pd.shape will give you the number of rows and columns present in the dataFrame.

where, df.shape[0] will give you the total rows present in the dataFrame.

and, df.shape[1] will give you the number of columns present in the dataFrame.

Example:

df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011'], 
                    'Phrases':['I have a cool family', 'I like avocados', 'I would like to go to school']})


df
Out[26]: 
        Date                       Phrases
0  10/2/2011          I have a cool family
1  11/2/2011               I like avocados
2  12/2/2011  I would like to go to school

df.shape
Out[27]: (3, 2)

df.shape[0]   #number of rows
Out[28]: 3

df.shape[1]   #number of columns
Out[29]: 2

Get into an interactive Python session with numpy and pandas, and experiment

Make a dataframe:

In [394]: df=pd.DataFrame(np.eye(3))                                            
In [395]: df                                                                    
Out[395]: 
     0    1    2
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0

Check its shape. That's a tuple (basic Python object):

In [396]: df.shape                                                              
Out[396]: (3, 3)
In [397]: df.shape[0]     # first element of the tuple                                                          
Out[397]: 3

Repeat with the shape parameter is just like using the number 3:

In [398]: np.repeat('red', df.shape[0])                                         
Out[398]: array(['red', 'red', 'red'], dtype='<U3')

Pandas and numpy are running in Python. So the regular evaluation order of Python applies.