How to exclude rows based on multi column value conditions in pandas dataframe?Add one row to pandas DataFrameSelecting multiple columns in a pandas dataframeAdding new column to existing DataFrame in Python pandasDelete column from pandas DataFrameHow to drop rows of Pandas DataFrame whose value in certain columns is NaNHow do I get the row count of a pandas DataFrame?How to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasDeleting DataFrame row in Pandas based on column valueGet list from pandas DataFrame column headers
Would a bit of grease on overhead door cables or bearings cause the springs to break?
Converting 3x7 to a 1x7. Is it possible with only existing parts?
Nth term of Van Eck Sequence
What is wind "CALM"?
Struggling to present results from long papers in short time slots
Why is Skinner so awkward in Hot Fuzz?
What things do I only get a limited opportunity to take photos of?
Idiom for 'person who gets violent when drunk"
Manager wants to hire me; HR does not. How to proceed?
Dedicated bike GPS computer over smartphone
Is it a good security practice to force employees hide their employer to avoid being targeted?
How drastic would the result be if I use fasta or reference assembly from ucsc and gtf from gencode?
Can Dive Down protect a creature against Pacifism?
Is it true that "only photographers care about noise"?
Can an open source licence be revoked if it violates employer's IP?
What did the 8086 (and 8088) do upon encountering an illegal instruction?
How many times to repeat an event with known probability before it has occurred a number of times
Boss making me feel guilty for leaving the company at the end of my internship
Will users know a CardView is clickable
How can religions without a hell discourage evil-doing?
Are soroban (Japanese abacus) classes worth doing?
Does every chapter have to "blow the reader away" so to speak?
How long would it take for sucrose to undergo hydrolysis in boiling water?
Is fission/fusion to iron the most efficient way to convert mass to energy?
How to exclude rows based on multi column value conditions in pandas dataframe?
Add one row to pandas DataFrameSelecting multiple columns in a pandas dataframeAdding new column to existing DataFrame in Python pandasDelete column from pandas DataFrameHow to drop rows of Pandas DataFrame whose value in certain columns is NaNHow do I get the row count of a pandas DataFrame?How to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasDeleting DataFrame row in Pandas based on column valueGet list from pandas DataFrame column headers
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
There's pandas dataframe ad below:
email score
a@domain.com A
b@domain.com A
c@domain.com C
d@domain.com B
I want to exclude rows with email a@domain.com and c@domain.com.Expect result as below:
email score
b@domain.com A
d@domain.com B
I tried 3 times but failed:
df=df[df.email !='a@domain.com' & df.email !='c@domain.com' ]
TypeError: cannot compare a dtyped [object] array with a scalar of type [bool]
df=df[df.email !='a@domain.com' && df.email !='c@domain.com' ]
SyntaxError: invalid syntax
df=df[df.email !='a@domain.com' | 'c@domain.com' ]
TypeError: unsupported operand type(s) for |: 'str' and 'str'
What's the problem?
python pandas
asked Mar 25 at 2:33
kittygirlkittygirl
597618
add a comment |
There's pandas dataframe ad below:
email score
a@domain.com A
b@domain.com A
c@domain.com C
d@domain.com B
I want to exclude rows with email a@domain.com and c@domain.com.Expect result as below:
email score
b@domain.com A
d@domain.com B
I tried 3 times but failed:
df=df[df.email !='a@domain.com' & df.email !='c@domain.com' ]
TypeError: cannot compare a dtyped [object] array with a scalar of type [bool]
df=df[df.email !='a@domain.com' && df.email !='c@domain.com' ]
SyntaxError: invalid syntax
df=df[df.email !='a@domain.com' | 'c@domain.com' ]
TypeError: unsupported operand type(s) for |: 'str' and 'str'
What's the problem?
python pandas
asked Mar 25 at 2:33
kittygirlkittygirl
597618
add a comment |
There's pandas dataframe ad below:
email score
a@domain.com A
b@domain.com A
c@domain.com C
d@domain.com B
I want to exclude rows with email a@domain.com and c@domain.com.Expect result as below:
email score
b@domain.com A
d@domain.com B
I tried 3 times but failed:
df=df[df.email !='a@domain.com' & df.email !='c@domain.com' ]
TypeError: cannot compare a dtyped [object] array with a scalar of type [bool]
df=df[df.email !='a@domain.com' && df.email !='c@domain.com' ]
SyntaxError: invalid syntax
df=df[df.email !='a@domain.com' | 'c@domain.com' ]
TypeError: unsupported operand type(s) for |: 'str' and 'str'
What's the problem?
python pandas
asked Mar 25 at 2:33
kittygirlkittygirl
597618
There's pandas dataframe ad below:
email score
a@domain.com A
b@domain.com A
c@domain.com C
d@domain.com B
I want to exclude rows with email a@domain.com and c@domain.com.Expect result as below:
email score
b@domain.com A
d@domain.com B
I tried 3 times but failed:
df=df[df.email !='a@domain.com' & df.email !='c@domain.com' ]
TypeError: cannot compare a dtyped [object] array with a scalar of type [bool]
df=df[df.email !='a@domain.com' && df.email !='c@domain.com' ]
SyntaxError: invalid syntax
df=df[df.email !='a@domain.com' | 'c@domain.com' ]
TypeError: unsupported operand type(s) for |: 'str' and 'str'
What's the problem?
python pandas
python pandas
asked Mar 25 at 2:33
kittygirlkittygirl
597618
asked Mar 25 at 2:33
kittygirlkittygirl
597618
edited Mar 25 at 2:34
kittygirl
asked Mar 25 at 2:33
kittygirlkittygirl
597618
asked Mar 25 at 2:33
kittygirlkittygirl
597618
asked Mar 25 at 2:33
kittygirlkittygirl
597618
597618
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You have to surround it by parenthesis:
df = df[(df.email != 'a@domain.com') & (df.email != 'c@domain.com')]
That said, it would be easier with isin:
df = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]
And now:
print(df)
Is gonna be the expected output.
