This is a very common warning from pandas. It means you are writing in a copy slice, not the original data so it might not apply to the original columns due to confusing chained assignment. Please read this [post][1]. It has detailed discussion on this `SettingWithCopyWarning`. In your case I think you can try 

    data.loc[data[&#39;name&#39;] == &#39;fred&#39;, &#39;A&#39;] = 0
    


  [1]: https://stackoverflow.com/questions/20625582/how-to-deal-with-this-pandas-warning

I need to implement push notification feature in my application. Exploring some options.

Google recently recommends Firebase platform, but this is not completely a free service. So I thought of using GCM again. Will google stop support for GCM in future?

Is Firebase Cloud Messaging free?

I&#39;m trying to test out Firebase Cloud messaging APIs as all functionality is not available from console (notably customization of notifications when app is in background). But for some reasons, I cannot get it to work, and it always shows up 401 error. I investigated the reasons for this, and tried it after regenerating new server key, but the error remains constant. Surprisingly, when I generated a new server key, it is not reflected in Firebase console and it shows server key as empty. Also, I tried adding my IP address to the server whitelist IPs but still no luck. I&#39;ve attached a screenshot of a request that I did with Postman (I substitue the server key in place of *serverKey*. 

I&#39;m stuck on this for a few hours and would really appreciate some help.[![enter image description here][1]][1]


  [1]: http://i.stack.imgur.com/JBv5Y.png

Firebase 401 unauthorized error FCM

I have a pandas dataframe: data. it has columns [&quot;name&quot;, &#39;A&#39;, &#39;B&#39;] 

What I want to do (and works) is:

    d2 = data[data[&#39;name&#39;] == &#39;fred&#39;] #This gives me multiple rows
    d2[&#39;A&#39;] = 0

This will set the column A on the fred rows to 0. 
I&#39;ve also done:

    indexes = d2.index
    data[&#39;A&#39;][indexes] = 0

However, both give me the same warning: 

    /Users/brianp/work/cyan/venv/lib/python2.7/site-packages/pandas/core/indexing.py:128: SettingWithCopyWarning: 
    A value is trying to be set on a copy of a slice from a DataFrame
    
    See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

How does pandas WANT me to do this?

Correct way to set value on a slice in pandas

I have a pandas dataframe: data. it has columns ["name", 'A', 'B']
What I want to do (and works) is:
<pre><code class="hljs language-ini">d2 = data[data['name'] == 'fred'] #This gives me multiple rows
d2['A'] = 0
</code></pre>
This will set the column A on the fred rows to 0.
I've also done:
<pre><code class="hljs language-ini">indexes = d2.index
data['A'][indexes] = 0
</code></pre>
However, both give me the same warning:
<pre><code class="hljs language-vbnet">/Users/brianp/work/cyan/venv/lib/python2.7/site-packages/pandas/core/indexing.py:128: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
</code></pre>
How does pandas WANT me to do this?

In multiple open source projects, I have seen people do `os.path.abspath(os.path.realpath(__file__))` to get the absolute path to the current file.

However, I find that `os.path.abspath(__file__)` and `os.path.realpath(__file__)` produce the same result. `os.path.abspath(os.path.realpath(__file__))` seems to be a bit redundant.

Is there a reason people are using that?

Why would one use both, os.path.abspath and os.path.realpath?

&gt; **This Q&amp;A is intended as a canonical(-ish) concerning two-dimensional (and multi-dimensional) interpolation using scipy. There are often questions concerning the basic syntax of various multidimensional interpolation methods, I hope to set these straight too.**

I have a set of scattered two-dimensional data points, and I would like to plot them as a nice surface, preferably using something like `contourf` or `plot_surface` in `matplotlib.pyplot`. How can I interpolate my two-dimensional or multidimensional data to a mesh using scipy?

I&#39;ve found the `scipy.interpolate` sub-package, but I keep getting errors when using `interp2d` or `bisplrep` or `griddata` or `RBFInterpolator` (or the older `Rbf`). What is the proper syntax of these methods?

How can I perform two-dimensional interpolation using scipy?

I try to scrape this [site][1] by Selenium.

I want to click in &quot;Next Page&quot; buttom, for this I do:

     driver.find_element_by_class_name(&#39;pagination-r&#39;).click()
it works for many pages but not for all, I got this error

    WebDriverException: Message: Element is not clickable at point (918, 13). Other element would receive the click: &lt;div class=&quot;linkAuchan&quot;&gt;&lt;/div&gt;

always for [this page][2] 


I read [this question][3] 

and I tried this 

    driver.implicitly_wait(10)
    el = driver.find_element_by_class_name(&#39;pagination-r&#39;)
    action = webdriver.common.action_chains.ActionChains(driver)
    action.move_to_element_with_offset(el, 918, 13)
    action.click()
    action.perform()

  [1]: http://www.auchandrive.fr/
  [2]: https://www.auchandrive.fr/drive/Nice-Cote-dAzur-869/Produits-Frais-R3686962/Traiteur-3686315/;jsessionid=D1CEED79373B4043E52D56E1D0E96A37.27BC109326910D9522BB1D9773F60A
  [3]: https://stackoverflow.com/questions/11908249/debugging-element-is-not-clickable-at-point-error
but I got the same error


Selenium-Debugging: Element is not clickable at point (X,Y)

Can someone give me an explanation why `isinstance()` returns True in the following case? I expected False, when writing the code.

    print isinstance(True, (float, int))
    True

My guess would be that its Python&#39;s internal subclassing, as zero and one - whether float or int - both evaluate when used as boolean, but don&#39;t know the exact reason.

