“ValueError: could not convert string to float” error in scikit-learn
-1
I'm running the following script:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
dataset = pd.read_csv('data/50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
onehotencoder = OneHotEncoder(categorical_features=3,
handle_unknown='ignore')
onehotencoder.fit(X)
The data head looks like:
data
And I've got this:
ValueError: could not convert string to float: 'New York'
I read the answers to similar questions and then opened scikit-learn documentations, but how you can see scikit-learn authors doesn't have issues with spaces in strings
I know that I can use LabelEncocder from sklearn.preprocessing and then use OHE and it works well, but in that case
In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
warnings.warn(msg, FutureWarning)
massage occurs.
You can use full csv file or
[[165349.2, 136897.8, 471784.1, 'New York', 192261.83],
[162597.7, 151377.59, 443898.53, 'California', 191792.06],
[153441.51, 101145.55, 407934.54, 'Florida', 191050.39],
[144372.41, 118671.85, 383199.62, 'New York', 182901.99],
[142107.34, 91391.77, 366168.42, 'Florida', 166187.94]]
5 first lines to test this code.
python numpy scikit-learn
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
add a comment |
-1
I'm running the following script:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
dataset = pd.read_csv('data/50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
onehotencoder = OneHotEncoder(categorical_features=3,
handle_unknown='ignore')
onehotencoder.fit(X)
The data head looks like:
data
And I've got this:
ValueError: could not convert string to float: 'New York'
I read the answers to similar questions and then opened scikit-learn documentations, but how you can see scikit-learn authors doesn't have issues with spaces in strings
I know that I can use LabelEncocder from sklearn.preprocessing and then use OHE and it works well, but in that case
In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
warnings.warn(msg, FutureWarning)
massage occurs.
You can use full csv file or
[[165349.2, 136897.8, 471784.1, 'New York', 192261.83],
[162597.7, 151377.59, 443898.53, 'California', 191792.06],
[153441.51, 101145.55, 407934.54, 'Florida', 191050.39],
[144372.41, 118671.85, 383199.62, 'New York', 182901.99],
[142107.34, 91391.77, 366168.42, 'Florida', 166187.94]]
5 first lines to test this code.
python numpy scikit-learn
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
My input, as you can see from code, is csv file
– Aziz Temirkhanov
Nov 26 '18 at 0:14
1
try: dataset.info() to check the types of data that you have in your dataframe.
– Jorge
Nov 26 '18 at 0:20
1
I've add 5 first lines and link to pastebin with full content of the file
– Aziz Temirkhanov
Nov 26 '18 at 0:29
The 'State' column full of 50 non-null objects. Now I see the problem, but anyway have no idea how to fix it without usingLabelEncoder
– Aziz Temirkhanov
Nov 26 '18 at 0:31
What would you expect 'New York' to be as a floating point number? Why would you think it has anything to do with a space in the string?
– Jared Smith
Nov 26 '18 at 0:33
add a comment |
-1
-1
-1
0
I'm running the following script:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
dataset = pd.read_csv('data/50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
onehotencoder = OneHotEncoder(categorical_features=3,
handle_unknown='ignore')
onehotencoder.fit(X)
The data head looks like:
data
And I've got this:
ValueError: could not convert string to float: 'New York'
I read the answers to similar questions and then opened scikit-learn documentations, but how you can see scikit-learn authors doesn't have issues with spaces in strings
I know that I can use LabelEncocder from sklearn.preprocessing and then use OHE and it works well, but in that case
In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
warnings.warn(msg, FutureWarning)
massage occurs.
You can use full csv file or
[[165349.2, 136897.8, 471784.1, 'New York', 192261.83],
[162597.7, 151377.59, 443898.53, 'California', 191792.06],
[153441.51, 101145.55, 407934.54, 'Florida', 191050.39],
[144372.41, 118671.85, 383199.62, 'New York', 182901.99],
[142107.34, 91391.77, 366168.42, 'Florida', 166187.94]]
5 first lines to test this code.
python numpy scikit-learn
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
I'm running the following script:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
dataset = pd.read_csv('data/50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
onehotencoder = OneHotEncoder(categorical_features=3,
handle_unknown='ignore')
onehotencoder.fit(X)
The data head looks like:
data
And I've got this:
ValueError: could not convert string to float: 'New York'
I read the answers to similar questions and then opened scikit-learn documentations, but how you can see scikit-learn authors doesn't have issues with spaces in strings
I know that I can use LabelEncocder from sklearn.preprocessing and then use OHE and it works well, but in that case
In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
warnings.warn(msg, FutureWarning)
massage occurs.
