lambda with underscore for an argument?

This may sounds like a strange question, but is there a way to refer to a standard no-op (aka null operation, null-pattern method, no-operation, do-nothing method) method for a Lambda in Java 8.

Currently, I have a method that takes a, say, ``void foo(Consumer&lt;Object&gt;)``, and I want to give it a no-op, I have to declare:


    foo(new Consumer&lt;Object&gt;() { 
      public void accept(Object o) { 
        // do nothing 
      }
    }


where I would like to be able to do something like:

    foo(Object::null)

instead.  Does something like exist?   

Not sure how that would work with multi-parameter methods -- perhaps this is a deficiency in the lambdas in Java.


Is there a method reference for a no-op (NOP) that can be used for anything lambda?

Let&#39;s say I have the following functional interface in Java 8:

    interface Action&lt;T, U&gt; {
       U execute(T t);
    }

And for some cases I need an action without arguments or return type. So I write
something like this:

    Action&lt;Void, Void&gt; a = () -&gt; { System.out.println(&quot;Do nothing!&quot;); };

However, it gives me compile error, I need to write it as

    Action&lt;Void, Void&gt; a = (Void v) -&gt; { System.out.println(&quot;Do nothing!&quot;); return null;};

Which is ugly. Is there any way to get rid of the `Void` type parameter?




Java 8 lambda Void argument

I would like to use Pandas `df.apply` but only for certain rows

As an example, I want to do something like this, but my actual issue is a little more complicated:

    import pandas as pd
    import math
    z = pd.DataFrame({&#39;a&#39;:[4.0,5.0,6.0,7.0,8.0],&#39;b&#39;:[6.0,0,5.0,0,1.0]})
    z.where(z[&#39;b&#39;] != 0, z[&#39;a&#39;] / z[&#39;b&#39;].apply(lambda l: math.log(l)), 0)

What I want in this example is the value in &#39;a&#39; divided by the log of the value in &#39;b&#39; for each row, and for rows where &#39;b&#39; is 0, I simply want to return 0.

Pandas apply but only for rows where a condition is met

I have a dataframe that may look like this:

    A        B        C
    foo      bar      foo bar
    bar foo  foo      bar

I want to look through every element of each row (or every element of each column) and apply the following function to get the subsequent DF:

    def foo_bar(x):
        return x.replace(&#39;foo&#39;, &#39;wow&#39;)

    A        B        C
    wow      bar      wow bar
    bar wow  wow      bar

Is there a simple one-liner that can apply a function to each cell? 

This is a simplistic example so there may be an easier way to execute this specific example other than applying a function, but what I am really asking about is how to apply a function in every cell within a dataframe. 

Apply function to each cell in DataFrame

I have a Python Pandas dataframe df:

    d=[[&#39;hello&#39;,1,&#39;GOOD&#39;,&#39;long.kw&#39;],
       [1.2,&#39;chipotle&#39;,np.nan,&#39;bingo&#39;],
       [&#39;various&#39;,np.nan,3000,123.456]]                                                    
    t=pd.DataFrame(data=d, columns=[&#39;A&#39;,&#39;B&#39;,&#39;C&#39;,&#39;D&#39;]) 
which looks like this:

    print(t)
             A         B     C        D
    0    hello         1  GOOD  long.kw
    1      1.2  chipotle   NaN    bingo
    2  various       NaN  3000  123.456
I am trying to create a new column which is a `list` of the values in `A`, `B`, `C`, and `D`.  So it would look like this:

    t[&#39;combined&#39;]                                             
    
    Out[125]: 
    0        [hello, 1, GOOD, long.kw]
    1        [1.2, chipotle, nan, bingo]
    2        [various, nan, 3000, 123.456]
    Name: combined, dtype: object

I am trying this code:

    t[&#39;combined&#39;] = t.apply(lambda x: list([x[&#39;A&#39;],
                                            x[&#39;B&#39;],
                                            x[&#39;C&#39;],
                                            x[&#39;D&#39;]]),axis=1)    

Which returns this error:

    ValueError: Wrong number of items passed 4, placement implies 1 
    
What is puzzling to me is if remove one of the columns that I want to put in the list (or add another column to the dataframe that I DON&#39;T add to the list), my code works.  

For instance, run this code:

    t[&#39;combined&#39;] = t.apply(lambda x: list([x[&#39;A&#39;],
                                            x[&#39;B&#39;],
                                            x[&#39;D&#39;]]),axis=1)      

Returns this which is perfect if I only wanted the 3 columns:

    print(t)
             A         B     C        D                 combined
    0    hello         1  GOOD  long.kw      [hello, 1, long.kw]
    1      1.2  chipotle   NaN    bingo   [1.2, chipotle, bingo]
    2  various       NaN  3000  123.456  [various, nan, 123.456]
                                        
I am at a complete loss as to why requesting the &#39;combined&#39; list be made of all columns in the dataframe would create an error, but selecting all but 1 column to create the &#39;combined&#39; list and the list is created as expected.                                        




Pandas - combine column values into a list in a new column

I have seen many answers posted to questions on Stack Overflow involving the use of the Pandas method `apply`. I have also seen users commenting under them saying that &quot;`apply` is slow, and should be avoided&quot;. 

I have read many articles on the topic of performance that explain `apply` is slow. I have also seen a disclaimer in the docs about how `apply` is simply a convenience function for passing UDFs (can&#39;t seem to find that now). So, the general consensus is that `apply` should be avoided if possible. However, this raises the following questions: 

1. If `apply` is so bad, then why is it in the API?
2. How and when should I make my code `apply`-free?
3. Are there ever any situations where `apply` is _good_ (better than other possible solutions)? 

Content Type	Original Author	Original Content on Stackoverflow
Question	sedavidw	View Question on Stackoverflow
Solution 1 - Python	EdChum	View Answer on Stackoverflow

Trouble passing in lambda to apply for pandas DataFrame

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