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 |
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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 9 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 |
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$begingroup$
Possible duplicate of How to binary encode multi-valued categorical variable from Pandas dataframe?
$endgroup$
– Emre
May 17 '16 at 6:11