Python for Finance: Analyze Big Financial Data

Q2 -1.225155 0.918877 0.580972 -0.130118

Q3 0.465366 -0.212719 0.624617 -0.568196

In  [ 41 ]: groups.max()

Out[41]:                                                            No1                         No2                         No3                         No4

                                    Quarter

                                    Q1                      -0.155881       1.065173        1.037444        1.301174

                                    Q2                      -0.777546       1.730278        1.227124        0.979389

                                    Q3                          1.040318    -0.128799       1.565701        0.414084

In  [ 42 ]: groups.size()

Out[42]:    Quarter

                                    Q1                                  3

                                    Q2                                  3

                                    Q3                                  3

                                    dtype:  int64

Grouping can also be done with multiple columns. To this end, we add another column,

indicating whether the month of the index date is odd or even:

In  [ 43 ]: df[‘Odd_Even’]  =   [‘Odd’, ‘Even’, ‘Odd’,  ‘Even’, ‘Odd’,  ‘Even’,

‘Odd’,  ‘Even’, ‘Odd’]

This additional information can now be used for a grouping based on two columns

simultaneously:

In  [ 44 ]: groups  =   df.groupby([‘Quarter’,  ‘Odd_Even’])

In  [ 45 ]: groups.size()

Out[45]:    Quarter     Odd_Even

                                    Q1                          Even                                1

                                                                        Odd                                 2

                                    Q2                          Even                                2

                                                                        Odd                                 1

                                    Q3                          Even                                1

                                                                        Odd                                 2

                                    dtype:  int64

In  [ 46 ]: groups.mean()

Out[46]:                                                                                                No1                         No2                         No3                         No4

                                    Quarter Odd_Even

                                    Q1                      Even                    -0.788818   -0.985819       0.403796    -1.753784

                                                                    Odd                     -0.446592   -0.343749       0.555425        0.450190

                                    Q2                      Even                    -0.955902       1.566050        0.822119        0.581734

                                                                    Odd                     -1.763660   -0.375469       0.098678    -1.553824

                                    Q3                      Even                    -0.103058   -0.366170   -0.478036   -0.032810

                                                                    Odd                         0.749578    -0.135993       1.175944    -0.835890

This concludes the introduction into pandas and the use of DataFrame objects. Subsequent

sections apply this tool set to real-world financial data.