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How do I remove name and dtype from pandas output
The Next CEO of Stack OverflowHow to flush output of print function?How can I remove a trailing newline in Python?How to randomly select an item from a list?How do I remove an element from a list by index in Python?How to remove items from a list while iterating?How to remove a key from a Python dictionary?Delete column from pandas DataFrame by column nameHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headers
I have output that looks like this:
nutrition_info_256174499 = df1.loc[:"Salt" , "%Reference Intake*"]
print (nutrition_info_256174499)
Typical Values
Energy 5%
Fat 1%
of which saturates 1%
Carbohydrates 7%
of which sugars 2%
Fibre -
Protein 7%
Salt 6%
Name: %Reference Intake*, dtype: object
What must be done to remove both Name and dtype at end of output?
python pandas
edited Mar 7 at 17:57
Darkonaut
4,79921429
asked Oct 27 '18 at 18:48
NabihNabih
82
add a comment |
I have output that looks like this:
nutrition_info_256174499 = df1.loc[:"Salt" , "%Reference Intake*"]
print (nutrition_info_256174499)
Typical Values
Energy 5%
Fat 1%
of which saturates 1%
Carbohydrates 7%
of which sugars 2%
Fibre -
Protein 7%
Salt 6%
Name: %Reference Intake*, dtype: object
What must be done to remove both Name and dtype at end of output?
python pandas
edited Mar 7 at 17:57
Darkonaut
4,79921429
asked Oct 27 '18 at 18:48
NabihNabih
82
add a comment |
I have output that looks like this:
nutrition_info_256174499 = df1.loc[:"Salt" , "%Reference Intake*"]
print (nutrition_info_256174499)
Typical Values
Energy 5%
Fat 1%
of which saturates 1%
Carbohydrates 7%
of which sugars 2%
Fibre -
Protein 7%
Salt 6%
Name: %Reference Intake*, dtype: object
What must be done to remove both Name and dtype at end of output?
python pandas
edited Mar 7 at 17:57
Darkonaut
4,79921429
asked Oct 27 '18 at 18:48
NabihNabih
82
I have output that looks like this:
nutrition_info_256174499 = df1.loc[:"Salt" , "%Reference Intake*"]
print (nutrition_info_256174499)
Typical Values
Energy 5%
Fat 1%
of which saturates 1%
Carbohydrates 7%
of which sugars 2%
Fibre -
Protein 7%
Salt 6%
Name: %Reference Intake*, dtype: object
What must be done to remove both Name and dtype at end of output?
python pandas
python pandas
edited Mar 7 at 17:57
Darkonaut
4,79921429
asked Oct 27 '18 at 18:48
NabihNabih
82
edited Mar 7 at 17:57
Darkonaut
4,79921429
asked Oct 27 '18 at 18:48
NabihNabih
82
edited Mar 7 at 17:57
Darkonaut
4,79921429
edited Mar 7 at 17:57
Darkonaut
4,79921429
edited Mar 7 at 17:57
Darkonaut
4,79921429
4,79921429
asked Oct 27 '18 at 18:48
NabihNabih
82
asked Oct 27 '18 at 18:48
NabihNabih
82
asked Oct 27 '18 at 18:48
NabihNabih
82
82
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
Use the .values attribute.
Example:
s = pd.Series(['race','gender'],index=[1,2])
print(s)
Out[159]:
1 race
2 gender
dtype: object
s.values
array(['race', 'gender'], dtype=object)
You can convert to a list or access each value:
list(s)
['race', 'gender']
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
add a comment |
For printing with preserving index you can use .to_string():
df = pd.DataFrame('a': [1,2,3], 'b': [2.23, 0.23, 2.3], index=['x1', 'x2', 'x3'])
print(df.loc[:'x2', 'b'].to_string())
# Out:
x1 2.23
x2 0.23
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
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
Use the .values attribute.
Example:
s = pd.Series(['race','gender'],index=[1,2])
print(s)
Out[159]:
1 race
2 gender
dtype: object
s.values
array(['race', 'gender'], dtype=object)
You can convert to a list or access each value:
list(s)
['race', 'gender']
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
add a comment |
Use the .values attribute.
Example:
s = pd.Series(['race','gender'],index=[1,2])
print(s)
Out[159]:
1 race
2 gender
dtype: object
s.values
array(['race', 'gender'], dtype=object)
You can convert to a list or access each value:
list(s)
['race', 'gender']
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
add a comment |
Use the .values attribute.
Example:
s = pd.Series(['race','gender'],index=[1,2])
print(s)
Out[159]:
1 race
2 gender
dtype: object
s.values
array(['race', 'gender'], dtype=object)
You can convert to a list or access each value:
list(s)
['race', 'gender']
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
Use the .values attribute.
Example:
s = pd.Series(['race','gender'],index=[1,2])
print(s)
Out[159]:
1 race
2 gender
dtype: object
s.values
array(['race', 'gender'], dtype=object)
You can convert to a list or access each value:
list(s)
['race', 'gender']
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
answered Oct 27 '18 at 18:53
makarosmakaros
6,57122547
6,57122547
add a comment |
add a comment |
For printing with preserving index you can use .to_string():
df = pd.DataFrame('a': [1,2,3], 'b': [2.23, 0.23, 2.3], index=['x1', 'x2', 'x3'])
print(df.loc[:'x2', 'b'].to_string())
# Out:
x1 2.23
x2 0.23
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
add a comment |
For printing with preserving index you can use .to_string():
df = pd.DataFrame('a': [1,2,3], 'b': [2.23, 0.23, 2.3], index=['x1', 'x2', 'x3'])
print(df.loc[:'x2', 'b'].to_string())
# Out:
x1 2.23
x2 0.23
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
add a comment |
For printing with preserving index you can use .to_string():
df = pd.DataFrame('a': [1,2,3], 'b': [2.23, 0.23, 2.3], index=['x1', 'x2', 'x3'])
print(df.loc[:'x2', 'b'].to_string())
# Out:
x1 2.23
x2 0.23
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
For printing with preserving index you can use .to_string():
df = pd.DataFrame('a': [1,2,3], 'b': [2.23, 0.23, 2.3], index=['x1', 'x2', 'x3'])
print(df.loc[:'x2', 'b'].to_string())
# Out:
x1 2.23
x2 0.23
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
answered Oct 27 '18 at 19:23
DarkonautDarkonaut
4,79921429
4,79921429
add a comment |
add a comment |
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