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Using Binary Encoding : How to get the original values back from encoded values?
How do I sort a list of dictionaries by a value of the dictionary?How to get the ASCII value of a character?How to randomly select an item from a list?How to get the current time in PythonHow do I sort a dictionary by value?How do I get the number of elements in a list in Python?How to access environment variable values?How to do Base64 encoding in node.js?“Large data” work flows using pandasGet statistics for each group (such as count, mean, etc) using pandas GroupBy?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I have the below data frame:
data='automobile':['car','car','car','car','scooter','scooter','bike','bike','bike']
df=pd.DataFrame(data)
encoder=ce.BinaryEncoder(cols=['automobile'])
df=encoder.fit_transform(df)
This gives me binary columns which is expected. But while performing clustering analysis how can I get to know which binary value corresponds to which automobile category.
python encoding scikit-learn categorical-data
edited Mar 23 at 10:35
desertnaut
21.5k84681
asked Mar 23 at 5:53
zavy molazavy mola
633
|
show 1 more comment
I have the below data frame:
data='automobile':['car','car','car','car','scooter','scooter','bike','bike','bike']
df=pd.DataFrame(data)
encoder=ce.BinaryEncoder(cols=['automobile'])
df=encoder.fit_transform(df)
This gives me binary columns which is expected. But while performing clustering analysis how can I get to know which binary value corresponds to which automobile category.
python encoding scikit-learn categorical-data
edited Mar 23 at 10:35
desertnaut
21.5k84681
asked Mar 23 at 5:53
zavy molazavy mola
633
encoder has a inverse_transform method. have you tried it?
– rdRahul
Mar 23 at 6:26
I read the docs but not exactly able to implement it
– zavy mola
Mar 23 at 6:30
you just need to pass the binary df to the function.how are you trying?
– rdRahul
Mar 23 at 6:32
It says BinaryEncoder object has no attribute inverse_transform
– zavy mola
Mar 23 at 6:37
you are trying encoder.inverse_transform( df ) . right ? seems strange.
– rdRahul
Mar 23 at 6:39
|
show 1 more comment
I have the below data frame:
data='automobile':['car','car','car','car','scooter','scooter','bike','bike','bike']
df=pd.DataFrame(data)
encoder=ce.BinaryEncoder(cols=['automobile'])
df=encoder.fit_transform(df)
This gives me binary columns which is expected. But while performing clustering analysis how can I get to know which binary value corresponds to which automobile category.
python encoding scikit-learn categorical-data
edited Mar 23 at 10:35
desertnaut
21.5k84681
asked Mar 23 at 5:53
zavy molazavy mola
633
I have the below data frame:
data='automobile':['car','car','car','car','scooter','scooter','bike','bike','bike']
df=pd.DataFrame(data)
encoder=ce.BinaryEncoder(cols=['automobile'])
df=encoder.fit_transform(df)
This gives me binary columns which is expected. But while performing clustering analysis how can I get to know which binary value corresponds to which automobile category.
python encoding scikit-learn categorical-data
python encoding scikit-learn categorical-data
edited Mar 23 at 10:35
desertnaut
21.5k84681
asked Mar 23 at 5:53
zavy molazavy mola
633
edited Mar 23 at 10:35
desertnaut
21.5k84681
asked Mar 23 at 5:53
zavy molazavy mola
633
edited Mar 23 at 10:35
desertnaut
21.5k84681
edited Mar 23 at 10:35
desertnaut
21.5k84681
edited Mar 23 at 10:35
desertnaut
21.5k84681
21.5k84681
asked Mar 23 at 5:53
zavy molazavy mola
633
asked Mar 23 at 5:53
zavy molazavy mola
633
asked Mar 23 at 5:53
zavy molazavy mola
633
633
encoder has a inverse_transform method. have you tried it?
– rdRahul
Mar 23 at 6:26
I read the docs but not exactly able to implement it
– zavy mola
Mar 23 at 6:30
you just need to pass the binary df to the function.how are you trying?
– rdRahul
Mar 23 at 6:32
It says BinaryEncoder object has no attribute inverse_transform
– zavy mola
Mar 23 at 6:37
you are trying encoder.inverse_transform( df ) . right ? seems strange.
– rdRahul
Mar 23 at 6:39
|
show 1 more comment
encoder has a inverse_transform method. have you tried it?
– rdRahul
Mar 23 at 6:26
I read the docs but not exactly able to implement it
– zavy mola
Mar 23 at 6:30
you just need to pass the binary df to the function.how are you trying?
– rdRahul
Mar 23 at 6:32
It says BinaryEncoder object has no attribute inverse_transform
– zavy mola
Mar 23 at 6:37
you are trying encoder.inverse_transform( df ) . right ? seems strange.
– rdRahul
Mar 23 at 6:39
encoder has a inverse_transform method. have you tried it?
– rdRahul
Mar 23 at 6:26
encoder has a inverse_transform method. have you tried it?
– rdRahul
Mar 23 at 6:26
I read the docs but not exactly able to implement it
– zavy mola
Mar 23 at 6:30
I read the docs but not exactly able to implement it
– zavy mola
Mar 23 at 6:30
you just need to pass the binary df to the function.how are you trying?
– rdRahul
Mar 23 at 6:32
you just need to pass the binary df to the function.how are you trying?
