Python If x==True, and if y==False then start array true

I have two dataframes.

PPASbool=

0   True

1   True

2   False

3   True

4   False



CPASbool=

0   True

1   False

2   False

3   False

4   True

I tried this below, but I received an error

 File "Typology.py", line 66, in <module>

    if PPASbool==True :

  File "C:UsersJimAnaconda3libsite-packagespandascoregeneric.py", 

line 1573, in __nonzero__

    .format(self.__class__.__name__))

 ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().



if PPASbool==True :

    if CPASbool == False :

        PeerNoCo=True

    else:

        PeerNoCo=False

else:

    PeerNoCo=False

I want to put True in a new Array called PeerNoCo if PPASbool is true and CPAS is False.

Expected output

PeerNoCo=

0   False

1   True

2   False

3   True

4   False

asked Nov 20 '18 at 19:04

Jim Hubbard

194

5

This is simply a logical and with the bitwise NOT of one of the series. a & ~b if a and b are the two series.

– user3483203
Nov 20 '18 at 19:06

2

The "ambiguous" value error happens when you try to evaluate a series to a signle boolean. For example if you have a series, A that equals to T, F, T. A == F is ambiguous, because some of A is False and other parts are True. On way to add more clarity depending on logic is to use all or any. if (A == True).all() or (A == True).any() to return a single True or False.

– Scott Boston
Nov 20 '18 at 19:10

Just to add, typically when trying to do logical if else, or if, elif else statements row-wise you would look to numpy.where or numpy.select, respectively. Though @user3483203 s solution is the most appropriate in this case.

– ALollz
Nov 20 '18 at 19:15

add a comment |

I have two dataframes.

PPASbool=

0   True

1   True

2   False

3   True

4   False



CPASbool=

0   True

1   False

2   False

3   False

4   True

I tried this below, but I received an error

 File "Typology.py", line 66, in <module>

    if PPASbool==True :

  File "C:UsersJimAnaconda3libsite-packagespandascoregeneric.py", 

line 1573, in __nonzero__

    .format(self.__class__.__name__))

 ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().



if PPASbool==True :

    if CPASbool == False :

        PeerNoCo=True

    else:

        PeerNoCo=False

else:

    PeerNoCo=False

I want to put True in a new Array called PeerNoCo if PPASbool is true and CPAS is False.

Expected output

PeerNoCo=

0   False

1   True

2   False

3   True

4   False

asked Nov 20 '18 at 19:04

Jim Hubbard

194

5

This is simply a logical and with the bitwise NOT of one of the series. a & ~b if a and b are the two series.

– user3483203
Nov 20 '18 at 19:06

2

The "ambiguous" value error happens when you try to evaluate a series to a signle boolean. For example if you have a series, A that equals to T, F, T. A == F is ambiguous, because some of A is False and other parts are True. On way to add more clarity depending on logic is to use all or any. if (A == True).all() or (A == True).any() to return a single True or False.

– Scott Boston
Nov 20 '18 at 19:10

Just to add, typically when trying to do logical if else, or if, elif else statements row-wise you would look to numpy.where or numpy.select, respectively. Though @user3483203 s solution is the most appropriate in this case.

– ALollz
Nov 20 '18 at 19:15

add a comment |

I have two dataframes.

PPASbool=

0   True

1   True

2   False

3   True

4   False



CPASbool=

0   True

1   False

2   False

3   False

4   True

I tried this below, but I received an error

 File "Typology.py", line 66, in <module>

    if PPASbool==True :

  File "C:UsersJimAnaconda3libsite-packagespandascoregeneric.py", 

line 1573, in __nonzero__

    .format(self.__class__.__name__))

 ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().



if PPASbool==True :

    if CPASbool == False :

        PeerNoCo=True

    else:

        PeerNoCo=False

else:

    PeerNoCo=False

I want to put True in a new Array called PeerNoCo if PPASbool is true and CPAS is False.

