Is there a way to drop duplicated rows based on an unhashable column?how to impute missing values on numpy...
How can I improve my fireworks photography?
En Passant For Beginners
Does the "particle exchange" operator have any validity?
Why don't I see the difference between two different files in insert mode in vim?
Why do members of Congress in committee hearings ask witnesses the same question multiple times?
What to do when being responsible for data protection in your lab, yet advice is ignored?
Why is working on the same position for more than 15 years not a red flag?
How to avoid being sexist when trying to employ someone to function in a very sexist environment?
Is it a fallacy if someone claims they need an explanation for every word of your argument to the point where they don't understand common terms?
How to remove trailing forward slash
Knowing when to use pictures over words
integral inequality of length of curve
Jumping Numbers
What's the most convenient time of year in the USA to end the world?
Are there any outlying considerations if I treat donning a shield as an object interaction during the first round of combat?
Manipulating a general length function
I am on the US no-fly list. What can I do in order to be allowed on flights which go through US airspace?
Issues with new Macs: hardware makes them difficult to use … what options might be available in the future?
Can polymorphing monsters spam their ability to effectively give themselves a massive health pool?
Is there hidden data in this .blend file? Trying to minimize the file size
Reference on complex cobordism
Do authors have to be politically correct in article-writing?
page split between longtable caption and table
Rear brake cable temporary fix possible?
Is there a way to drop duplicated rows based on an unhashable column?
how to impute missing values on numpy array created by train_test_split from pandas.DataFrame?How do I convert a pandas dataframe to a 1d array?How to change a cell in Pandas dataframe with respective frequency of the cell in respective columnPopulate column based on previous row with a twistCounting the occurrence of each string in a pandas dataframe columnCreate new data frames from existing data frame based on unique column valuesHow to statistically prove that a column in a dataframe is not neededShould I use pandas get_dummies and create additional columns or use my own encoding code that keeps 1 column?How can I merge 2+ DataFrame objects without duplicating column names?operations on multiple entries in one column based on conditions meet from multiple column entries
$begingroup$
i have a pandas dataframe df with one column z filled with set values
i want to drop duplicated rows where 2 rows are considered duplicated version of one another when they have same column z values ( which are sets ).
import pandas as pd
lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } ) , ( 'b' , 'a' , { 'a' , 'b' } ) ]
lbls = [ 'x' , 'y' , 'z' ]
df = pd.DataFrame.from_records( lnks , columns = lbls )
Trying to drop duplicated rows based on column z values :
df.drop_duplicates( subset = 'z' , keep='first')
And i get the error message :
TypeError: unhashable type: 'set'
Is there a way to drop duplicated rows based on a unhashable typed column ?
python pandas dataframe
edited 1 hour ago
n1k31t4
6,1362319
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
$endgroup$
add a comment |
$begingroup$
i have a pandas dataframe df with one column z filled with set values
i want to drop duplicated rows where 2 rows are considered duplicated version of one another when they have same column z values ( which are sets ).
import pandas as pd
lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } ) , ( 'b' , 'a' , { 'a' , 'b' } ) ]
lbls = [ 'x' , 'y' , 'z' ]
df = pd.DataFrame.from_records( lnks , columns = lbls )
Trying to drop duplicated rows based on column z values :
df.drop_duplicates( subset = 'z' , keep='first')
And i get the error message :
TypeError: unhashable type: 'set'
Is there a way to drop duplicated rows based on a unhashable typed column ?
python pandas dataframe
edited 1 hour ago
n1k31t4
6,1362319
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
$endgroup$
$begingroup$
I assume it is a typo - but there isn't actually a duplicate in row z anyway because onebalso has a space:'b '.
$endgroup$
– n1k31t4
4 hours ago
$begingroup$
right. I've made a correction. thx.
$endgroup$
– Fabrice BOUCHAREL
3 hours ago
add a comment |
$begingroup$
i have a pandas dataframe df with one column z filled with set values
i want to drop duplicated rows where 2 rows are considered duplicated version of one another when they have same column z values ( which are sets ).
import pandas as pd
lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } ) , ( 'b' , 'a' , { 'a' , 'b' } ) ]
lbls = [ 'x' , 'y' , 'z' ]
df = pd.DataFrame.from_records( lnks , columns = lbls )
Trying to drop duplicated rows based on column z values :
df.drop_duplicates( subset = 'z' , keep='first')
And i get the error message :
TypeError: unhashable type: 'set'
Is there a way to drop duplicated rows based on a unhashable typed column ?
python pandas dataframe
edited 1 hour ago
n1k31t4
6,1362319
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
$endgroup$
i have a pandas dataframe df with one column z filled with set values
i want to drop duplicated rows where 2 rows are considered duplicated version of one another when they have same column z values ( which are sets ).
