The return value of Series.describe() is a Series with the descriptive statistics. The dtype you see in the Series is not the dtype of the original column but the dtype of the statistics - which is float. The name of the result is price because that is set as the name of the Series autos["price"].

The info() method in pandas provides basic information about a DataFrame, including the data types of each column, the number of rows, and the memory usage. It also displays the index name and type. On the other hand, the describe() method provides detailed descriptive statistics about the numerical columns in a DataFrame, including count, mean, standard deviation, minimum value, 25th percentile, median (50th percentile), 75th percentile, and maximum value.

.describe is the bound method. It's bound to your dataframe, and the representation of a bound method includes the repr() output of whatever it is bound to.

You can see this at the start of the output:

<bound method NDFrame.describe of ...>

The rest is just the same string as what repr(data) produces.

Note that Python interactive interpreter always echoes the representation of whatever the last expression produced (unless it produced None). data.describe produces the bound method, data.describe() produces whatever that method was designed to produce.

You can create the same kind of output for any bound method:

>>> class Foo:
...     def __repr__(self):
...         return "[This is an instance of the Foo class]"
...     def bar(self):
...         return "This is what Foo().bar() produces"
...
>>> Foo()
[This is an instance of the Foo class]
>>> Foo().bar
<bound method Foo.bar of [This is an instance of the Foo class]>
>>> Foo().bar()
"This is what Foo().bar() produces"

Note that Foo() has a custom __repr__ method, which is what is called to produce the representation of an instance.

You can see the same kind of output (the representation of the whole dataframe) for any method on the dataframe you don't actually call, e.g. data.sum, data.query, data.pivot, or data.__repr__.

A bound method is part of the process by which Python passes in the instance as the first argument when you call it, the argument usually named self. It is basically a proxy object with references to the original function (data.describe.__func__) and the instance to pass in before all other arguments (data.describe.__self__). See the descriptor HOWTO for details on how binding works.

If you wanted to express the __repr__ implementation of a bound method as Python code, it would be:

def __repr__(self):
    return f"<bound method {self.__func__.__qualname__} of {self.__self__!r}>"

The info() and describe() functions in pandas are considered inspection functions because they provide valuable insights into the structure, content, and statistical summary of a DataFrame. The info() method in pandas provides basic information about a DataFrame, including the data types of each column, the number of rows, and the memory usage. It also displays the index name and type. On the other hand, the describe() method provides detailed descriptive statistics about the numerical columns in a DataFrame, including count, mean, standard deviation, minimum value, 25th percentile, median (50th percentile), 75th percentile, and maximum value.

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