Count of unique values per group as new column with pandas
I would like to count the unique observations by a group in a pandas dataframe and create a new column that has the unique count. Importantly, I would not like to reduce the rows in the dataframe; effectively performing something similar to a window function in SQL.
df = pd.DataFrame({
'uID': ['James', 'Henry', 'Abe', 'James', 'Henry', 'Brian', 'Claude', 'James'],
'mID': ['A', 'B', 'A', 'B', 'A', 'A', 'A', 'C']
})
df.groupby('mID')['uID'].nunique()
Will get the unique count per group, but it summarises (reduces the rows), I would effectively like to do something along the lines of:
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
(this obviously does not work)
It is possible to accomplish the desired outcome by taking the unique summarised dataframe and joining it to the original dataframe but I am wondering if there is a more minimal solution.
Thanks
python pandas dataframe group-by pandas-groupby
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
add a comment |
I would like to count the unique observations by a group in a pandas dataframe and create a new column that has the unique count. Importantly, I would not like to reduce the rows in the dataframe; effectively performing something similar to a window function in SQL.
df = pd.DataFrame({
'uID': ['James', 'Henry', 'Abe', 'James', 'Henry', 'Brian', 'Claude', 'James'],
'mID': ['A', 'B', 'A', 'B', 'A', 'A', 'A', 'C']
})
df.groupby('mID')['uID'].nunique()
Will get the unique count per group, but it summarises (reduces the rows), I would effectively like to do something along the lines of:
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
(this obviously does not work)
It is possible to accomplish the desired outcome by taking the unique summarised dataframe and joining it to the original dataframe but I am wondering if there is a more minimal solution.
Thanks
python pandas dataframe group-by pandas-groupby
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
add a comment |
I would like to count the unique observations by a group in a pandas dataframe and create a new column that has the unique count. Importantly, I would not like to reduce the rows in the dataframe; effectively performing something similar to a window function in SQL.
df = pd.DataFrame({
'uID': ['James', 'Henry', 'Abe', 'James', 'Henry', 'Brian', 'Claude', 'James'],
'mID': ['A', 'B', 'A', 'B', 'A', 'A', 'A', 'C']
})
df.groupby('mID')['uID'].nunique()
Will get the unique count per group, but it summarises (reduces the rows), I would effectively like to do something along the lines of:
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
(this obviously does not work)
It is possible to accomplish the desired outcome by taking the unique summarised dataframe and joining it to the original dataframe but I am wondering if there is a more minimal solution.
Thanks
python pandas dataframe group-by pandas-groupby
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
I would like to count the unique observations by a group in a pandas dataframe and create a new column that has the unique count. Importantly, I would not like to reduce the rows in the dataframe; effectively performing something similar to a window function in SQL.
df = pd.DataFrame({
'uID': ['James', 'Henry', 'Abe', 'James', 'Henry', 'Brian', 'Claude', 'James'],
'mID': ['A', 'B', 'A', 'B', 'A', 'A', 'A', 'C']
})
df.groupby('mID')['uID'].nunique()
Will get the unique count per group, but it summarises (reduces the rows), I would effectively like to do something along the lines of:
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
(this obviously does not work)
It is possible to accomplish the desired outcome by taking the unique summarised dataframe and joining it to the original dataframe but I am wondering if there is a more minimal solution.
Thanks
python pandas dataframe group-by pandas-groupby
python pandas dataframe group-by pandas-groupby
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
edited Dec 30 '18 at 16:10
coldspeed
125k23126213
125k23126213
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
asked Nov 12 '18 at 23:35
ZeroStackZeroStack
386116
386116
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
GroupBy.transform('nunique')
On v0.23.4, your solution works for me.
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
GroupBy.nunique + pd.Series.map
Additionally, with your existing solution, you could map the series back to mID:
df['ncount'] = df.mID.map(df.groupby('mID')['uID'].nunique())
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
add a comment |
You are very close!
df['ncount'] = df.groupby('mID')['uID'].transform(pd.Series.nunique)
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
Thanks Peter, on my original data I get a ValueError: Length mismatch: Expected axis has 29101 elements, new values have 29457 elements, i'm not even creating a new column just assigning to a new variable. Your solution does answer the question, any ideas on this error? EDIT: NA values were the culprit here.
– ZeroStack
Nov 12 '18 at 23:47
@ZeroStack, that might be this bug: github.com/pandas-dev/pandas/issues/17093 I would try df.fillna(0).groupby(...), and if that works, investigate further how to fill any missing values in the columnsmIDand/oruID.
– Peter Leimbigler
Nov 12 '18 at 23:53
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%2f53271655%2fcount-of-unique-values-per-group-as-new-column-with-pandas%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
GroupBy.transform('nunique')
On v0.23.4, your solution works for me.
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
GroupBy.nunique + pd.Series.map
Additionally, with your existing solution, you could map the series back to mID:
df['ncount'] = df.mID.map(df.groupby('mID')['uID'].nunique())
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
add a comment |
GroupBy.transform('nunique')
On v0.23.4, your solution works for me.
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
GroupBy.nunique + pd.Series.map
Additionally, with your existing solution, you could map the series back to mID:
df['ncount'] = df.mID.map(df.groupby('mID')['uID'].nunique())
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
add a comment |
GroupBy.transform('nunique')
On v0.23.4, your solution works for me.
