Split a list of values into columns of a dataframe?
2
$begingroup$
I am new to python and stuck at a particular problem involving dataframes.
The image has a sample column, however the data is not consistent. There are also some floats and NAN. I need these to be split across columns. That is each unique value becomes a column in the df.
Any insights?
python pandas
asked May 17 '16 at 1:37
DrjDrj
2771416
$endgroup$
add a comment |
2
$begingroup$
I am new to python and stuck at a particular problem involving dataframes.
The image has a sample column, however the data is not consistent. There are also some floats and NAN. I need these to be split across columns. That is each unique value becomes a column in the df.
Any insights?
python pandas
asked May 17 '16 at 1:37
DrjDrj
2771416
$endgroup$
$begingroup$
Possible duplicate of How to binary encode multi-valued categorical variable from Pandas dataframe?
$endgroup$
– Emre
May 17 '16 at 6:11
add a comment |
2
2
2
4
$begingroup$
I am new to python and stuck at a particular problem involving dataframes.
The image has a sample column, however the data is not consistent. There are also some floats and NAN. I need these to be split across columns. That is each unique value becomes a column in the df.
Any insights?
python pandas
asked May 17 '16 at 1:37
DrjDrj
2771416
$endgroup$
I am new to python and stuck at a particular problem involving dataframes.
The image has a sample column, however the data is not consistent. There are also some floats and NAN. I need these to be split across columns. That is each unique value becomes a column in the df.
Any insights?
python pandas
python pandas
asked May 17 '16 at 1:37
DrjDrj
2771416
asked May 17 '16 at 1:37
DrjDrj
2771416
asked May 17 '16 at 1:37
DrjDrj
2771416
asked May 17 '16 at 1:37
DrjDrj
2771416
asked May 17 '16 at 1:37
DrjDrj
2771416
2771416
$begingroup$
Possible duplicate of How to binary encode multi-valued categorical variable from Pandas dataframe?
$endgroup$
– Emre
May 17 '16 at 6:11
add a comment |
$begingroup$
Possible duplicate of How to binary encode multi-valued categorical variable from Pandas dataframe?
$endgroup$
– Emre
May 17 '16 at 6:11
$begingroup$
Possible duplicate of How to binary encode multi-valued categorical variable from Pandas dataframe?
$endgroup$
– Emre
May 17 '16 at 6:11
$begingroup$
Possible duplicate of How to binary encode multi-valued categorical variable from Pandas dataframe?
$endgroup$
– Emre
May 17 '16 at 6:11
add a comment |
3 Answers
3
active
oldest
votes
6
$begingroup$
It looks like you're trying to "featurize" the genre column.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary']), ('Adventure', 'Biography', 'Drama', 'Thriller')]).apply(frozenset).to_frame(name='genre')
for genre in frozenset.union(*df.genre):
df[genre] = df.apply(lambda _: int(genre in _.genre), axis=1)
The output:
| row | genre | Romance | Documentary | Thriller | Biography | Family | Drama | Comedy | Adventure | Fantasy |
|-----|-----------------------------------------|---------|-------------|----------|-----------|--------|-------|--------|-----------|---------|
| 0 | (Drama, Adventure, Fantasy) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 1 | (Comedy, Family) | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 2 | (Drama, Comedy, Romance) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 3 | (Drama) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | (Documentary) | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | (Drama, Biography, Adventure, Thriller) | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 |
answered May 17 '16 at 6:08
EmreEmre
8,50011935
$endgroup$
1
$begingroup$
@Drj If this answers your question, please tick it off, otherwise indicate what's wrong. This helps keep our site useful.
$endgroup$
– Emre
May 20 '16 at 21:48
add a comment |
1
$begingroup$
If you want counts, instead of the Boolean values, you can try like this.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy','Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary','Documentary']), ('Adventure','Adventure' ,'Biography', 'Drama', 'Thriller')]).apply(list).to_frame(name='genre')
for genre in set.union(*df.genre.apply(set)):
df[genre] = df.apply(lambda _: int(_.genre.count(genre)), axis=1)
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
$endgroup$
add a comment |
1
$begingroup$
I tried it first with pandas before but it was just a pain to achieve. Use MultiLabelBinarizer from the scikit-learn package:
import pandas
from sklearn.preprocessing import MultiLabelBinarizer
# Binarise labels
mlb = MultiLabelBinarizer()
expandedLabelData = mlb.fit_transform(data["genre"])
labelClasses = mlb.classes_
# Create a pandas.DataFrame from our output
expandedLabels = pandas.DataFrame(expandedLabelData, columns=labelClasses)
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
$endgroup$
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
});
}
});
draft saved
draft discarded
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%2f11797%2fsplit-a-list-of-values-into-columns-of-a-dataframe%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
6
$begingroup$
It looks like you're trying to "featurize" the genre column.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary']), ('Adventure', 'Biography', 'Drama', 'Thriller')]).apply(frozenset).to_frame(name='genre')
for genre in frozenset.union(*df.genre):
df[genre] = df.apply(lambda _: int(genre in _.genre), axis=1)
The output:
| row | genre | Romance | Documentary | Thriller | Biography | Family | Drama | Comedy | Adventure | Fantasy |
|-----|-----------------------------------------|---------|-------------|----------|-----------|--------|-------|--------|-----------|---------|
| 0 | (Drama, Adventure, Fantasy) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 1 | (Comedy, Family) | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 2 | (Drama, Comedy, Romance) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 3 | (Drama) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | (Documentary) | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | (Drama, Biography, Adventure, Thriller) | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 |
answered May 17 '16 at 6:08
EmreEmre
8,50011935
$endgroup$
1
$begingroup$
@Drj If this answers your question, please tick it off, otherwise indicate what's wrong. This helps keep our site useful.
