Return category labels for a categorical series
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I am using pandas for extracting the categories of a dataset like in the following code:
data=pd.read_csv("iris.csv",header=None)
data[4]=data[4].astype("category")
When I print the values of data[4] I got this list:
1 Setosa
2 Setosa
3 Setosa
4 Setosa
5 Setosa
6 Setosa
7 Setosa
...
149 Virginica
150 Virginica
Name: 4, Length: 150, dtype: category
Categories (3, object): [Setosa, Versicolor, Virginica]
but I want to have the three categories in an array so to have something like:
[Setosa, Versicolor, Virginica]
I was looking around, but I could not find anything that could serve.
Any help?
python pandas series
edited Nov 12 at 4:59
smj
1,076613
asked Nov 11 at 23:01
Little
94052145
add a comment |
up vote
0
down vote
favorite
I am using pandas for extracting the categories of a dataset like in the following code:
data=pd.read_csv("iris.csv",header=None)
data[4]=data[4].astype("category")
When I print the values of data[4] I got this list:
1 Setosa
2 Setosa
3 Setosa
4 Setosa
5 Setosa
6 Setosa
7 Setosa
...
149 Virginica
150 Virginica
Name: 4, Length: 150, dtype: category
Categories (3, object): [Setosa, Versicolor, Virginica]
but I want to have the three categories in an array so to have something like:
[Setosa, Versicolor, Virginica]
I was looking around, but I could not find anything that could serve.
Any help?
python pandas series
edited Nov 12 at 4:59
smj
1,076613
asked Nov 11 at 23:01
Little
94052145
it is because the values are on the fourth column, how can I extract the non-repeated values in an array?
– Little
Nov 11 at 23:11
your data are already category? There is no problem with what you are doing
– Khalil Al Hooti
Nov 11 at 23:11
the problem is that I do not know how to call the categories values, because it prints all. I would like to do something like data[4].getcategories() and return an array of [s,v,vi]
– Little
Nov 11 at 23:13
1
dtype category helps decrease memory use typedata.info(verbose=True)before and after changing dtype to category and check How much computer memory is saved!
– Khalil Al Hooti
Nov 11 at 23:17
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am using pandas for extracting the categories of a dataset like in the following code:
data=pd.read_csv("iris.csv",header=None)
data[4]=data[4].astype("category")
When I print the values of data[4] I got this list:
1 Setosa
2 Setosa
3 Setosa
4 Setosa
5 Setosa
6 Setosa
7 Setosa
...
149 Virginica
150 Virginica
Name: 4, Length: 150, dtype: category
Categories (3, object): [Setosa, Versicolor, Virginica]
but I want to have the three categories in an array so to have something like:
[Setosa, Versicolor, Virginica]
I was looking around, but I could not find anything that could serve.
Any help?
python pandas series
edited Nov 12 at 4:59
smj
1,076613
asked Nov 11 at 23:01
Little
94052145
I am using pandas for extracting the categories of a dataset like in the following code:
data=pd.read_csv("iris.csv",header=None)
data[4]=data[4].astype("category")
When I print the values of data[4] I got this list:
1 Setosa
2 Setosa
3 Setosa
4 Setosa
5 Setosa
6 Setosa
7 Setosa
...
149 Virginica
150 Virginica
Name: 4, Length: 150, dtype: category
Categories (3, object): [Setosa, Versicolor, Virginica]
but I want to have the three categories in an array so to have something like:
[Setosa, Versicolor, Virginica]
I was looking around, but I could not find anything that could serve.
Any help?
python pandas series
python pandas series
edited Nov 12 at 4:59
smj
1,076613
asked Nov 11 at 23:01
Little
94052145
edited Nov 12 at 4:59
smj
1,076613
asked Nov 11 at 23:01
Little
94052145
edited Nov 12 at 4:59
smj
1,076613
edited Nov 12 at 4:59
smj
1,076613
edited Nov 12 at 4:59
smj
1,076613
1,076613
asked Nov 11 at 23:01
Little
94052145
asked Nov 11 at 23:01
Little
94052145
asked Nov 11 at 23:01
Little
94052145
94052145
it is because the values are on the fourth column, how can I extract the non-repeated values in an array?
