TypeError: cannot concatenate 'str' and 'float' objects : pandas
<p>I'm trying to work on a data science problem using pandas. My dataset contains the columns : "country", "conversion", "test", "userid" etc. In the country column, there are around 10 countries. "test" column has values 0 and 1 denoting two types of tests: control for 0 and experiment for 1. "conversion" also has values 0 and 1 which denote whether the person has converted or not. </p>

<p>I want to groupby country and compute p-value and mean of test==0 and test==1 for every group. I am trying to use the following function, however it throws an error, "TypeError: cannot concatenate 'str' and 'float' objects". Can someone please shed light on this? </p>

<pre><code>def f(x):
        control = x.loc[(x.test==0)]
        test = x.loc[(x.test==1)]
        p_value = stats.ttest_ind(control,test)[0]
        control_mean = control['conversion'].mean()
        test_mean = test['conversion'].mean()
        return pd.Series({'p_value': p_value, 'conversion_test': test_mean, 'conversion_control': control_mean})     

bycountry = data1.groupby('country').apply(f) 
bycountry = bycountry.reset_index(level='None')
bycountry
</code></pre>

<p>Complete error message:</p>

<pre><code>TypeError                                 Traceback (most recent call last)
&lt;ipython-input-495-bd6227878520&gt; in &lt;module&gt;()
      7     return pd.Series({'p_value': p_value, 'conversion_test': test_mean, 'conversion_control': control_mean})
      8 
----&gt; 9 bycountry = data1.groupby("country").apply(f)
     10 bycountry = bycountry.reset_index(level='None')
     11 bycountry

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\pandas\core\groupby.pyc in apply(self, func, *args, **kwargs)
    649         # ignore SettingWithCopy here in case the user mutates
    650         with option_context('mode.chained_assignment', None):
--&gt; 651             return self._python_apply_general(f)
    652 
    653     def _python_apply_general(self, f):

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\pandas\core\groupby.pyc in _python_apply_general(self, f)
    653     def _python_apply_general(self, f):
    654         keys, values, mutated = self.grouper.apply(f, self._selected_obj,
--&gt; 655                                                    self.axis)
    656 
    657         return self._wrap_applied_output(

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\pandas\core\groupby.pyc in apply(self, f, data, axis)
   1525             # group might be modified
   1526             group_axes = _get_axes(group)
-&gt; 1527             res = f(group)
   1528             if not _is_indexed_like(res, group_axes):
   1529                 mutated = True

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\pandas\core\groupby.pyc in f(g)
    645         @wraps(func)
    646         def f(g):
--&gt; 647             return func(g, *args, **kwargs)
    648 
    649         # ignore SettingWithCopy here in case the user mutates

&lt;ipython-input-495-bd6227878520&gt; in f(x)
      2     control = x.loc[(x.test==0)]
      3     test = x.loc[(x.test==1)]
----&gt; 4     p_value = stats.ttest_ind(control,test)[0]
      5     control_mean = control['conversion'].mean()
      6     test_mean = test['conversion'].mean()

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\scipy\stats\stats.pyc in ttest_ind(a, b, axis, equal_var, nan_policy)
   3865         return Ttest_indResult(np.nan, np.nan)
   3866 
-&gt; 3867     v1 = np.var(a, axis, ddof=1)
   3868     v2 = np.var(b, axis, ddof=1)
   3869     n1 = a.shape[axis]

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\numpy\core\fromnumeric.pyc in var(a, axis, dtype, out, ddof, keepdims)
   3098 
   3099     return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
-&gt; 3100                          keepdims=keepdims)

C:\Users\SnehaPriya\Anaconda2\lib\site-packages\numpy\core\_methods.pyc in _var(a, axis, dtype, out, ddof, keepdims)
     89     # Note that if dtype is not of inexact type then arraymean will
     90     # not be either.
---&gt; 91     arrmean = umr_sum(arr, axis, dtype, keepdims=True)
     92     if isinstance(arrmean, mu.ndarray):
     93         arrmean = um.true_divide(

TypeError: cannot concatenate 'str' and 'float' objects
</code></pre>

<p>output of df.dtypes:</p>

<pre><code>user_id                      int64
date                datetime64[ns]
source                      object
device                      object
browser_language            object
ads_channel                 object
browser                     object
conversion                   int64
test                         int64
sex                         object
age                        float64
country                     object
dtype: object
</code></pre>