You can use the [`astype`](http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.astype.html) method to cast a Series (one column):

    df[&#39;col_name&#39;] = df[&#39;col_name&#39;].astype(object)

Or the entire DataFrame:

    df = df.astype(object)

---

### Update

[Since version 0.15, you can use the category datatype](http://pandas.pydata.org/pandas-docs/stable/categorical.html) in a Series/column:

    df[&#39;col_name&#39;] = df[&#39;col_name&#39;].astype(&#39;category&#39;)

*Note: `pd.Factor` was been deprecated and has been removed in favor of `pd.Categorical`.*

There&#39;s also pd.factorize function to use:

    # use the df data from @herrfz

    In [150]: pd.factorize(df.b)
    Out[150]: (array([0, 1, 0, 1, 2]), array([&#39;yes&#39;, &#39;no&#39;, &#39;absent&#39;], dtype=object))
    In [152]: df[&#39;c&#39;] = pd.factorize(df.b)[0]

    In [153]: df
    Out[153]: 
       a       b  c
    0  1     yes  0
    1  2      no  1
    2  3     yes  0
    3  4      no  1
    4  5  absent  2

`Factor` and `Categorical` are the same, as far as I know. I think it was initially called Factor, and then changed to Categorical. To convert to Categorical maybe you can use `pandas.Categorical.from_array`, something like this:

    In [27]: df = pd.DataFrame({&#39;a&#39; : [1, 2, 3, 4, 5], &#39;b&#39; : [&#39;yes&#39;, &#39;no&#39;, &#39;yes&#39;, &#39;no&#39;, &#39;absent&#39;]})
    
    In [28]: df
    Out[28]: 
       a       b
    0  1     yes
    1  2      no
    2  3     yes
    3  4      no
    4  5  absent
    
    In [29]: df[&#39;c&#39;] = pd.Categorical.from_array(df.b).labels
    
    In [30]: df
    Out[30]: 
       a       b  c
    0  1     yes  2
    1  2      no  1
    2  3     yes  2
    3  4      no  1
    4  5  absent  0

I have a website that I am using the new Universal Analytics (analytics.js) to track. Everything is setup and working (pageviews, referrals, etc.) using the following code snippet:

    &lt;script&gt;
	  (function(i,s,o,g,r,a,m){i[&#39;GoogleAnalyticsObject&#39;]=r;i[r]=i[r]||function(){
	  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
	  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
	  })(window,document,&#39;script&#39;,&#39;//www.google-analytics.com/analytics.js&#39;,&#39;ga&#39;);

	  ga(&#39;create&#39;, &#39;UA-39570713-1&#39;, &#39;site.com&#39;);
	  ga(&#39;send&#39;, &#39;pageview&#39;);

	&lt;/script&gt;

That is located before the `&lt;/head&gt;` tag.

I am using JQuery to fire off an event. I tested the JQuery with an alert message and it is getting called, so that isn&#39;t the problem. Here is the snippet that fires when a button is clicked:

    $(&#39;#submitButton&#39;).on(&#39;click&#39;, function() {
		  ga(&#39;send&#39;, &#39;event&#39;, &#39;button&#39;, &#39;click&#39;, &#39;contact form&#39;);
		});

Nothing is appearing in the Events section of Analytics. I keep clicking the button, even from different computers just to make sure it isn&#39;t excluding my IP address. Because the [Analytics doc][1] that Google provides does not provide a whole lot of explanation I&#39;m at a loss here.


  [1]: https://developers.google.com/analytics/devguides/collection/analyticsjs/events?hl=en_US

Events not being tracked in new Google Analytics (analytics.js) setup

I am testing my app that uses `iCloud`. Sometimes, when I fire up the app I get the following warning in the console:

    GEOResourceManifestServerRemoteProxy: Lost connection to geod

Once I get this warning I don&#39;t seem to get any data from `iCloud`. I have tried searching this issue, but have found very little information on it. 

Any suggestions on how one should try to detect this issue and try to handle it?

iCloud and GEOResourceManifestServerRemoteProxy: Lost connection to geod warning

In pandas, how can I convert a column of a DataFrame into dtype object?
Or better yet, into a factor? (For those who speak R, in Python, how do I `as.factor()`?)

Also, what&#39;s the difference between `pandas.Factor` and `pandas.Categorical`?


Pandas - make a column dtype object or Factor

<p>In pandas, how can I convert a column of a DataFrame into dtype object?
Or better yet, into a factor? (For those who speak R, in Python, how do I <code>as.factor()</code>?)</p>
<p>Also, what's the difference between <code>pandas.Factor</code> and <code>pandas.Categorical</code>?</p>


I have an external package I want to install into my python virtualenv from a tar file.
What is the best way to install the package?