You can use full csv file or
[[165349.2, 136897.8, 471784.1, 'New York', 192261.83],
[162597.7, 151377.59, 443898.53, 'California', 191792.06],
[153441.51, 101145.55, 407934.54, 'Florida', 191050.39],
[144372.41, 118671.85, 383199.62, 'New York', 182901.99],
[142107.34, 91391.77, 366168.42, 'Florida', 166187.94]]
5 first lines to test this code.
python numpy scikit-learn
python numpy scikit-learn
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
edited Nov 26 '18 at 1:33
Aziz Temirkhanov
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
asked Nov 26 '18 at 0:08
Aziz TemirkhanovAziz Temirkhanov
43
43
My input, as you can see from code, is csv file
– Aziz Temirkhanov
Nov 26 '18 at 0:14
1
try: dataset.info() to check the types of data that you have in your dataframe.
– Jorge
Nov 26 '18 at 0:20
1
I've add 5 first lines and link to pastebin with full content of the file
– Aziz Temirkhanov
Nov 26 '18 at 0:29
The 'State' column full of 50 non-null objects. Now I see the problem, but anyway have no idea how to fix it without usingLabelEncoder
– Aziz Temirkhanov
Nov 26 '18 at 0:31
What would you expect 'New York' to be as a floating point number? Why would you think it has anything to do with a space in the string?
– Jared Smith
Nov 26 '18 at 0:33
add a comment |
My input, as you can see from code, is csv file
– Aziz Temirkhanov
Nov 26 '18 at 0:14
1
try: dataset.info() to check the types of data that you have in your dataframe.
– Jorge
Nov 26 '18 at 0:20
1
I've add 5 first lines and link to pastebin with full content of the file
– Aziz Temirkhanov
Nov 26 '18 at 0:29
The 'State' column full of 50 non-null objects. Now I see the problem, but anyway have no idea how to fix it without usingLabelEncoder
– Aziz Temirkhanov
Nov 26 '18 at 0:31
What would you expect 'New York' to be as a floating point number? Why would you think it has anything to do with a space in the string?
– Jared Smith
Nov 26 '18 at 0:33
My input, as you can see from code, is csv file
– Aziz Temirkhanov
Nov 26 '18 at 0:14
My input, as you can see from code, is csv file
– Aziz Temirkhanov
Nov 26 '18 at 0:14
1
1
try: dataset.info() to check the types of data that you have in your dataframe.
– Jorge
Nov 26 '18 at 0:20
try: dataset.info() to check the types of data that you have in your dataframe.
– Jorge
Nov 26 '18 at 0:20
1
1
I've add 5 first lines and link to pastebin with full content of the file
– Aziz Temirkhanov
Nov 26 '18 at 0:29
I've add 5 first lines and link to pastebin with full content of the file
– Aziz Temirkhanov
Nov 26 '18 at 0:29
The 'State' column full of 50 non-null objects. Now I see the problem, but anyway have no idea how to fix it without using
LabelEncoder– Aziz Temirkhanov
Nov 26 '18 at 0:31
The 'State' column full of 50 non-null objects. Now I see the problem, but anyway have no idea how to fix it without using
LabelEncoder– Aziz Temirkhanov
Nov 26 '18 at 0:31
What would you expect 'New York' to be as a floating point number? Why would you think it has anything to do with a space in the string?
– Jared Smith
Nov 26 '18 at 0:33
What would you expect 'New York' to be as a floating point number? Why would you think it has anything to do with a space in the string?
– Jared Smith
Nov 26 '18 at 0:33
add a comment |
2 Answers
2
active
oldest
votes
0
It is categorical_features=3 that hurts you. You cannot use categorical_features with string data. Remove this option, and luck will be with you. Also, you probably need fit_transform, not fit as such.
onehotencoder = OneHotEncoder(handle_unknown='ignore')
transformed = onehotencoder.fit_transform(X[:, [3]]).toarray()
X1 = np.concatenate([X[:, :2], transformed, X[:, 4:]], axis=1)
#array([[165349.2, 136897.8, 0.0, '0.0, 1.0, 192261.83],
# [162597.7, 151377.59, 1.0, 0.0, 0.0, 191792.06],
# [153441.51, 101145.55, 0.0, 1.0, 0.0, 191050.39],
# [144372.41, 118671.85, 0.0, 0.0, 1.0, 182901.99],
# [142107.34, 91391.77, 0.0, 1.0, 0.0, 166187.94']])
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
In that case the whole dataset tranforms to categorical data, not only 3d column
– Aziz Temirkhanov
Nov 26 '18 at 0:57
You can choose which columns to transform.
