Python for Finance: Analyze Big Financial Data

Out[8]: numbers             100

                                dtype:  int64

In  [ 9 ]:  df.apply(lambda x:  x   **   2 )        #   square  of  every   element

Out[9]:             numbers

                                a                       100

                                b                       400

                                c                       900

                                d                   1600

In general, you can implement the same vectorized operations on a DataFrame object as on

a NumPy ndarray object:

In  [ 10 ]: df  **   2       #  again   square, this    time    NumPy-like

Out[10]:                numbers

                                    a                       100

                                    b                       400

                                    c                       900

                                    d                   1600

Enlarging the DataFrame object in both dimensions is possible:

In  [ 11 ]: df[‘floats’]    =   (1.5,   2.5,    3.5,    4.5)

#   new column  is  generated

df

Out[11]:                numbers     floats

                                    a                           10                  1.5

                                    b                           20                  2.5

                                    c                           30                  3.5

                                    d                           40                  4.5

In  [ 12 ]: df[‘floats’]        #   selection   of  column

Out[12]:    a               1.5

                                    b               2.5

                                    c               3.5

                                    d               4.5

                                    Name:   floats, dtype:  float64

A whole DataFrame object can also be taken to define a new column. In such a case,

indices are aligned automatically:

In  [ 13 ]: df[‘names’] =   pd.DataFrame([‘Yves’,   ‘Guido’,    ‘Felix’,    ‘Francesc’],

index=[‘d’, ‘a’,    ‘b’,    ‘c’])

df

Out[13]:                numbers     floats                  names

                                    a                           10                  1.5                 Guido

                                    b                           20                  2.5                 Felix

                                    c                           30                  3.5     Francesc

                                    d                           40                  4.5                     Yves

Appending data works similarly. However, in the following example we see a side effect

that is usually to be avoided — the index gets replaced by a simple numbered index:

In  [ 14 ]: df.append({‘numbers’:    100 ,  ‘floats’:   5.75,   ‘names’:    ‘Henry’},

ignore_index=True)

#   temporary   object; df  not changed

Out[14]:                numbers     floats                  names

                                    0                           10              1.50                    Guido

                                    1                           20              2.50                    Felix

                                    2                           30              3.50        Francesc

                                    3                           40              4.50                        Yves

                                    4                       100             5.75                    Henry

It is often better to append a DataFrame object, providing the appropriate index

information. This preserves the index:

In  [ 15 ]: df  =   df.append(pd.DataFrame({‘numbers’:   100 ,  ‘floats’:   5.75,

‘names’:    ‘Henry’},   index=[‘z’,]))

df

Out[15]:                floats                  names       numbers

                                    a               1.50                    Guido                           10

                                    b               2.50                    Felix                           20