Expected output

PeerNoCo=

0   False

1   True

2   False

3   True

4   False

asked Nov 20 '18 at 19:04

Jim Hubbard

194

I have two dataframes.

PPASbool=

0   True

1   True

2   False

3   True

4   False



CPASbool=

0   True

1   False

2   False

3   False

4   True

I tried this below, but I received an error

 File "Typology.py", line 66, in <module>

    if PPASbool==True :

  File "C:UsersJimAnaconda3libsite-packagespandascoregeneric.py", 

line 1573, in __nonzero__

    .format(self.__class__.__name__))

 ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().



if PPASbool==True :

    if CPASbool == False :

        PeerNoCo=True

    else:

        PeerNoCo=False

else:

    PeerNoCo=False

I want to put True in a new Array called PeerNoCo if PPASbool is true and CPAS is False.

Expected output

PeerNoCo=

0   False

1   True

2   False

3   True

4   False

python python-3.x pandas numpy

asked Nov 20 '18 at 19:04

Jim Hubbard

194

asked Nov 20 '18 at 19:04

Jim Hubbard

194

asked Nov 20 '18 at 19:04

Jim Hubbard

194

asked Nov 20 '18 at 19:04

Jim Hubbard

194

asked Nov 20 '18 at 19:04

Jim Hubbard

194

5

This is simply a logical and with the bitwise NOT of one of the series. a & ~b if a and b are the two series.

– user3483203
Nov 20 '18 at 19:06

2

The "ambiguous" value error happens when you try to evaluate a series to a signle boolean. For example if you have a series, A that equals to T, F, T. A == F is ambiguous, because some of A is False and other parts are True. On way to add more clarity depending on logic is to use all or any. if (A == True).all() or (A == True).any() to return a single True or False.

– Scott Boston
Nov 20 '18 at 19:10

Just to add, typically when trying to do logical if else, or if, elif else statements row-wise you would look to numpy.where or numpy.select, respectively. Though @user3483203 s solution is the most appropriate in this case.

– ALollz
Nov 20 '18 at 19:15

add a comment |

5

This is simply a logical and with the bitwise NOT of one of the series. a & ~b if a and b are the two series.

– user3483203
Nov 20 '18 at 19:06

2

The "ambiguous" value error happens when you try to evaluate a series to a signle boolean. For example if you have a series, A that equals to T, F, T. A == F is ambiguous, because some of A is False and other parts are True. On way to add more clarity depending on logic is to use all or any. if (A == True).all() or (A == True).any() to return a single True or False.

– Scott Boston
Nov 20 '18 at 19:10

Just to add, typically when trying to do logical if else, or if, elif else statements row-wise you would look to numpy.where or numpy.select, respectively. Though @user3483203 s solution is the most appropriate in this case.

– ALollz
Nov 20 '18 at 19:15

This is simply a logical and with the bitwise NOT of one of the series. a & ~b if a and b are the two series.

– user3483203
Nov 20 '18 at 19:06

The "ambiguous" value error happens when you try to evaluate a series to a signle boolean. For example if you have a series, A that equals to T, F, T. A == F is ambiguous, because some of A is False and other parts are True. On way to add more clarity depending on logic is to use all or any. if (A == True).all() or (A == True).any() to return a single True or False.

– Scott Boston
Nov 20 '18 at 19:10

Just to add, typically when trying to do logical if else, or if, elif else statements row-wise you would look to numpy.where or numpy.select, respectively. Though @user3483203 s solution is the most appropriate in this case.

– ALollz
Nov 20 '18 at 19:15

add a comment |

1 Answer
1

active

oldest

votes

2 things:

you don't have to say == True. If is automatically testing a condition. If you want to condition on variable temp being True, you can just do if temp:

You're trying to do a conditional on a series. You can iterate over your values. If you want to maintain indices, you can use enumerate().

EDIT:

As one of your comments have mentioned, the easiest way to achieve what you want is just to store PPASbool & ~CPAS.

edited Nov 20 '18 at 19:18

answered Nov 20 '18 at 19:09

John Rouhana

1779

1

I generally agree that ==True is redundant. However if the dtype is not bool (perhaps it was reindexed and now contains NaN and is object) then it's necessary.