– DYZ
Nov 26 '18 at 0:58
I ran this code:onehotencoder = OneHotEncoder(handle_unknown='ignore') onehotencoder.fit(X[:, 3])and got this error:ValueError: Expected 2D array, got 1D array instead:
– Aziz Temirkhanov
Nov 26 '18 at 1:08
Because you pass a 1D array instead of a 2D array. You ought to passX[:, [3]]orX[:,3].reshape(1,-1).
– DYZ
Nov 26 '18 at 1:24
OK, I did it like you said. Now if I apply thisX = onehotencoder.transform(X[:, [3]]).toarray()I losing my first 3 colums. If I apply thisX = onehotencoder.transform(X[:, 3]).toarray()the same error occurs
– Aziz Temirkhanov
Nov 26 '18 at 1:30
|
show 4 more comments
0
Try this:
from sklearn.compose import ColumnTransformer, make_column_transformer
from sklearn.preprocessing import OneHotEncoder
columntransformer = make_column_transformer(
(OneHotEncoder(categories='auto'), [3]),
remainder='passthrough')
X = columntransformer.fit_transform(X)
X = X.astype(float)
answered Dec 17 '18 at 2:59
Muke888Muke888
164
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
It is categorical_features=3 that hurts you. You cannot use categorical_features with string data. Remove this option, and luck will be with you. Also, you probably need fit_transform, not fit as such.
onehotencoder = OneHotEncoder(handle_unknown='ignore')
transformed = onehotencoder.fit_transform(X[:, [3]]).toarray()
X1 = np.concatenate([X[:, :2], transformed, X[:, 4:]], axis=1)
#array([[165349.2, 136897.8, 0.0, '0.0, 1.0, 192261.83],
# [162597.7, 151377.59, 1.0, 0.0, 0.0, 191792.06],
# [153441.51, 101145.55, 0.0, 1.0, 0.0, 191050.39],
# [144372.41, 118671.85, 0.0, 0.0, 1.0, 182901.99],
# [142107.34, 91391.77, 0.0, 1.0, 0.0, 166187.94']])
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
In that case the whole dataset tranforms to categorical data, not only 3d column
– Aziz Temirkhanov
Nov 26 '18 at 0:57
You can choose which columns to transform.
– DYZ
Nov 26 '18 at 0:58
I ran this code:onehotencoder = OneHotEncoder(handle_unknown='ignore') onehotencoder.fit(X[:, 3])and got this error:ValueError: Expected 2D array, got 1D array instead:
– Aziz Temirkhanov
Nov 26 '18 at 1:08
Because you pass a 1D array instead of a 2D array. You ought to passX[:, [3]]orX[:,3].reshape(1,-1).
– DYZ
Nov 26 '18 at 1:24
OK, I did it like you said. Now if I apply thisX = onehotencoder.transform(X[:, [3]]).toarray()I losing my first 3 colums. If I apply thisX = onehotencoder.transform(X[:, 3]).toarray()the same error occurs
– Aziz Temirkhanov
Nov 26 '18 at 1:30
|
show 4 more comments
0
It is categorical_features=3 that hurts you. You cannot use categorical_features with string data. Remove this option, and luck will be with you. Also, you probably need fit_transform, not fit as such.
onehotencoder = OneHotEncoder(handle_unknown='ignore')
transformed = onehotencoder.fit_transform(X[:, [3]]).toarray()
X1 = np.concatenate([X[:, :2], transformed, X[:, 4:]], axis=1)
#array([[165349.2, 136897.8, 0.0, '0.0, 1.0, 192261.83],
# [162597.7, 151377.59, 1.0, 0.0, 0.0, 191792.06],
# [153441.51, 101145.55, 0.0, 1.0, 0.0, 191050.39],
# [144372.41, 118671.85, 0.0, 0.0, 1.0, 182901.99],
# [142107.34, 91391.77, 0.0, 1.0, 0.0, 166187.94']])
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
In that case the whole dataset tranforms to categorical data, not only 3d column
– Aziz Temirkhanov
Nov 26 '18 at 0:57
You can choose which columns to transform.