– Little
Nov 11 at 23:11
your data are already category? There is no problem with what you are doing
– Khalil Al Hooti
Nov 11 at 23:11
the problem is that I do not know how to call the categories values, because it prints all. I would like to do something like data[4].getcategories() and return an array of [s,v,vi]
– Little
Nov 11 at 23:13
1
dtype category helps decrease memory use typedata.info(verbose=True)before and after changing dtype to category and check How much computer memory is saved!
– Khalil Al Hooti
Nov 11 at 23:17
add a comment |
it is because the values are on the fourth column, how can I extract the non-repeated values in an array?
– Little
Nov 11 at 23:11
your data are already category? There is no problem with what you are doing
– Khalil Al Hooti
Nov 11 at 23:11
the problem is that I do not know how to call the categories values, because it prints all. I would like to do something like data[4].getcategories() and return an array of [s,v,vi]
– Little
Nov 11 at 23:13
1
dtype category helps decrease memory use typedata.info(verbose=True)before and after changing dtype to category and check How much computer memory is saved!
– Khalil Al Hooti
Nov 11 at 23:17
it is because the values are on the fourth column, how can I extract the non-repeated values in an array?
– Little
Nov 11 at 23:11
it is because the values are on the fourth column, how can I extract the non-repeated values in an array?
– Little
Nov 11 at 23:11
your data are already category? There is no problem with what you are doing
– Khalil Al Hooti
Nov 11 at 23:11
your data are already category? There is no problem with what you are doing
– Khalil Al Hooti
Nov 11 at 23:11
the problem is that I do not know how to call the categories values, because it prints all. I would like to do something like data[4].getcategories() and return an array of [s,v,vi]
– Little
Nov 11 at 23:13
the problem is that I do not know how to call the categories values, because it prints all. I would like to do something like data[4].getcategories() and return an array of [s,v,vi]
– Little
Nov 11 at 23:13
1
1
dtype category helps decrease memory use type
data.info(verbose=True) before and after changing dtype to category and check How much computer memory is saved!– Khalil Al Hooti
Nov 11 at 23:17
dtype category helps decrease memory use type
data.info(verbose=True) before and after changing dtype to category and check How much computer memory is saved!– Khalil Al Hooti
Nov 11 at 23:17
add a comment |
1 Answer
1
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up vote
0
down vote
data[4].cat.categories.values might be superior here, versus .unique().
Take a look at https://pandas.pydata.org/pandas-docs/stable/categorical.html, "Working with categories".
Note: The result of unique() is not always the same as
Series.cat.categories, because Series.unique() has a couple of
guarantees, namely that it returns categories in the order of
appearance, and it only includes values that are actually present.
.cat.categories.values seems much faster, because of the reasons listed above I imagine.
Example:
import pandas as pd
import numpy as np
s = pd.Series(np.random.choice(['a', 'b', 'c'], 1000), dtype = "category")
% timeit a = s.unique()
# 303 µs ± 23.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
% timeit b = s.cat.categories.values
# 1.26 µs ± 27.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
print(s.cat.categories.values)
# ['a' 'b' 'c']
The size of your dataset and other requirements will likely determine which is better.
answered Nov 11 at 23:33
smj
1,076613
add a comment |
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up vote
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down vote
data[4].cat.categories.values might be superior here, versus .unique().
Take a look at https://pandas.pydata.org/pandas-docs/stable/categorical.html, "Working with categories".
Note: The result of unique() is not always the same as
Series.cat.categories, because Series.unique() has a couple of
guarantees, namely that it returns categories in the order of
appearance, and it only includes values that are actually present.
.cat.categories.values seems much faster, because of the reasons listed above I imagine.