– ALollz
Nov 20 '18 at 19:17

1

Definitely valid- thanks for pointing that out.

– John Rouhana
Nov 20 '18 at 19:19

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

2 things:

you don't have to say == True. If is automatically testing a condition. If you want to condition on variable temp being True, you can just do if temp:

You're trying to do a conditional on a series. You can iterate over your values. If you want to maintain indices, you can use enumerate().

EDIT:

As one of your comments have mentioned, the easiest way to achieve what you want is just to store PPASbool & ~CPAS.

edited Nov 20 '18 at 19:18

answered Nov 20 '18 at 19:09

John Rouhana

1779

1

I generally agree that ==True is redundant. However if the dtype is not bool (perhaps it was reindexed and now contains NaN and is object) then it's necessary.

– ALollz
Nov 20 '18 at 19:17

1

Definitely valid- thanks for pointing that out.

– John Rouhana
Nov 20 '18 at 19:19

add a comment |

2 things:

you don't have to say == True. If is automatically testing a condition. If you want to condition on variable temp being True, you can just do if temp:

You're trying to do a conditional on a series. You can iterate over your values. If you want to maintain indices, you can use enumerate().

EDIT:

As one of your comments have mentioned, the easiest way to achieve what you want is just to store PPASbool & ~CPAS.

edited Nov 20 '18 at 19:18

answered Nov 20 '18 at 19:09

John Rouhana

1779

1

I generally agree that ==True is redundant. However if the dtype is not bool (perhaps it was reindexed and now contains NaN and is object) then it's necessary.

– ALollz
Nov 20 '18 at 19:17

1

Definitely valid- thanks for pointing that out.

– John Rouhana
Nov 20 '18 at 19:19

add a comment |

2 things:

you don't have to say == True. If is automatically testing a condition. If you want to condition on variable temp being True, you can just do if temp:

You're trying to do a conditional on a series. You can iterate over your values. If you want to maintain indices, you can use enumerate().

EDIT:

As one of your comments have mentioned, the easiest way to achieve what you want is just to store PPASbool & ~CPAS.

edited Nov 20 '18 at 19:18

answered Nov 20 '18 at 19:09

John Rouhana

1779

2 things:

you don't have to say == True. If is automatically testing a condition. If you want to condition on variable temp being True, you can just do if temp:

You're trying to do a conditional on a series. You can iterate over your values. If you want to maintain indices, you can use enumerate().

EDIT:

As one of your comments have mentioned, the easiest way to achieve what you want is just to store PPASbool & ~CPAS.

edited Nov 20 '18 at 19:18

answered Nov 20 '18 at 19:09

John Rouhana

1779

edited Nov 20 '18 at 19:18

answered Nov 20 '18 at 19:09

John Rouhana

1779

answered Nov 20 '18 at 19:09

John Rouhana

1779

answered Nov 20 '18 at 19:09

John Rouhana

1779

1

I generally agree that ==True is redundant. However if the dtype is not bool (perhaps it was reindexed and now contains NaN and is object) then it's necessary.

– ALollz
Nov 20 '18 at 19:17

1

Definitely valid- thanks for pointing that out.

– John Rouhana
Nov 20 '18 at 19:19

add a comment |

1

I generally agree that ==True is redundant. However if the dtype is not bool (perhaps it was reindexed and now contains NaN and is object) then it's necessary.

– ALollz
Nov 20 '18 at 19:17

1

Definitely valid- thanks for pointing that out.

– John Rouhana
Nov 20 '18 at 19:19

I generally agree that ==True is redundant. However if the dtype is not bool (perhaps it was reindexed and now contains NaN and is object) then it's necessary.

– ALollz
Nov 20 '18 at 19:17

Definitely valid- thanks for pointing that out.

– John Rouhana
Nov 20 '18 at 19:19

add a comment |

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