Pandas 'describe' is not returning summary of all columns

I am running 'describe()' on a dataframe and getting summaries of only int columns (pandas 14.0).

The documentation says that for object columns frequency of most common value, and additional statistics would be returned. What could be wrong? (no error message is returned by the way)

Edit:

I think it's how the function is set to behave on mixed column types in a dataframe. Although the documentation fails to mention it.

Example code:

df_test = pd.DataFrame({'$a':[1,2], '$b': [10,20]})
df_test.dtypes
df_test.describe()
df_test['$a'] = df_test['$a'].astype(str)
df_test.describe()
df_test['$a'].describe()
df_test['$b'].describe()

My ugly work around in the meanwhile:

def my_df_describe(df):
    objects = []
    numerics = []
    for c in df:
        if (df[c].dtype == object):
            objects.append(c)
        else:
            numerics.append(c)

    return df[numerics].describe(), df[objects].describe()

As of pandas v15.0, use the parameter, DataFrame.describe(include = 'all') to get a summary of all the columns when the dataframe has mixed column types. The default behavior is to only provide a summary for the numerical columns.

Example:

In[1]:

df = pd.DataFrame({'$a':['a', 'b', 'c', 'd', 'a'], '$b': np.arange(5)})
df.describe(include = 'all')

Out[1]:

        $a    $b
count   5   5.000000
unique  4   NaN
top     a   NaN
freq    2   NaN
mean    NaN 2.000000
std     NaN 1.581139
min     NaN 0.000000
25%     NaN 1.000000
50%     NaN 2.000000
75%     NaN 3.000000
max     NaN 4.000000

The numerical columns will have NaNs for summary statistics pertaining to objects (strings) and vice versa.

Summarizing only numerical or object columns

1. To call describe() on just the numerical columns use describe(include = [np.number])

2. To call describe() on just the objects (strings) using describe(include = ['O']).

   In[2]:
   
   df.describe(include = [np.number])
   
   Out[3]:
   
            $b
   count   5.000000
   mean    2.000000
   std     1.581139
   min     0.000000
   25%     1.000000
   50%     2.000000
   75%     3.000000
   max     4.000000
   
   In[3]:
   
   df.describe(include = ['O'])
   
   Out[3]:
   
       $a
   count   5
   unique  4
   top     a
   freq    2
   

pd.options.display.max_columns = DATA.shape[1] will work.

Here DATA is a 2d matrix, and above code will display stats vertically.