iloc method return different type of data
I have a dataset 30 obs and 2 columns i used code below to create independent and depend dataset for a single linear regression.
So each dataset is expected to be a 1 column array.
But the return X is a 2d arrary and returned y is a 1d array what is the reason for that?
So to put my quetion in one line:
what is the difference between
X = dataset.iloc[:, 0].values
and
X = dataset.iloc[:, :-1].values?
When I use:
X = dataset.iloc[:, 0].values
y = dataset.iloc[:, 1].values
X.shape
Out[207]: (30,)
y.shape
Out[204]: (30,)
When I use:
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
X.shape
Out[203]: (30, 1)
y.shape
Out[204]: (30,)
python python-3.x pandas dataframe indexing
edited Dec 31 '18 at 14:52
jpp
100k2161111
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
add a comment |
I have a dataset 30 obs and 2 columns i used code below to create independent and depend dataset for a single linear regression.
So each dataset is expected to be a 1 column array.
But the return X is a 2d arrary and returned y is a 1d array what is the reason for that?
So to put my quetion in one line:
what is the difference between
X = dataset.iloc[:, 0].values
and
X = dataset.iloc[:, :-1].values?
When I use:
X = dataset.iloc[:, 0].values
y = dataset.iloc[:, 1].values
X.shape
Out[207]: (30,)
y.shape
Out[204]: (30,)
When I use:
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
X.shape
Out[203]: (30, 1)
y.shape
Out[204]: (30,)
python python-3.x pandas dataframe indexing
edited Dec 31 '18 at 14:52
jpp
100k2161111
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
add a comment |
I have a dataset 30 obs and 2 columns i used code below to create independent and depend dataset for a single linear regression.
So each dataset is expected to be a 1 column array.
But the return X is a 2d arrary and returned y is a 1d array what is the reason for that?
So to put my quetion in one line:
what is the difference between
X = dataset.iloc[:, 0].values
and
X = dataset.iloc[:, :-1].values?
When I use:
X = dataset.iloc[:, 0].values
y = dataset.iloc[:, 1].values
X.shape
Out[207]: (30,)
y.shape
Out[204]: (30,)
When I use:
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
X.shape
Out[203]: (30, 1)
y.shape
Out[204]: (30,)
python python-3.x pandas dataframe indexing
edited Dec 31 '18 at 14:52
jpp
100k2161111
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
I have a dataset 30 obs and 2 columns i used code below to create independent and depend dataset for a single linear regression.
So each dataset is expected to be a 1 column array.
But the return X is a 2d arrary and returned y is a 1d array what is the reason for that?
So to put my quetion in one line:
what is the difference between
X = dataset.iloc[:, 0].values
and
X = dataset.iloc[:, :-1].values?
When I use:
X = dataset.iloc[:, 0].values
y = dataset.iloc[:, 1].values
X.shape
Out[207]: (30,)
y.shape
Out[204]: (30,)
When I use:
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
X.shape
Out[203]: (30, 1)
y.shape
Out[204]: (30,)
python python-3.x pandas dataframe indexing
python python-3.x pandas dataframe indexing
edited Dec 31 '18 at 14:52
jpp
100k2161111
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
edited Dec 31 '18 at 14:52
jpp
100k2161111
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
edited Dec 31 '18 at 14:52
jpp
100k2161111
edited Dec 31 '18 at 14:52
jpp
100k2161111
edited Dec 31 '18 at 14:52
jpp
100k2161111
100k2161111
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
asked Dec 31 '18 at 14:40
Dongyang FuDongyang Fu
134
134
add a comment |
add a comment |
1 Answer
1
active
oldest
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:-1 represents a range1, while -1 is a scalar. Ranges are 1-dimensional, while scalars are 0-dimensional. Think of a line versus a point; a range is a line while a scalar is a point. This is reflected in how Pandas translates a range vs scalar when indexing.
Therefore, the following are equivalent for a dataframe with 2 columns:
df = pd.DataFrame(np.random.random((5, 2)))
df.iloc[:, :-1].shape # (5, 1)
df.iloc[:, [0]].shape # (5, 1)
Using a scalar will remove the extra dimension. You can do this in a couple of ways:
df.iloc[:, 0].shape # (5,)
df.iloc[:, -2].shape # (5,)
In fact, :-1 is syntactic sugar for a slice object: slice(0, -1). In practice, the simpler syntax is preferred unless you need to pass slice objects around.