– DYZ
Nov 26 '18 at 0:58
I ran this code:onehotencoder = OneHotEncoder(handle_unknown='ignore') onehotencoder.fit(X[:, 3])and got this error:ValueError: Expected 2D array, got 1D array instead:
– Aziz Temirkhanov
Nov 26 '18 at 1:08
Because you pass a 1D array instead of a 2D array. You ought to passX[:, [3]]orX[:,3].reshape(1,-1).
– DYZ
Nov 26 '18 at 1:24
OK, I did it like you said. Now if I apply thisX = onehotencoder.transform(X[:, [3]]).toarray()I losing my first 3 colums. If I apply thisX = onehotencoder.transform(X[:, 3]).toarray()the same error occurs
– Aziz Temirkhanov
Nov 26 '18 at 1:30
|
show 4 more comments
0
0
0
It is categorical_features=3 that hurts you. You cannot use categorical_features with string data. Remove this option, and luck will be with you. Also, you probably need fit_transform, not fit as such.
onehotencoder = OneHotEncoder(handle_unknown='ignore')
transformed = onehotencoder.fit_transform(X[:, [3]]).toarray()
X1 = np.concatenate([X[:, :2], transformed, X[:, 4:]], axis=1)
#array([[165349.2, 136897.8, 0.0, '0.0, 1.0, 192261.83],
# [162597.7, 151377.59, 1.0, 0.0, 0.0, 191792.06],
# [153441.51, 101145.55, 0.0, 1.0, 0.0, 191050.39],
# [144372.41, 118671.85, 0.0, 0.0, 1.0, 182901.99],
# [142107.34, 91391.77, 0.0, 1.0, 0.0, 166187.94']])
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
It is categorical_features=3 that hurts you. You cannot use categorical_features with string data. Remove this option, and luck will be with you. Also, you probably need fit_transform, not fit as such.
onehotencoder = OneHotEncoder(handle_unknown='ignore')
transformed = onehotencoder.fit_transform(X[:, [3]]).toarray()
X1 = np.concatenate([X[:, :2], transformed, X[:, 4:]], axis=1)
#array([[165349.2, 136897.8, 0.0, '0.0, 1.0, 192261.83],
# [162597.7, 151377.59, 1.0, 0.0, 0.0, 191792.06],
# [153441.51, 101145.55, 0.0, 1.0, 0.0, 191050.39],
# [144372.41, 118671.85, 0.0, 0.0, 1.0, 182901.99],
# [142107.34, 91391.77, 0.0, 1.0, 0.0, 166187.94']])
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
edited Nov 26 '18 at 1:50
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
answered Nov 26 '18 at 0:56
DYZDYZ
27.9k62150
27.9k62150
In that case the whole dataset tranforms to categorical data, not only 3d column
– Aziz Temirkhanov
Nov 26 '18 at 0:57
You can choose which columns to transform.
– DYZ
Nov 26 '18 at 0:58
I ran this code:onehotencoder = OneHotEncoder(handle_unknown='ignore') onehotencoder.fit(X[:, 3])and got this error:ValueError: Expected 2D array, got 1D array instead:
– Aziz Temirkhanov
Nov 26 '18 at 1:08
Because you pass a 1D array instead of a 2D array. You ought to passX[:, [3]]orX[:,3].reshape(1,-1).
– DYZ
Nov 26 '18 at 1:24
OK, I did it like you said. Now if I apply thisX = onehotencoder.transform(X[:, [3]]).toarray()I losing my first 3 colums. If I apply thisX = onehotencoder.transform(X[:, 3]).toarray()the same error occurs
– Aziz Temirkhanov
Nov 26 '18 at 1:30
|
show 4 more comments
In that case the whole dataset tranforms to categorical data, not only 3d column
– Aziz Temirkhanov
Nov 26 '18 at 0:57
You can choose which columns to transform.
– DYZ
Nov 26 '18 at 0:58
I ran this code:onehotencoder = OneHotEncoder(handle_unknown='ignore') onehotencoder.fit(X[:, 3])and got this error:ValueError: Expected 2D array, got 1D array instead:
– Aziz Temirkhanov
Nov 26 '18 at 1:08
Because you pass a 1D array instead of a 2D array. You ought to passX[:, [3]]orX[:,3].reshape(1,-1).
– DYZ
Nov 26 '18 at 1:24
OK, I did it like you said. Now if I apply thisX = onehotencoder.transform(X[:, [3]]).toarray()I losing my first 3 colums. If I apply thisX = onehotencoder.transform(X[:, 3]).toarray()the same error occurs
– Aziz Temirkhanov
Nov 26 '18 at 1:30
In that case the whole dataset tranforms to categorical data, not only 3d column
– Aziz Temirkhanov
Nov 26 '18 at 0:57
In that case the whole dataset tranforms to categorical data, not only 3d column
– Aziz Temirkhanov
Nov 26 '18 at 0:57
You can choose which columns to transform.