$endgroup$
– Emre
May 20 '16 at 21:48
add a comment |
6
$begingroup$
It looks like you're trying to "featurize" the genre column.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary']), ('Adventure', 'Biography', 'Drama', 'Thriller')]).apply(frozenset).to_frame(name='genre')
for genre in frozenset.union(*df.genre):
df[genre] = df.apply(lambda _: int(genre in _.genre), axis=1)
The output:
| row | genre | Romance | Documentary | Thriller | Biography | Family | Drama | Comedy | Adventure | Fantasy |
|-----|-----------------------------------------|---------|-------------|----------|-----------|--------|-------|--------|-----------|---------|
| 0 | (Drama, Adventure, Fantasy) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 1 | (Comedy, Family) | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 2 | (Drama, Comedy, Romance) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 3 | (Drama) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | (Documentary) | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | (Drama, Biography, Adventure, Thriller) | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 |
answered May 17 '16 at 6:08
EmreEmre
8,50011935
$endgroup$
1
$begingroup$
@Drj If this answers your question, please tick it off, otherwise indicate what's wrong. This helps keep our site useful.
$endgroup$
– Emre
May 20 '16 at 21:48
add a comment |
6
6
6
$begingroup$
It looks like you're trying to "featurize" the genre column.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary']), ('Adventure', 'Biography', 'Drama', 'Thriller')]).apply(frozenset).to_frame(name='genre')
for genre in frozenset.union(*df.genre):
df[genre] = df.apply(lambda _: int(genre in _.genre), axis=1)
The output:
| row | genre | Romance | Documentary | Thriller | Biography | Family | Drama | Comedy | Adventure | Fantasy |
|-----|-----------------------------------------|---------|-------------|----------|-----------|--------|-------|--------|-----------|---------|
| 0 | (Drama, Adventure, Fantasy) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 1 | (Comedy, Family) | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 2 | (Drama, Comedy, Romance) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 3 | (Drama) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | (Documentary) | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | (Drama, Biography, Adventure, Thriller) | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 |
answered May 17 '16 at 6:08
EmreEmre
8,50011935
$endgroup$
It looks like you're trying to "featurize" the genre column.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary']), ('Adventure', 'Biography', 'Drama', 'Thriller')]).apply(frozenset).to_frame(name='genre')
for genre in frozenset.union(*df.genre):
df[genre] = df.apply(lambda _: int(genre in _.genre), axis=1)
The output:
| row | genre | Romance | Documentary | Thriller | Biography | Family | Drama | Comedy | Adventure | Fantasy |
|-----|-----------------------------------------|---------|-------------|----------|-----------|--------|-------|--------|-----------|---------|
| 0 | (Drama, Adventure, Fantasy) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 1 | (Comedy, Family) | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| 2 | (Drama, Comedy, Romance) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
| 3 | (Drama) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | (Documentary) | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5 | (Drama, Biography, Adventure, Thriller) | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 |
answered May 17 '16 at 6:08
EmreEmre
8,50011935
answered May 17 '16 at 6:08
EmreEmre
8,50011935
answered May 17 '16 at 6:08
EmreEmre
8,50011935
answered May 17 '16 at 6:08
EmreEmre
8,50011935
8,50011935
1
$begingroup$
@Drj If this answers your question, please tick it off, otherwise indicate what's wrong. This helps keep our site useful.
$endgroup$
– Emre
May 20 '16 at 21:48
add a comment |
1
$begingroup$
@Drj If this answers your question, please tick it off, otherwise indicate what's wrong. This helps keep our site useful.
$endgroup$
– Emre
May 20 '16 at 21:48
1
1
$begingroup$
@Drj If this answers your question, please tick it off, otherwise indicate what's wrong. This helps keep our site useful.
