Handling indexes with python

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ipdb> print(Y)                                                                                                                                

array([[263.71 ],

       [263.63 ],

       [263.475],

       [263.34 ],

       [262.725],

       [262.725],

       [262.75 ],

       [262.435],

       [262.585],

       [262.51 ],

       [262.63 ],

       [262.75 ],

       [262.87 ],

       [262.93 ],

       [263.055]])

I need to make some changes on the array for the purpose of a classification problem.

Suppose P_i a price inside the array. I need to apply the
log-returns (i.e. log(P_i/P_{i-1}) on that array.

Once the log-returns is applied, I need to change the values by 0. or 1.. If the value is non-zero, replace it by float(1), otherwise replace 0 by float(0).

I tried to handle the changes, but it is unclear which package to use and how to manage the indexes. As the base array is built with numpy, I thought maybe keep using it, but it is very confused in my head.

How can I code the two previous changes on the base array with python? What package?

edited Nov 21 at 18:42

asked Nov 21 at 18:30

user1050421

137

3

Really, it just sounds like you need a bit of education and practice on how to use arrays in Python.
– Robert Harvey♦
Nov 21 at 18:31

1

Possible duplicate of Logarithmic returns in pandas dataframe
– Conner
Nov 21 at 18:33

You should be able to do everything with numpy, no need to extra packages. For example, np.log(P[1:]/P[:-1]).
– hpaulj
Nov 21 at 18:38

add a comment |

up vote
-1
down vote

favorite

ipdb> print(Y)                                                                                                                                

array([[263.71 ],

       [263.63 ],

       [263.475],

       [263.34 ],

       [262.725],

       [262.725],

       [262.75 ],

       [262.435],

       [262.585],

       [262.51 ],

       [262.63 ],

       [262.75 ],

       [262.87 ],

       [262.93 ],

       [263.055]])

I need to make some changes on the array for the purpose of a classification problem.

Suppose P_i a price inside the array. I need to apply the
log-returns (i.e. log(P_i/P_{i-1}) on that array.

Once the log-returns is applied, I need to change the values by 0. or 1.. If the value is non-zero, replace it by float(1), otherwise replace 0 by float(0).

How can I code the two previous changes on the base array with python? What package?

edited Nov 21 at 18:42

asked Nov 21 at 18:30

user1050421

137

3

Really, it just sounds like you need a bit of education and practice on how to use arrays in Python.
– Robert Harvey♦
Nov 21 at 18:31

1

Possible duplicate of Logarithmic returns in pandas dataframe
– Conner
Nov 21 at 18:33

You should be able to do everything with numpy, no need to extra packages. For example, np.log(P[1:]/P[:-1]).
– hpaulj
Nov 21 at 18:38

add a comment |

up vote
-1
down vote

favorite

ipdb> print(Y)                                                                                                                                

array([[263.71 ],

       [263.63 ],

       [263.475],

       [263.34 ],

       [262.725],

       [262.725],

       [262.75 ],

       [262.435],

       [262.585],

       [262.51 ],

       [262.63 ],

       [262.75 ],

       [262.87 ],

       [262.93 ],

       [263.055]])

I need to make some changes on the array for the purpose of a classification problem.

Suppose P_i a price inside the array. I need to apply the
log-returns (i.e. log(P_i/P_{i-1}) on that array.

Once the log-returns is applied, I need to change the values by 0. or 1.. If the value is non-zero, replace it by float(1), otherwise replace 0 by float(0).

How can I code the two previous changes on the base array with python? What package?

edited Nov 21 at 18:42

asked Nov 21 at 18:30

user1050421

137

ipdb> print(Y)                                                                                                                                

array([[263.71 ],

       [263.63 ],

       [263.475],

       [263.34 ],

       [262.725],

       [262.725],

       [262.75 ],

       [262.435],

       [262.585],

       [262.51 ],

       [262.63 ],

       [262.75 ],

       [262.87 ],

       [262.93 ],

       [263.055]])

I need to make some changes on the array for the purpose of a classification problem.

Suppose P_i a price inside the array. I need to apply the
log-returns (i.e. log(P_i/P_{i-1}) on that array.

Once the log-returns is applied, I need to change the values by 0. or 1.. If the value is non-zero, replace it by float(1), otherwise replace 0 by float(0).

How can I code the two previous changes on the base array with python? What package?

python arrays numpy classification

edited Nov 21 at 18:42

asked Nov 21 at 18:30

user1050421

137

edited Nov 21 at 18:42

asked Nov 21 at 18:30

user1050421

137

edited Nov 21 at 18:42

asked Nov 21 at 18:30

user1050421

137

asked Nov 21 at 18:30

user1050421

137

asked Nov 21 at 18:30

user1050421

137

3

Really, it just sounds like you need a bit of education and practice on how to use arrays in Python.
– Robert Harvey♦
Nov 21 at 18:31

1

Possible duplicate of Logarithmic returns in pandas dataframe
– Conner
Nov 21 at 18:33

You should be able to do everything with numpy, no need to extra packages. For example, np.log(P[1:]/P[:-1]).
– hpaulj
Nov 21 at 18:38

add a comment |

3

Really, it just sounds like you need a bit of education and practice on how to use arrays in Python.
– Robert Harvey♦
Nov 21 at 18:31

1

Possible duplicate of Logarithmic returns in pandas dataframe
– Conner
Nov 21 at 18:33

You should be able to do everything with numpy, no need to extra packages. For example, np.log(P[1:]/P[:-1]).
– hpaulj
Nov 21 at 18:38

Really, it just sounds like you need a bit of education and practice on how to use arrays in Python.
– Robert Harvey♦
Nov 21 at 18:31

Possible duplicate of Logarithmic returns in pandas dataframe
– Conner
Nov 21 at 18:33

You should be able to do everything with numpy, no need to extra packages. For example, np.log(P[1:]/P[:-1]).
– hpaulj
Nov 21 at 18:38

add a comment |

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