– DYZ
Nov 26 '18 at 0:58
You can choose which columns to transform.
– DYZ
Nov 26 '18 at 0:58
I ran this code:
onehotencoder = OneHotEncoder(handle_unknown='ignore') onehotencoder.fit(X[:, 3]) and got this error: ValueError: Expected 2D array, got 1D array instead:– Aziz Temirkhanov
Nov 26 '18 at 1:08
I ran this code:
onehotencoder = OneHotEncoder(handle_unknown='ignore') onehotencoder.fit(X[:, 3]) and got this error: ValueError: Expected 2D array, got 1D array instead:– Aziz Temirkhanov
Nov 26 '18 at 1:08
Because you pass a 1D array instead of a 2D array. You ought to pass
X[:, [3]] or X[:,3].reshape(1,-1).– DYZ
Nov 26 '18 at 1:24
Because you pass a 1D array instead of a 2D array. You ought to pass
X[:, [3]] or X[:,3].reshape(1,-1).– DYZ
Nov 26 '18 at 1:24
OK, I did it like you said. Now if I apply this
X = onehotencoder.transform(X[:, [3]]).toarray() I losing my first 3 colums. If I apply this X = onehotencoder.transform(X[:, 3]).toarray() the same error occurs– Aziz Temirkhanov
Nov 26 '18 at 1:30
OK, I did it like you said. Now if I apply this
X = onehotencoder.transform(X[:, [3]]).toarray() I losing my first 3 colums. If I apply this X = onehotencoder.transform(X[:, 3]).toarray() the same error occurs– Aziz Temirkhanov
Nov 26 '18 at 1:30
|
show 4 more comments
0
Try this:
from sklearn.compose import ColumnTransformer, make_column_transformer
from sklearn.preprocessing import OneHotEncoder
columntransformer = make_column_transformer(
(OneHotEncoder(categories='auto'), [3]),
remainder='passthrough')
X = columntransformer.fit_transform(X)
X = X.astype(float)
answered Dec 17 '18 at 2:59
Muke888Muke888
164
add a comment |
0
Try this:
from sklearn.compose import ColumnTransformer, make_column_transformer
from sklearn.preprocessing import OneHotEncoder
columntransformer = make_column_transformer(
(OneHotEncoder(categories='auto'), [3]),
remainder='passthrough')
X = columntransformer.fit_transform(X)
X = X.astype(float)
answered Dec 17 '18 at 2:59
Muke888Muke888
164
add a comment |
0
0
0
Try this:
from sklearn.compose import ColumnTransformer, make_column_transformer
from sklearn.preprocessing import OneHotEncoder
columntransformer = make_column_transformer(
(OneHotEncoder(categories='auto'), [3]),
remainder='passthrough')
X = columntransformer.fit_transform(X)
X = X.astype(float)
answered Dec 17 '18 at 2:59
Muke888Muke888
164
Try this:
from sklearn.compose import ColumnTransformer, make_column_transformer
from sklearn.preprocessing import OneHotEncoder
columntransformer = make_column_transformer(
(OneHotEncoder(categories='auto'), [3]),
remainder='passthrough')
X = columntransformer.fit_transform(X)
X = X.astype(float)
answered Dec 17 '18 at 2:59
Muke888Muke888
164
answered Dec 17 '18 at 2:59
Muke888Muke888
164
answered Dec 17 '18 at 2:59
Muke888Muke888
164
answered Dec 17 '18 at 2:59
Muke888Muke888
164
164
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My input, as you can see from code, is csv file
– Aziz Temirkhanov
Nov 26 '18 at 0:14
1
try: dataset.info() to check the types of data that you have in your dataframe.
– Jorge
Nov 26 '18 at 0:20
1
I've add 5 first lines and link to pastebin with full content of the file
– Aziz Temirkhanov
Nov 26 '18 at 0:29
The 'State' column full of 50 non-null objects. Now I see the problem, but anyway have no idea how to fix it without using
LabelEncoder– Aziz Temirkhanov
Nov 26 '18 at 0:31
What would you expect 'New York' to be as a floating point number? Why would you think it has anything to do with a space in the string?
– Jared Smith
Nov 26 '18 at 0:33