Example:
import pandas as pd
import numpy as np
s = pd.Series(np.random.choice(['a', 'b', 'c'], 1000), dtype = "category")
% timeit a = s.unique()
# 303 µs ± 23.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
% timeit b = s.cat.categories.values
# 1.26 µs ± 27.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
print(s.cat.categories.values)
# ['a' 'b' 'c']
The size of your dataset and other requirements will likely determine which is better.
answered Nov 11 at 23:33
smj
1,076613
add a comment |
up vote
0
down vote
data[4].cat.categories.values might be superior here, versus .unique().
Take a look at https://pandas.pydata.org/pandas-docs/stable/categorical.html, "Working with categories".
Note: The result of unique() is not always the same as
Series.cat.categories, because Series.unique() has a couple of
guarantees, namely that it returns categories in the order of
appearance, and it only includes values that are actually present.
.cat.categories.values seems much faster, because of the reasons listed above I imagine.
Example:
import pandas as pd
import numpy as np
s = pd.Series(np.random.choice(['a', 'b', 'c'], 1000), dtype = "category")
% timeit a = s.unique()
# 303 µs ± 23.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
% timeit b = s.cat.categories.values
# 1.26 µs ± 27.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
print(s.cat.categories.values)
# ['a' 'b' 'c']
The size of your dataset and other requirements will likely determine which is better.
answered Nov 11 at 23:33
smj
1,076613
add a comment |
up vote
0
down vote
up vote
0
down vote
data[4].cat.categories.values might be superior here, versus .unique().
Take a look at https://pandas.pydata.org/pandas-docs/stable/categorical.html, "Working with categories".
Note: The result of unique() is not always the same as
Series.cat.categories, because Series.unique() has a couple of
guarantees, namely that it returns categories in the order of
appearance, and it only includes values that are actually present.
.cat.categories.values seems much faster, because of the reasons listed above I imagine.
Example:
import pandas as pd
import numpy as np
s = pd.Series(np.random.choice(['a', 'b', 'c'], 1000), dtype = "category")
% timeit a = s.unique()
# 303 µs ± 23.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
% timeit b = s.cat.categories.values
# 1.26 µs ± 27.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
print(s.cat.categories.values)
# ['a' 'b' 'c']
The size of your dataset and other requirements will likely determine which is better.
answered Nov 11 at 23:33
smj
1,076613
data[4].cat.categories.values might be superior here, versus .unique().
Take a look at https://pandas.pydata.org/pandas-docs/stable/categorical.html, "Working with categories".
Note: The result of unique() is not always the same as
Series.cat.categories, because Series.unique() has a couple of
guarantees, namely that it returns categories in the order of
appearance, and it only includes values that are actually present.
.cat.categories.values seems much faster, because of the reasons listed above I imagine.
Example:
import pandas as pd
import numpy as np
s = pd.Series(np.random.choice(['a', 'b', 'c'], 1000), dtype = "category")
% timeit a = s.unique()
# 303 µs ± 23.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
% timeit b = s.cat.categories.values
# 1.26 µs ± 27.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
print(s.cat.categories.values)
# ['a' 'b' 'c']
The size of your dataset and other requirements will likely determine which is better.
answered Nov 11 at 23:33
smj
1,076613
answered Nov 11 at 23:33
smj
1,076613
answered Nov 11 at 23:33
smj
1,076613
answered Nov 11 at 23:33
smj
1,076613
1,076613
add a comment |
add a comment |
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it is because the values are on the fourth column, how can I extract the non-repeated values in an array?
– Little
Nov 11 at 23:11
your data are already category? There is no problem with what you are doing
– Khalil Al Hooti
Nov 11 at 23:11
the problem is that I do not know how to call the categories values, because it prints all. I would like to do something like data[4].getcategories() and return an array of [s,v,vi]
– Little
Nov 11 at 23:13
1
dtype category helps decrease memory use type
data.info(verbose=True)before and after changing dtype to category and check How much computer memory is saved!– Khalil Al Hooti
Nov 11 at 23:17