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
1
what's~indf = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]?
– kittygirl
Mar 25 at 2:36
@kittygirl theisinmeans if it contains any element in the list, but since those are the ones you don't want, use that sign to get the opposite of it and remove those.
– U9-Forward
Mar 25 at 2:37
1
Why not!but~?
– kittygirl
Mar 25 at 2:39
@kittygirl That's a very good point, but in pandas, it is different, you have to use~
– U9-Forward
Mar 25 at 2:40
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55330568%2fhow-to-exclude-rows-based-on-multi-column-value-conditions-in-pandas-dataframe%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You have to surround it by parenthesis:
df = df[(df.email != 'a@domain.com') & (df.email != 'c@domain.com')]
That said, it would be easier with isin:
df = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]
And now:
print(df)
Is gonna be the expected output.
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
1
what's~indf = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]?
– kittygirl
Mar 25 at 2:36
@kittygirl theisinmeans if it contains any element in the list, but since those are the ones you don't want, use that sign to get the opposite of it and remove those.
– U9-Forward
Mar 25 at 2:37
1
Why not!but~?
– kittygirl
Mar 25 at 2:39
@kittygirl That's a very good point, but in pandas, it is different, you have to use~
– U9-Forward
Mar 25 at 2:40
add a comment |
You have to surround it by parenthesis:
df = df[(df.email != 'a@domain.com') & (df.email != 'c@domain.com')]
That said, it would be easier with isin:
df = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]
And now:
print(df)
Is gonna be the expected output.
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
1
what's~indf = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]?
– kittygirl
Mar 25 at 2:36
@kittygirl theisinmeans if it contains any element in the list, but since those are the ones you don't want, use that sign to get the opposite of it and remove those.
– U9-Forward
Mar 25 at 2:37
1
Why not!but~?
– kittygirl
Mar 25 at 2:39
@kittygirl That's a very good point, but in pandas, it is different, you have to use~
– U9-Forward
Mar 25 at 2:40
add a comment |
You have to surround it by parenthesis:
df = df[(df.email != 'a@domain.com') & (df.email != 'c@domain.com')]
That said, it would be easier with isin:
df = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]
And now:
print(df)
Is gonna be the expected output.
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
You have to surround it by parenthesis:
df = df[(df.email != 'a@domain.com') & (df.email != 'c@domain.com')]
That said, it would be easier with isin:
df = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]
And now:
print(df)
Is gonna be the expected output.
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
answered Mar 25 at 2:34
U9-ForwardU9-Forward
21.7k51847
21.7k51847
1
what's~indf = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]?
– kittygirl
Mar 25 at 2:36
@kittygirl theisinmeans if it contains any element in the list, but since those are the ones you don't want, use that sign to get the opposite of it and remove those.
– U9-Forward
Mar 25 at 2:37
1
Why not!but~?
– kittygirl
Mar 25 at 2:39
@kittygirl That's a very good point, but in pandas, it is different, you have to use~
– U9-Forward
Mar 25 at 2:40
add a comment |
1
what's~indf = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]?
– kittygirl
Mar 25 at 2:36
@kittygirl theisinmeans if it contains any element in the list, but since those are the ones you don't want, use that sign to get the opposite of it and remove those.
– U9-Forward
Mar 25 at 2:37
1
Why not!but~?
– kittygirl
Mar 25 at 2:39
@kittygirl That's a very good point, but in pandas, it is different, you have to use~
– U9-Forward
Mar 25 at 2:40
1
1
what's
~ in df = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]?– kittygirl
Mar 25 at 2:36
what's
~ in df = df[~df.email.isin(['a@domain.com', 'c@domain.com'])]?– kittygirl
Mar 25 at 2:36
@kittygirl the
isin means if it contains any element in the list, but since those are the ones you don't want, use that sign to get the opposite of it and remove those.– U9-Forward
Mar 25 at 2:37
@kittygirl the
isin means if it contains any element in the list, but since those are the ones you don't want, use that sign to get the opposite of it and remove those.– U9-Forward
Mar 25 at 2:37
1
1
Why not
! but ~?– kittygirl
Mar 25 at 2:39
Why not
! but ~?– kittygirl
Mar 25 at 2:39
@kittygirl That's a very good point, but in pandas, it is different, you have to use
~– U9-Forward
Mar 25 at 2:40
@kittygirl That's a very good point, but in pandas, it is different, you have to use
~– U9-Forward
Mar 25 at 2:40
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55330568%2fhow-to-exclude-rows-based-on-multi-column-value-conditions-in-pandas-dataframe%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
