Python for Finance: Analyze Big Financial Data

The helper column has fulfilled its purpose and can now be deleted:

In  [ 71 ]: del es[‘DEL’]

es.info()

Out[71]:    <class  ‘pandas.core.frame.DataFrame’>

                                    DatetimeIndex:  7153    entries,    1986-12-31  00:00:00    to  2014-09-26  00:00:00

                                    Data    columns (total  8   columns):

                                    SX5P                7153    non-null    float64

                                    SX5E                7153    non-null    float64

                                    SXXP                7153    non-null    float64

                                    SXXE                7153    non-null    float64

                                    SXXF                7153    non-null    float64

                                    SXXA                7153    non-null    float64

                                    DK5F                7153    non-null    float64

                                    DKXF                7153    non-null    float64

                                    dtypes: float64(8)

Equipped with the knowledge about the structure of the EURO STOXX 50 data set, we

can also use the advanced capabilities of the read_csv function to make the import more

compact and efficient:

In  [ 72 ]: cols    =   [‘SX5P’,    ‘SX5E’, ‘SXXP’, ‘SXXE’, ‘SXXF’,

‘SXXA’, ‘DK5F’, ‘DKXF’]

es  =   pd.read_csv(es_url, index_col= 0 ,  parse_dates=True,

sep=‘;’,    dayfirst=True,  header=None,

skiprows= 4 ,   names=cols)

In  [ 73 ]: es.tail()

Out[73]:                                                                SX5P                    SX5E                SXXP                SXXE                SXXF                SXXA                    DK5F

                                            DKXF

                                    2014-09-22      3096.02     3257.48     346.69      325.68      403.16      357.08      9703.33

                                    564.81

                                    2014-09-23      3057.89     3205.93     341.89      320.72      397.96      352.56      9602.32

                                    558.35

                                    2014-09-24      3086.12     3244.01     344.35      323.42      400.58      354.72      9628.84

                                    559.83

                                    2014-09-25      3059.01     3202.31     341.44      319.77      396.90      352.58      9537.95

                                    555.51

                                    2014-09-26      3063.71     3219.58     342.30      321.39      398.33      352.71      9558.51

                                    556.57

Fortunately, the VSTOXX data set is already in a form such that it can be imported a bit

more easily into a DataFrame object:

In  [ 74 ]: vs  =   pd.read_csv(‘./data/vs.txt’,    index_col= 0 ,  header= 2 ,

parse_dates=True,   sep=‘,’,    dayfirst=True)

vs.info()

Out[74]:    <class  ‘pandas.core.frame.DataFrame’>

                                    DatetimeIndex:  4010    entries,    1999-01-04  00:00:00    to  2014-09-26  00:00:00

                                    Data    columns (total  9   columns):

                                    V2TX                4010    non-null    float64

                                    V6I1                3591    non-null    float64

                                    V6I2                4010    non-null    float64

                                    V6I3                3960    non-null    float64

                                    V6I4                4010    non-null    float64

                                    V6I5                4010    non-null    float64

                                    V6I6                3995    non-null    float64

                                    V6I7                4010    non-null    float64

                                    V6I8                3999    non-null    float64

                                    dtypes: float64(9)

Table 6-6 contains the parameters of this important import function. There are a multitude

of parameters, the majority of which default to None; object, of course, is nondefault and

has to be specified in any case.

Table 6-6. Parameters of read_csv function

Parameter Format Description