What would be the most pythonic way to solve such a situation? I could use `type()` but in most cases this is considered less pythonic.

Comparing boolean and int using isinstance

I&#39;ve a problem that Jupyter can&#39;t see env variable in bashrc file, is there a way to load these variables in jupyter or add custome variable to it?

How to set env variable in Jupyter notebook

I am running Python on a Windows machine at the following path:

    C:\WinPython-64bit-3.4.4.1

I am trying to upgrade to the latest version of pandas (currently running &#39;0.17.1&#39;) but am having problems.

I have looked at previous posts and have tried on the command line using :

    c:/&gt;pip install --upgrade pandas

but just got `&#39;pip is not recognised as an internal or external command, operable program or batch file&#39;`.


Upgrade version of Pandas

Given the following data frame:

    import pandas as pd
    import numpy as np
    df=pd.DataFrame({&#39;A&#39;:[&#39;A&#39;,&#39;A&#39;,&#39;A&#39;,&#39;B&#39;,&#39;B&#39;,&#39;B&#39;],
                    &#39;B&#39;:[&#39;a&#39;,&#39;a&#39;,&#39;b&#39;,&#39;a&#39;,&#39;a&#39;,&#39;a&#39;],
                    })
    df

     	A 	B
    0 	A 	a 
    1 	A 	a 
    2 	A 	b 
    3 	B 	a 
    4 	B 	a 
    5 	B 	a

I&#39;d like to create column &#39;C&#39;, which numbers the rows within each group in columns A and B like this:

     	A 	B   C
    0 	A 	a 	1
    1 	A 	a 	2
    2 	A 	b 	1
    3 	B 	a   1
    4 	B 	a   2
    5 	B 	a   3

I&#39;ve tried this so far:

    df[&#39;C&#39;]=df.groupby([&#39;A&#39;,&#39;B&#39;])[&#39;B&#39;].transform(&#39;rank&#39;)

...but it doesn&#39;t work!

Pandas number rows within group in increasing order

My dataframe has a `DOB` column (example format **`1/1/2016`**) which by default gets converted to Pandas dtype &#39;object&#39;.

Converting this to date format with `df[&#39;DOB&#39;] = pd.to_datetime(df[&#39;DOB&#39;])`, the date gets converted to: **`2016-01-26`** and its `dtype` is: `datetime64[ns]`.

Now I want to convert this date format to **`01/26/2016` or any other general date format**. How do I do it?

(Whatever the method I try, it always shows the date in `2016-01-26` format.)

How to change the datetime format in Pandas

I currently have this code. It works perfectly.

It loops through excel files in a folder,
removes the first 2 rows, then saves them as individual excel files,
and it also saves the files in the loop as an appended file.

Currently the appended file **overwrites** the existing file each time I run the code.

I need to append the new data to the bottom of the **already existing excel sheet** (&#39;master_data.xlsx)


    dfList = []
    path = &#39;C:\\Test\\TestRawFile&#39; 
    newpath = &#39;C:\\Path\\To\\New\\Folder&#39;
    
    for fn in os.listdir(path): 
      # Absolute file path
      file = os.path.join(path, fn)
      if os.path.isfile(file): 
        # Import the excel file and call it xlsx_file 
        xlsx_file = pd.ExcelFile(file) 
        # View the excel files sheet names 
        xlsx_file.sheet_names 
        # Load the xlsx files Data sheet as a dataframe 
        df = xlsx_file.parse(&#39;Sheet1&#39;,header= None) 
        df_NoHeader = df[2:] 
        data = df_NoHeader 
        # Save individual dataframe
        data.to_excel(os.path.join(newpath, fn))
    
        dfList.append(data) 
    
    appended_data = pd.concat(dfList)
    appended_data.to_excel(os.path.join(newpath, &#39;master_data.xlsx&#39;))


I thought this would be a simple task, but I guess not.
I think I need to bring in the master_data.xlsx file as a dataframe, then match the index up with the new appended data, and save it back out. Or maybe there is an easier way. Any Help is appreciated.


Append existing excel sheet with new dataframe using python pandas

Suppose I have a dataframe with countries that goes as:

    cc | temp
    US | 37.0
    CA | 12.0
    US | 35.0
    AU | 20.0

I know that there is a pd.get_dummies function to convert the countries to &#39;one-hot encodings&#39;. However, I wish to convert them to indices instead such that I will get `cc_index = [1,2,1,3]` instead. 