import pandas as pd
lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } ) , ( 'b' , 'a' , { 'a' , 'b' } ) ]
lbls = [ 'x' , 'y' , 'z' ]
df = pd.DataFrame.from_records( lnks , columns = lbls )
Trying to drop duplicated rows based on column z values :
df.drop_duplicates( subset = 'z' , keep='first')
And i get the error message :
TypeError: unhashable type: 'set'
Is there a way to drop duplicated rows based on a unhashable typed column ?
python pandas dataframe
python pandas dataframe
edited 1 hour ago
n1k31t4
6,1362319
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
edited 1 hour ago
n1k31t4
6,1362319
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
edited 1 hour ago
n1k31t4
6,1362319
edited 1 hour ago
n1k31t4
6,1362319
edited 1 hour ago
n1k31t4
6,1362319
6,1362319
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
asked 5 hours ago
Fabrice BOUCHARELFabrice BOUCHAREL
585
585
$begingroup$
I assume it is a typo - but there isn't actually a duplicate in row z anyway because onebalso has a space:'b '.
$endgroup$
– n1k31t4
4 hours ago
$begingroup$
right. I've made a correction. thx.
$endgroup$
– Fabrice BOUCHAREL
3 hours ago
add a comment |
$begingroup$
I assume it is a typo - but there isn't actually a duplicate in row z anyway because onebalso has a space:'b '.
$endgroup$
– n1k31t4
4 hours ago
$begingroup$
right. I've made a correction. thx.
$endgroup$
– Fabrice BOUCHAREL
3 hours ago
$begingroup$
I assume it is a typo - but there isn't actually a duplicate in row z anyway because one
b also has a space: 'b '.$endgroup$
– n1k31t4
4 hours ago
$begingroup$
I assume it is a typo - but there isn't actually a duplicate in row z anyway because one
b also has a space: 'b '.$endgroup$
– n1k31t4
4 hours ago
$begingroup$
right. I've made a correction. thx.
$endgroup$
– Fabrice BOUCHAREL
3 hours ago
$begingroup$
right. I've made a correction. thx.
$endgroup$
– Fabrice BOUCHAREL
3 hours ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
It is true that a set is not hashable (it cannot be used as a key in a hashmap a.k.a a dictionary). So what you can do is to just convert the column to a type that is hashable - I would go for a tuple.
I made a new column that is just the "z" column you had, converted to tuples. Then you can use the same method you tried to, on the new column:
In [1] : import pandas as pd
...:
...: lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } )
...: , ( 'b' , 'a' , { 'a' , 'b' } ) ]
...: lbls = [ 'x' , 'y' , 'z' ]
...: df = pd.DataFrame.from_records( lnks , columns = lbls)
In [2]: df["z_tuple"] = df.z.apply(lambda x: tuple(x))
In [3]: df.drop_duplicates(subset="z_tuple", keep="first")
Out[3]:
x y z z_tuple
0 a b {b, a} (b, a)
1 b c {c, b} (c, b)
The apply method lets you apply a function to each item in a column, and then returns the values as a new column (a Pandas Series object). This lets you assign it back to the original DataFrame as a new column, as I did.
You can also remove the "z_tuple" column then if you no longer want it:
In [4] : df.drop("z_tuple", axis=1, inplace=True)
In [5] : df
Out[5] :
x y z
0 a b {b, a}
1 b c {c, b}
2 b a {b, a}
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
$endgroup$
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
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: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
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%2fdatascience.stackexchange.com%2fquestions%2f46541%2fis-there-a-way-to-drop-duplicated-rows-based-on-an-unhashable-column%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
$begingroup$
It is true that a set is not hashable (it cannot be used as a key in a hashmap a.k.a a dictionary). So what you can do is to just convert the column to a type that is hashable - I would go for a tuple.
I made a new column that is just the "z" column you had, converted to tuples. Then you can use the same method you tried to, on the new column:
In [1] : import pandas as pd
...:
...: lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } )
...: , ( 'b' , 'a' , { 'a' , 'b' } ) ]
...: lbls = [ 'x' , 'y' , 'z' ]
...: df = pd.DataFrame.from_records( lnks , columns = lbls)
In [2]: df["z_tuple"] = df.z.apply(lambda x: tuple(x))
In [3]: df.drop_duplicates(subset="z_tuple", keep="first")
Out[3]:
x y z z_tuple
0 a b {b, a} (b, a)
1 b c {c, b} (c, b)
The apply method lets you apply a function to each item in a column, and then returns the values as a new column (a Pandas Series object). This lets you assign it back to the original DataFrame as a new column, as I did.