I&#39;ve discovered 2 ways that can do it:

 1. Extract the tar file, then run `python setup.py install` inside of the extracted directory.
 2. `pip install packagename.tar.gz` from example # 7 in https://pip.pypa.io/en/stable/reference/pip_install/#examples

Is if there is any difference doing them in these 2 ways.

Difference between &#39;python setup.py install&#39; and &#39;pip install&#39;

I have a small python project that has the following structure - 

    Project 
     -- pkg01
       -- test01.py
     -- pkg02
       -- test02.py
     -- logging.conf

 
I plan to use the default logging module to print messages to stdout and a log file.
To use the logging module, some initialization is required - 

    import logging.config
    
    logging.config.fileConfig(&#39;logging.conf&#39;)
    logger = logging.getLogger(&#39;pyApp&#39;)
    
    logger.info(&#39;testing&#39;)

At present, I perform this initialization in every module before I start logging messages. Is it possible to perform this initialization only once in one place such that the same settings are reused by logging all over the project?


Using logging in multiple modules

I&#39;m trying to create an OrderedDict object but no sooner do I create it, than the elements are all jumbled.

This is what I do:

    from collections import OrderedDict
    od = OrderedDict({(0,0):[2],(0,1):[1,9],(0,2):[1,5,9]})

The elements don&#39;t stay in the order I assign

    od
    OrderedDict([((0, 1), [1, 9]), ((0, 0), [2]), ((0, 2), [1, 5, 9])])

docs.python.org doesn&#39;t have an example and I can&#39;t figure out why the order is getting jumbled. Any help is greatly appreciated. 

Python OrderedDict not keeping element order

I want to be able to get a estimate of the distance between two (latitude, longitude) points. I want to undershoot, as this will be for A* graph search and I want it to be **fast**. The points will be at most 800 km apart.

How can I quickly estimate the distance between two (latitude, longitude) points?

If I have a list of card suits in arbitrary order like so:

    suits = [&quot;h&quot;, &quot;c&quot;, &quot;d&quot;, &quot;s&quot;]

and I want to return a list without the `&#39;c&#39;`

    noclubs = [&quot;h&quot;, &quot;d&quot;, &quot;s&quot;]

is there a simple way to do this?

A quick way to return list without a specific element in Python

![Structure of data;][1]


  [1]: http://i.stack.imgur.com/a34it.png

Using Python Pandas I am trying to find the ```Country``` &amp; ```Place``` with the maximum value.

This returns the maximum value:

    data.groupby([&#39;Country&#39;,&#39;Place&#39;])[&#39;Value&#39;].max()

But how do I get the corresponding ```Country``` and ```Place``` name?

Find maximum value of a column and return the corresponding row values using Pandas

I&#39;ve got a bunch of polling data; I want to compute a Pandas rolling mean to get an estimate for each day based on a three-day window. According to [this question][1], the `rolling_*` functions compute the window based on a specified number of values, and not a specific datetime range. 

How do I implement this functionality?

Sample input data: 

    polls_subset.tail(20)
    Out[185]: 
                favorable  unfavorable  other
    
    enddate                                  
    2012-10-25       0.48         0.49   0.03
    2012-10-25       0.51         0.48   0.02
    2012-10-27       0.51         0.47   0.02
    2012-10-26       0.56         0.40   0.04
    2012-10-28       0.48         0.49   0.04
    2012-10-28       0.46         0.46   0.09
    2012-10-28       0.48         0.49   0.03
    2012-10-28       0.49         0.48   0.03
    2012-10-30       0.53         0.45   0.02
    2012-11-01       0.49         0.49   0.03
    2012-11-01       0.47         0.47   0.05
    2012-11-01       0.51         0.45   0.04
    2012-11-03       0.49         0.45   0.06
    2012-11-04       0.53         0.39   0.00
    2012-11-04       0.47         0.44   0.08
    2012-11-04       0.49         0.48   0.03
    2012-11-04       0.52         0.46   0.01
    2012-11-04       0.50         0.47   0.03
    2012-11-05       0.51         0.46   0.02
    2012-11-07       0.51         0.41   0.00

Output would have only one row for each date. 