$endgroup$
– Emre
May 20 '16 at 21:48
$begingroup$
@Drj If this answers your question, please tick it off, otherwise indicate what's wrong. This helps keep our site useful.
$endgroup$
– Emre
May 20 '16 at 21:48
add a comment |
1
$begingroup$
If you want counts, instead of the Boolean values, you can try like this.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy','Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary','Documentary']), ('Adventure','Adventure' ,'Biography', 'Drama', 'Thriller')]).apply(list).to_frame(name='genre')
for genre in set.union(*df.genre.apply(set)):
df[genre] = df.apply(lambda _: int(_.genre.count(genre)), axis=1)
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
$endgroup$
add a comment |
1
$begingroup$
If you want counts, instead of the Boolean values, you can try like this.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy','Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary','Documentary']), ('Adventure','Adventure' ,'Biography', 'Drama', 'Thriller')]).apply(list).to_frame(name='genre')
for genre in set.union(*df.genre.apply(set)):
df[genre] = df.apply(lambda _: int(_.genre.count(genre)), axis=1)
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
$endgroup$
add a comment |
1
1
1
$begingroup$
If you want counts, instead of the Boolean values, you can try like this.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy','Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary','Documentary']), ('Adventure','Adventure' ,'Biography', 'Drama', 'Thriller')]).apply(list).to_frame(name='genre')
for genre in set.union(*df.genre.apply(set)):
df[genre] = df.apply(lambda _: int(_.genre.count(genre)), axis=1)
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
$endgroup$
If you want counts, instead of the Boolean values, you can try like this.
df = pandas.Series([('Adventure', 'Drama', 'Fantasy','Fantasy'), ('Comedy', 'Family'), ('Drama', 'Comedy', 'Romance'), (['Drama']),
(['Documentary','Documentary']), ('Adventure','Adventure' ,'Biography', 'Drama', 'Thriller')]).apply(list).to_frame(name='genre')
for genre in set.union(*df.genre.apply(set)):
df[genre] = df.apply(lambda _: int(_.genre.count(genre)), axis=1)
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
edited Apr 4 '18 at 13:57
Stephen Rauch
1,52751129
1,52751129
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
answered Apr 4 '18 at 10:57
TARUN KUMARTARUN KUMAR
113
113
add a comment |
add a comment |
1
$begingroup$
I tried it first with pandas before but it was just a pain to achieve. Use MultiLabelBinarizer from the scikit-learn package:
import pandas
from sklearn.preprocessing import MultiLabelBinarizer
# Binarise labels
mlb = MultiLabelBinarizer()
expandedLabelData = mlb.fit_transform(data["genre"])
labelClasses = mlb.classes_
# Create a pandas.DataFrame from our output
expandedLabels = pandas.DataFrame(expandedLabelData, columns=labelClasses)
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
$endgroup$
add a comment |
1
$begingroup$
I tried it first with pandas before but it was just a pain to achieve. Use MultiLabelBinarizer from the scikit-learn package:
import pandas
from sklearn.preprocessing import MultiLabelBinarizer
# Binarise labels
mlb = MultiLabelBinarizer()
expandedLabelData = mlb.fit_transform(data["genre"])
labelClasses = mlb.classes_
# Create a pandas.DataFrame from our output
expandedLabels = pandas.DataFrame(expandedLabelData, columns=labelClasses)
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
$endgroup$
add a comment |
1
1
1
$begingroup$
I tried it first with pandas before but it was just a pain to achieve. Use MultiLabelBinarizer from the scikit-learn package:
import pandas
from sklearn.preprocessing import MultiLabelBinarizer
# Binarise labels
mlb = MultiLabelBinarizer()
expandedLabelData = mlb.fit_transform(data["genre"])
labelClasses = mlb.classes_
# Create a pandas.DataFrame from our output
expandedLabels = pandas.DataFrame(expandedLabelData, columns=labelClasses)
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
$endgroup$
I tried it first with pandas before but it was just a pain to achieve. Use MultiLabelBinarizer from the scikit-learn package:
import pandas
from sklearn.preprocessing import MultiLabelBinarizer
# Binarise labels
mlb = MultiLabelBinarizer()
expandedLabelData = mlb.fit_transform(data["genre"])
labelClasses = mlb.classes_
# Create a pandas.DataFrame from our output
expandedLabels = pandas.DataFrame(expandedLabelData, columns=labelClasses)
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
edited 24 mins ago
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
answered Nov 26 '18 at 23:12
holzkohlengrillholzkohlengrill
1113
1113
add a comment |
add a comment |
draft saved
draft discarded
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.
draft saved
draft discarded
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%2f11797%2fsplit-a-list-of-values-into-columns-of-a-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
$begingroup$
Possible duplicate of How to binary encode multi-valued categorical variable from Pandas dataframe?
$endgroup$
– Emre
May 17 '16 at 6:11