I&#39;m assuming that there is a faster way than using the get_dummies along with a numpy where clause as shown below:

`[np.where(x) for x in df.cc.get_dummies().values]`

This is somewhat easier to do in R using &#39;factors&#39; so I&#39;m hoping pandas has something similar.


Pandas: convert categories to numbers

I love pandas and have been using it for years and feel pretty confident I have a good handle on how to subset dataframes and deal with views vs copies appropriately (though I use a lot of assertions to be sure). I also know that there have been tons of questions about SettingWithCopyWarning, e.g. https://stackoverflow.com/questions/20625582/how-to-deal-with-settingwithcopywarning-in-pandas
and some great recent guides on wrapping your head around when it happens, e.g. [Understanding SettingWithCopyWarning in pandas](https://www.dataquest.io/blog/settingwithcopywarning/). 

But I also know specific things like the quote from [this answer](https://stackoverflow.com/a/17961468/8022335) are no longer in the most recent docs (`0.22.0`) and that many things have been deprecated over the years (leading to some inappropriate old SO answers), and that things are [continuing to change](https://stackoverflow.com/a/34908742/8022335). 

Recently after teaching pandas to complete newcomers with very basic general Python knowledge about things like avoiding chained-indexing (and using `.iloc`/`.loc`), I&#39;ve still struggled to provide *general rules of thumb* to know when it&#39;s important to pay attention to the `SettingWithCopyWarning` (e.g. when it&#39;s safe to ignore it). 

I&#39;ve personally found that the specific pattern of subsetting a dataframe according so some rule (e.g. slicing or boolean operation) and then modifying that subset, *independent of the original dataframe*, is a much more common operation than the docs suggest. In this situation we want to *modify the copy not the original* and the warning is confusing/scary to newcomers.

I know it&#39;s not trivial to know ahead of time  when a view vs a copy is returned, e.g.   
https://stackoverflow.com/questions/23296282/what-rules-does-pandas-use-to-generate-a-view-vs-a-copy  
https://stackoverflow.com/questions/26879073/checking-whether-data-frame-is-copy-or-view-in-pandas

So instead I&#39;m looking for the answer to a more general (beginner friendly) question: **when does performing an operation on a subsetted dataframe affect the original dataframe from which it was created, and when are they independent?**. 

I&#39;ve created some cases below that I think seem reasonable, but I&#39;m not sure if there&#39;s a &quot;gotcha&quot; I&#39;m missing or if there&#39;s any easier way to think/check this. I was hoping someone could confirm that my intuitions about the following use cases are correct as the pertain to my question above.

    import pandas as pd
    df1 = pd.DataFrame({&#39;A&#39;:[2,4,6,8,10],&#39;B&#39;:[1,3,5,7,9],&#39;C&#39;:[10,20,30,40,50]})
 
1) Warning: No    
   Original changed: No  

    # df1 will be unaffected because we use .copy() method explicitly 
    df2 = df1.copy()
    #
    # Reference: docs
    df2.iloc[0,1] = 100

2) Warning: Yes (I don&#39;t really understood why)    
   Original changed: No  

    # df1 will be unaffected because .query() always returns a copy
    #
    # Reference:
    # https://stackoverflow.com/a/23296545/8022335
    df2 = df1.query(&#39;A &lt; 10&#39;)
    df2.iloc[0,1] = 100

3) Warning: Yes  
   Original changed: No

    # df1 will be unaffected because boolean indexing with .loc
    # always returns a copy
    #
    # Reference:
    # https://stackoverflow.com/a/17961468/8022335
    df2 = df1.loc[df1[&#39;A&#39;] &lt; 10,:]
    df2.iloc[0,1] = 100

4) Warning: No  
   Original changed: No

    # df1 will be unaffected because list indexing with .loc (or .iloc)
    # always returns a copy
    #
    # Reference:
    # Same as 4)
    df2 = df1.loc[[0,3,4],:]
    df2.iloc[0,1] = 100

5) Warning: No  
   Original changed: Yes (confusing to newcomers but makes sense)  

    # df1 will be affected because scalar/slice indexing with .iloc/.loc
    # always references the original dataframe, but may sometimes 
    # provide a view and sometimes provide a copy
    #
    # Reference: docs
    df2 = df1.loc[:10,:]
    df2.iloc[0,1] = 100

**tl;dr**
When creating a new dataframe from the original, changing the new dataframe:  
Will change the original when **scalar/slice indexing with .loc/.iloc is used to create the new dataframe**.  
Will *not* change the original when **boolean indexing with .loc, `.query()`, or `.copy()` is used to create the new dataframe**

Content Type	Original Author	Original Content on Stackoverflow
Question	Brian Postow	View Question on Stackoverflow
Solution 1 - Python	Andreas Hsieh	View Answer on Stackoverflow

Correct way to set value on a slice in pandas

Python Problem Overview

Python Solutions

Solution 1 - Python

Firebase 401 unauthorized error FCM

Is Firebase Cloud Messaging free?

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