  [1]: https://stackoverflow.com/questions/9762193/pandas-rolling-median-for-duplicate-time-series-data

Pandas: rolling mean by time interval

What&#39;s a simple and efficient way to shuffle a dataframe in pandas, by rows or by columns? I.e. how to write a function `shuffle(df, n, axis=0)` that takes a dataframe, a number of shuffles `n`, and an axis (`axis=0` is rows, `axis=1` is columns) and returns a copy of the dataframe that has been shuffled `n` times. 

**Edit**: key is to do this without destroying the row/column labels of the dataframe. If you just shuffle `df.index` that loses all that information. I want the resulting `df` to be the same as the original except with the order of rows or order of columns different.


**Edit2**: My question was unclear. When I say shuffle the rows, I mean shuffle each row independently. So if you have two columns `a` and `b`, I want each row shuffled on its own, so that you don&#39;t have the same associations between `a` and `b` as you do if you just re-order each row as a whole. Something like: 

    for 1...n:
      for each col in df: shuffle column
    return new_df

But hopefully more efficient than naive looping. This does not work for me:

    def shuffle(df, n, axis=0):
            shuffled_df = df.copy()
            for k in range(n):
                shuffled_df.apply(np.random.shuffle(shuffled_df.values),axis=axis)
            return shuffled_df
    
    df = pandas.DataFrame({&#39;A&#39;:range(10), &#39;B&#39;:range(10)})
    shuffle(df, 5)

shuffling/permutating a DataFrame in pandas

When I run the program, Pandas gives &#39;Future warning&#39; like below every time.

    D:\Python\lib\site-packages\pandas\core\frame.py:3581: FutureWarning: rename with inplace=True  will return None from pandas 0.11 onward
      &quot; from pandas 0.11 onward&quot;, FutureWarning) 

I got the msg, but I just want to stop Pandas showing such msg again and again, is there any buildin parameter that I can set to let Pandas not pop up the &#39;Future warning&#39; ?

How to suppress Pandas Future warning ?

I have some hierarchical data which bottoms out into time series data which looks something like this:

    df = pandas.DataFrame(
        {&#39;value_a&#39;: values_a, &#39;value_b&#39;: values_b},
        index=[states, cities, dates])
    df.index.names = [&#39;State&#39;, &#39;City&#39;, &#39;Date&#39;]
    df

                                   value_a  value_b
    State   City       Date                        
    Georgia Atlanta    2012-01-01        0       10
                       2012-01-02        1       11
                       2012-01-03        2       12
                       2012-01-04        3       13
            Savanna    2012-01-01        4       14
                       2012-01-02        5       15
                       2012-01-03        6       16
                       2012-01-04        7       17
    Alabama Mobile     2012-01-01        8       18
                       2012-01-02        9       19
                       2012-01-03       10       20
                       2012-01-04       11       21
            Montgomery 2012-01-01       12       22
                       2012-01-02       13       23
                       2012-01-03       14       24
                       2012-01-04       15       25

I&#39;d like to perform time resampling per city, so something like
    
    df.resample(&quot;2D&quot;, how=&quot;sum&quot;)

would output

                                 value_a  value_b
    State   City       Date                        
    Georgia Atlanta    2012-01-01        1       21
                       2012-01-03        5       25
            Savanna    2012-01-01        9       29
                       2012-01-03       13       33
    Alabama Mobile     2012-01-01       17       37
                       2012-01-03       21       41
            Montgomery 2012-01-01       25       45
                       2012-01-03       29       49

as is, `df.resample(&#39;2D&#39;, how=&#39;sum&#39;)` gets me

    TypeError: Only valid with DatetimeIndex or PeriodIndex

Fair enough, but I&#39;d sort of expect this to work:

    &gt;&gt;&gt; df.swaplevel(&#39;Date&#39;, &#39;State&#39;).resample(&#39;2D&#39;, how=&#39;sum&#39;)
    TypeError: Only valid with DatetimeIndex or PeriodIndex


at which point I&#39;m really running out of ideas... is there some way stack and unstack might be able to help me?

Content Type	Original Author	Original Content on Stackoverflow
Question	N. McA.	View Question on Stackoverflow
Solution 1 - Python	Andy Hayden	View Answer on Stackoverflow
Solution 2 - Python	piggybox	View Answer on Stackoverflow
Solution 3 - Python	herrfz	View Answer on Stackoverflow

Pandas - make a column dtype object or Factor

Python Problem Overview

Python Solutions

Solution 1 - Python

Update

Solution 2 - Python

Solution 3 - Python

iCloud and GEOResourceManifestServerRemoteProxy: Lost connection to geod warning

Events not being tracked in new Google Analytics (analytics.js) setup

Attributions