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
GroupBy.nunique + pd.Series.map
Additionally, with your existing solution, you could map the series back to mID:
df['ncount'] = df.mID.map(df.groupby('mID')['uID'].nunique())
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
GroupBy.transform('nunique')
On v0.23.4, your solution works for me.
df['ncount'] = df.groupby('mID')['uID'].transform('nunique')
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
GroupBy.nunique + pd.Series.map
Additionally, with your existing solution, you could map the series back to mID:
df['ncount'] = df.mID.map(df.groupby('mID')['uID'].nunique())
df
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
answered Nov 13 '18 at 0:57
coldspeedcoldspeed
125k23126213
125k23126213
add a comment |
add a comment |
You are very close!
df['ncount'] = df.groupby('mID')['uID'].transform(pd.Series.nunique)
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
Thanks Peter, on my original data I get a ValueError: Length mismatch: Expected axis has 29101 elements, new values have 29457 elements, i'm not even creating a new column just assigning to a new variable. Your solution does answer the question, any ideas on this error? EDIT: NA values were the culprit here.
– ZeroStack
Nov 12 '18 at 23:47
@ZeroStack, that might be this bug: github.com/pandas-dev/pandas/issues/17093 I would try df.fillna(0).groupby(...), and if that works, investigate further how to fill any missing values in the columnsmIDand/oruID.
– Peter Leimbigler
Nov 12 '18 at 23:53
add a comment |
You are very close!
df['ncount'] = df.groupby('mID')['uID'].transform(pd.Series.nunique)
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
Thanks Peter, on my original data I get a ValueError: Length mismatch: Expected axis has 29101 elements, new values have 29457 elements, i'm not even creating a new column just assigning to a new variable. Your solution does answer the question, any ideas on this error? EDIT: NA values were the culprit here.
– ZeroStack
Nov 12 '18 at 23:47
@ZeroStack, that might be this bug: github.com/pandas-dev/pandas/issues/17093 I would try df.fillna(0).groupby(...), and if that works, investigate further how to fill any missing values in the columnsmIDand/oruID.
– Peter Leimbigler
Nov 12 '18 at 23:53
add a comment |
You are very close!
df['ncount'] = df.groupby('mID')['uID'].transform(pd.Series.nunique)
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
You are very close!
df['ncount'] = df.groupby('mID')['uID'].transform(pd.Series.nunique)
uID mID ncount
0 James A 5
1 Henry B 2
2 Abe A 5
3 James B 2
4 Henry A 5
5 Brian A 5
6 Claude A 5
7 James C 1
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
answered Nov 12 '18 at 23:43
Peter LeimbiglerPeter Leimbigler
3,8981415
3,8981415
Thanks Peter, on my original data I get a ValueError: Length mismatch: Expected axis has 29101 elements, new values have 29457 elements, i'm not even creating a new column just assigning to a new variable. Your solution does answer the question, any ideas on this error? EDIT: NA values were the culprit here.
– ZeroStack
Nov 12 '18 at 23:47
@ZeroStack, that might be this bug: github.com/pandas-dev/pandas/issues/17093 I would try df.fillna(0).groupby(...), and if that works, investigate further how to fill any missing values in the columnsmIDand/oruID.
– Peter Leimbigler
Nov 12 '18 at 23:53
add a comment |
Thanks Peter, on my original data I get a ValueError: Length mismatch: Expected axis has 29101 elements, new values have 29457 elements, i'm not even creating a new column just assigning to a new variable. Your solution does answer the question, any ideas on this error? EDIT: NA values were the culprit here.
– ZeroStack
Nov 12 '18 at 23:47
@ZeroStack, that might be this bug: github.com/pandas-dev/pandas/issues/17093 I would try df.fillna(0).groupby(...), and if that works, investigate further how to fill any missing values in the columnsmIDand/oruID.
– Peter Leimbigler
Nov 12 '18 at 23:53
Thanks Peter, on my original data I get a ValueError: Length mismatch: Expected axis has 29101 elements, new values have 29457 elements, i'm not even creating a new column just assigning to a new variable. Your solution does answer the question, any ideas on this error? EDIT: NA values were the culprit here.
– ZeroStack
Nov 12 '18 at 23:47
Thanks Peter, on my original data I get a ValueError: Length mismatch: Expected axis has 29101 elements, new values have 29457 elements, i'm not even creating a new column just assigning to a new variable. Your solution does answer the question, any ideas on this error? EDIT: NA values were the culprit here.
– ZeroStack
Nov 12 '18 at 23:47
@ZeroStack, that might be this bug: github.com/pandas-dev/pandas/issues/17093 I would try df.fillna(0).groupby(...), and if that works, investigate further how to fill any missing values in the columns
mID and/or uID.– Peter Leimbigler
Nov 12 '18 at 23:53
@ZeroStack, that might be this bug: github.com/pandas-dev/pandas/issues/17093 I would try df.fillna(0).groupby(...), and if that works, investigate further how to fill any missing values in the columns
mID and/or uID.– Peter Leimbigler
Nov 12 '18 at 23:53
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%2f53271655%2fcount-of-unique-values-per-group-as-new-column-with-pandas%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