You can also remove the "z_tuple" column then if you no longer want it:
In [4] : df.drop("z_tuple", axis=1, inplace=True)
In [5] : df
Out[5] :
x y z
0 a b {b, a}
1 b c {c, b}
2 b a {b, a}
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
$endgroup$
add a comment |
$begingroup$
It is true that a set is not hashable (it cannot be used as a key in a hashmap a.k.a a dictionary). So what you can do is to just convert the column to a type that is hashable - I would go for a tuple.
I made a new column that is just the "z" column you had, converted to tuples. Then you can use the same method you tried to, on the new column:
In [1] : import pandas as pd
...:
...: lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } )
...: , ( 'b' , 'a' , { 'a' , 'b' } ) ]
...: lbls = [ 'x' , 'y' , 'z' ]
...: df = pd.DataFrame.from_records( lnks , columns = lbls)
In [2]: df["z_tuple"] = df.z.apply(lambda x: tuple(x))
In [3]: df.drop_duplicates(subset="z_tuple", keep="first")
Out[3]:
x y z z_tuple
0 a b {b, a} (b, a)
1 b c {c, b} (c, b)
The apply method lets you apply a function to each item in a column, and then returns the values as a new column (a Pandas Series object). This lets you assign it back to the original DataFrame as a new column, as I did.
You can also remove the "z_tuple" column then if you no longer want it:
In [4] : df.drop("z_tuple", axis=1, inplace=True)
In [5] : df
Out[5] :
x y z
0 a b {b, a}
1 b c {c, b}
2 b a {b, a}
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
$endgroup$
add a comment |
$begingroup$
It is true that a set is not hashable (it cannot be used as a key in a hashmap a.k.a a dictionary). So what you can do is to just convert the column to a type that is hashable - I would go for a tuple.
I made a new column that is just the "z" column you had, converted to tuples. Then you can use the same method you tried to, on the new column:
In [1] : import pandas as pd
...:
...: lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } )
...: , ( 'b' , 'a' , { 'a' , 'b' } ) ]
...: lbls = [ 'x' , 'y' , 'z' ]
...: df = pd.DataFrame.from_records( lnks , columns = lbls)
In [2]: df["z_tuple"] = df.z.apply(lambda x: tuple(x))
In [3]: df.drop_duplicates(subset="z_tuple", keep="first")
Out[3]:
x y z z_tuple
0 a b {b, a} (b, a)
1 b c {c, b} (c, b)
The apply method lets you apply a function to each item in a column, and then returns the values as a new column (a Pandas Series object). This lets you assign it back to the original DataFrame as a new column, as I did.
You can also remove the "z_tuple" column then if you no longer want it:
In [4] : df.drop("z_tuple", axis=1, inplace=True)
In [5] : df
Out[5] :
x y z
0 a b {b, a}
1 b c {c, b}
2 b a {b, a}
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
$endgroup$
It is true that a set is not hashable (it cannot be used as a key in a hashmap a.k.a a dictionary). So what you can do is to just convert the column to a type that is hashable - I would go for a tuple.
I made a new column that is just the "z" column you had, converted to tuples. Then you can use the same method you tried to, on the new column:
In [1] : import pandas as pd
...:
...: lnks = [ ( 'a' , 'b' , { 'a' , 'b' } ) , ( 'b' , 'c' , { 'b' , 'c' } )
...: , ( 'b' , 'a' , { 'a' , 'b' } ) ]
...: lbls = [ 'x' , 'y' , 'z' ]
...: df = pd.DataFrame.from_records( lnks , columns = lbls)
In [2]: df["z_tuple"] = df.z.apply(lambda x: tuple(x))
In [3]: df.drop_duplicates(subset="z_tuple", keep="first")
Out[3]:
x y z z_tuple
0 a b {b, a} (b, a)
1 b c {c, b} (c, b)
The apply method lets you apply a function to each item in a column, and then returns the values as a new column (a Pandas Series object). This lets you assign it back to the original DataFrame as a new column, as I did.
You can also remove the "z_tuple" column then if you no longer want it:
In [4] : df.drop("z_tuple", axis=1, inplace=True)
In [5] : df
Out[5] :
x y z
0 a b {b, a}
1 b c {c, b}
2 b a {b, a}
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
answered 4 hours ago
n1k31t4n1k31t4
6,1362319
6,1362319
add a comment |
add a comment |
Thanks for contributing an answer to Data Science Stack Exchange!
- 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.
Use MathJax to format equations. MathJax reference.
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%2fdatascience.stackexchange.com%2fquestions%2f46541%2fis-there-a-way-to-drop-duplicated-rows-based-on-an-unhashable-column%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
$begingroup$
I assume it is a typo - but there isn't actually a duplicate in row z anyway because one
balso has a space:'b '.$endgroup$
– n1k31t4
4 hours ago
$begingroup$
right. I've made a correction. thx.
$endgroup$
– Fabrice BOUCHAREL
3 hours ago