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
thx! I am new to python. I m a little crazy about how difficult it is to me. this helps so much!
– Dongyang Fu
Dec 31 '18 at 14:54
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
:-1 represents a range1, while -1 is a scalar. Ranges are 1-dimensional, while scalars are 0-dimensional. Think of a line versus a point; a range is a line while a scalar is a point. This is reflected in how Pandas translates a range vs scalar when indexing.
Therefore, the following are equivalent for a dataframe with 2 columns:
df = pd.DataFrame(np.random.random((5, 2)))
df.iloc[:, :-1].shape # (5, 1)
df.iloc[:, [0]].shape # (5, 1)
Using a scalar will remove the extra dimension. You can do this in a couple of ways:
df.iloc[:, 0].shape # (5,)
df.iloc[:, -2].shape # (5,)
In fact, :-1 is syntactic sugar for a slice object: slice(0, -1). In practice, the simpler syntax is preferred unless you need to pass slice objects around.
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
thx! I am new to python. I m a little crazy about how difficult it is to me. this helps so much!
– Dongyang Fu
Dec 31 '18 at 14:54
add a comment |
:-1 represents a range1, while -1 is a scalar. Ranges are 1-dimensional, while scalars are 0-dimensional. Think of a line versus a point; a range is a line while a scalar is a point. This is reflected in how Pandas translates a range vs scalar when indexing.
Therefore, the following are equivalent for a dataframe with 2 columns:
df = pd.DataFrame(np.random.random((5, 2)))
df.iloc[:, :-1].shape # (5, 1)
df.iloc[:, [0]].shape # (5, 1)
Using a scalar will remove the extra dimension. You can do this in a couple of ways:
df.iloc[:, 0].shape # (5,)
df.iloc[:, -2].shape # (5,)
In fact, :-1 is syntactic sugar for a slice object: slice(0, -1). In practice, the simpler syntax is preferred unless you need to pass slice objects around.
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
thx! I am new to python. I m a little crazy about how difficult it is to me. this helps so much!
– Dongyang Fu
Dec 31 '18 at 14:54
add a comment |
:-1 represents a range1, while -1 is a scalar. Ranges are 1-dimensional, while scalars are 0-dimensional. Think of a line versus a point; a range is a line while a scalar is a point. This is reflected in how Pandas translates a range vs scalar when indexing.
Therefore, the following are equivalent for a dataframe with 2 columns:
df = pd.DataFrame(np.random.random((5, 2)))
df.iloc[:, :-1].shape # (5, 1)
df.iloc[:, [0]].shape # (5, 1)
Using a scalar will remove the extra dimension. You can do this in a couple of ways:
df.iloc[:, 0].shape # (5,)
df.iloc[:, -2].shape # (5,)
In fact, :-1 is syntactic sugar for a slice object: slice(0, -1). In practice, the simpler syntax is preferred unless you need to pass slice objects around.
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
:-1 represents a range1, while -1 is a scalar. Ranges are 1-dimensional, while scalars are 0-dimensional. Think of a line versus a point; a range is a line while a scalar is a point. This is reflected in how Pandas translates a range vs scalar when indexing.
Therefore, the following are equivalent for a dataframe with 2 columns:
df = pd.DataFrame(np.random.random((5, 2)))
df.iloc[:, :-1].shape # (5, 1)
df.iloc[:, [0]].shape # (5, 1)
Using a scalar will remove the extra dimension. You can do this in a couple of ways:
df.iloc[:, 0].shape # (5,)
df.iloc[:, -2].shape # (5,)
In fact, :-1 is syntactic sugar for a slice object: slice(0, -1). In practice, the simpler syntax is preferred unless you need to pass slice objects around.
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
edited Dec 31 '18 at 14:51
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
answered Dec 31 '18 at 14:44
jppjpp
100k2161111
100k2161111
thx! I am new to python. I m a little crazy about how difficult it is to me. this helps so much!
– Dongyang Fu
Dec 31 '18 at 14:54
add a comment |
thx! I am new to python. I m a little crazy about how difficult it is to me. this helps so much!
– Dongyang Fu
Dec 31 '18 at 14:54
thx! I am new to python. I m a little crazy about how difficult it is to me. this helps so much!
– Dongyang Fu
Dec 31 '18 at 14:54
thx! I am new to python. I m a little crazy about how difficult it is to me. this helps so much!
– Dongyang Fu
Dec 31 '18 at 14:54
add a comment |
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