– rdRahul
Mar 23 at 6:32
It says BinaryEncoder object has no attribute inverse_transform
– zavy mola
Mar 23 at 6:37
It says BinaryEncoder object has no attribute inverse_transform
– zavy mola
Mar 23 at 6:37
you are trying encoder.inverse_transform( df ) . right ? seems strange.
– rdRahul
Mar 23 at 6:39
you are trying encoder.inverse_transform( df ) . right ? seems strange.
– rdRahul
Mar 23 at 6:39
|
show 1 more comment
1 Answer
1
active
oldest
votes
if you want to keep Label for 'decoding', i suggest you to use LabelEncoder:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
data = 'automobile': ['car', 'car', 'car', 'car', 'scooter', 'scooter', 'bike', 'bike', 'bike']
df = pd.DataFrame(data)
ler = LabelEncoder().fit(df['automobile'])
df['automobile']=ler.transform(df['automobile'])
dico = dict(zip(ler.classes_, ler.transform(ler.classes_)))
print(df)
print(dico)
output: df
automobile
0 1
1 1
2 1
3 1
4 2
5 2
6 0
7 0
8 0
output: dico
'bike': 0, 'car': 1, 'scooter': 2
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
if this answer helps you, please, dont forget to uvpvote/validate the answer
– Frenchy
Apr 11 at 15:10
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
if you want to keep Label for 'decoding', i suggest you to use LabelEncoder:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
data = 'automobile': ['car', 'car', 'car', 'car', 'scooter', 'scooter', 'bike', 'bike', 'bike']
df = pd.DataFrame(data)
ler = LabelEncoder().fit(df['automobile'])
df['automobile']=ler.transform(df['automobile'])
dico = dict(zip(ler.classes_, ler.transform(ler.classes_)))
print(df)
print(dico)
output: df
automobile
0 1
1 1
2 1
3 1
4 2
5 2
6 0
7 0
8 0
output: dico
'bike': 0, 'car': 1, 'scooter': 2
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
if this answer helps you, please, dont forget to uvpvote/validate the answer
– Frenchy
Apr 11 at 15:10
add a comment |
if you want to keep Label for 'decoding', i suggest you to use LabelEncoder:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
data = 'automobile': ['car', 'car', 'car', 'car', 'scooter', 'scooter', 'bike', 'bike', 'bike']
df = pd.DataFrame(data)
ler = LabelEncoder().fit(df['automobile'])
df['automobile']=ler.transform(df['automobile'])
dico = dict(zip(ler.classes_, ler.transform(ler.classes_)))
print(df)
print(dico)
output: df
automobile
0 1
1 1
2 1
3 1
4 2
5 2
6 0
7 0
8 0
output: dico
'bike': 0, 'car': 1, 'scooter': 2
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
if this answer helps you, please, dont forget to uvpvote/validate the answer
– Frenchy
Apr 11 at 15:10
add a comment |
if you want to keep Label for 'decoding', i suggest you to use LabelEncoder:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
data = 'automobile': ['car', 'car', 'car', 'car', 'scooter', 'scooter', 'bike', 'bike', 'bike']
df = pd.DataFrame(data)
ler = LabelEncoder().fit(df['automobile'])
df['automobile']=ler.transform(df['automobile'])
dico = dict(zip(ler.classes_, ler.transform(ler.classes_)))
print(df)
print(dico)
output: df
automobile
0 1
1 1
2 1
3 1
4 2
5 2
6 0
7 0
8 0
output: dico
'bike': 0, 'car': 1, 'scooter': 2
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
if you want to keep Label for 'decoding', i suggest you to use LabelEncoder:
import pandas as pd
from sklearn.preprocessing import LabelEncoder
data = 'automobile': ['car', 'car', 'car', 'car', 'scooter', 'scooter', 'bike', 'bike', 'bike']
df = pd.DataFrame(data)
ler = LabelEncoder().fit(df['automobile'])
df['automobile']=ler.transform(df['automobile'])
dico = dict(zip(ler.classes_, ler.transform(ler.classes_)))
print(df)
print(dico)
output: df
automobile
0 1
1 1
2 1
3 1
4 2
5 2
6 0
7 0
8 0
output: dico
'bike': 0, 'car': 1, 'scooter': 2
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
answered Mar 23 at 8:11
FrenchyFrenchy
2,6162518
2,6162518
if this answer helps you, please, dont forget to uvpvote/validate the answer
– Frenchy
Apr 11 at 15:10
add a comment |
if this answer helps you, please, dont forget to uvpvote/validate the answer
– Frenchy
Apr 11 at 15:10
if this answer helps you, please, dont forget to uvpvote/validate the answer
– Frenchy
Apr 11 at 15:10
if this answer helps you, please, dont forget to uvpvote/validate the answer
– Frenchy
Apr 11 at 15:10
add a comment |
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encoder has a inverse_transform method. have you tried it?
– rdRahul
Mar 23 at 6:26
I read the docs but not exactly able to implement it
– zavy mola
Mar 23 at 6:30
you just need to pass the binary df to the function.how are you trying?
– rdRahul
Mar 23 at 6:32
It says BinaryEncoder object has no attribute inverse_transform
– zavy mola
Mar 23 at 6:37
you are trying encoder.inverse_transform( df ) . right ? seems strange.
– rdRahul
Mar 23 at 6:39