How to copy dataframe while keeping C-Contiguos arrangement of data?
1
pandas dataframe copy method returns a dataframe with data organized as F-Contiguos, even if the original dataframe was arranged as C-Contiguos.
Example:
In [1]: import numpy
...: import pandas
...: easy_matrix_example = numpy.array([
...: [100, 20200, 20900 ],
...: [200, 80200, 80900 ],
...: [300, 180200, 180900 ],
...: [400, 320200, 320900 ],
...: [500, 500200, 500900 ],
...: ], dtype=numpy.float64)
...: easy_df_example = pandas.DataFrame(easy_matrix_example, columns=["A","B","C"])
...:
...:
In [2]: easy_df_example
Out[2]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [5]: easy_df_example.values.flags
Out[5]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
In [6]: df_copy = easy_df_example.copy()
In [7]: df_copy
Out[7]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [8]: df_copy.values.flags
Out[8]:
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
What's the recommended approach to copying a C-Contiguos dataframe and obtaining another C-Contiguous dataframe?
python pandas dataframe
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
add a comment |
1
pandas dataframe copy method returns a dataframe with data organized as F-Contiguos, even if the original dataframe was arranged as C-Contiguos.
Example:
In [1]: import numpy
...: import pandas
...: easy_matrix_example = numpy.array([
...: [100, 20200, 20900 ],
...: [200, 80200, 80900 ],
...: [300, 180200, 180900 ],
...: [400, 320200, 320900 ],
...: [500, 500200, 500900 ],
...: ], dtype=numpy.float64)
...: easy_df_example = pandas.DataFrame(easy_matrix_example, columns=["A","B","C"])
...:
...:
In [2]: easy_df_example
Out[2]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [5]: easy_df_example.values.flags
Out[5]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
In [6]: df_copy = easy_df_example.copy()
In [7]: df_copy
Out[7]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [8]: df_copy.values.flags
Out[8]:
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
What's the recommended approach to copying a C-Contiguos dataframe and obtaining another C-Contiguous dataframe?
python pandas dataframe
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
add a comment |
1
1
1
pandas dataframe copy method returns a dataframe with data organized as F-Contiguos, even if the original dataframe was arranged as C-Contiguos.
Example:
In [1]: import numpy
...: import pandas
...: easy_matrix_example = numpy.array([
...: [100, 20200, 20900 ],
...: [200, 80200, 80900 ],
...: [300, 180200, 180900 ],
...: [400, 320200, 320900 ],
...: [500, 500200, 500900 ],
...: ], dtype=numpy.float64)
...: easy_df_example = pandas.DataFrame(easy_matrix_example, columns=["A","B","C"])
...:
...:
In [2]: easy_df_example
Out[2]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [5]: easy_df_example.values.flags
Out[5]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
In [6]: df_copy = easy_df_example.copy()
In [7]: df_copy
Out[7]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [8]: df_copy.values.flags
Out[8]:
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
What's the recommended approach to copying a C-Contiguos dataframe and obtaining another C-Contiguous dataframe?
python pandas dataframe
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
pandas dataframe copy method returns a dataframe with data organized as F-Contiguos, even if the original dataframe was arranged as C-Contiguos.
Example:
In [1]: import numpy
...: import pandas
...: easy_matrix_example = numpy.array([
...: [100, 20200, 20900 ],
...: [200, 80200, 80900 ],
...: [300, 180200, 180900 ],
...: [400, 320200, 320900 ],
...: [500, 500200, 500900 ],
...: ], dtype=numpy.float64)
...: easy_df_example = pandas.DataFrame(easy_matrix_example, columns=["A","B","C"])
...:
...:
In [2]: easy_df_example
Out[2]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [5]: easy_df_example.values.flags
Out[5]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
In [6]: df_copy = easy_df_example.copy()
In [7]: df_copy
Out[7]:
A B C
0 100.0 20200.0 20900.0
1 200.0 80200.0 80900.0
2 300.0 180200.0 180900.0
3 400.0 320200.0 320900.0
4 500.0 500200.0 500900.0
In [8]: df_copy.values.flags
Out[8]:
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
What's the recommended approach to copying a C-Contiguos dataframe and obtaining another C-Contiguous dataframe?
python pandas dataframe
python pandas dataframe
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
asked Nov 22 '18 at 23:25
AlechanAlechan
113112
113112
add a comment |
add a comment |
1 Answer
1
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oldest
votes
0
you can create a new object copying the underlying value yourself.
example:
df = pd.DataFrame(np.random.random(12).reshape(4,3), columns=list('abc'))
df2 = pd.DataFrame(df.values.copy(), columns=df.columns)
df2.values.flags
outputs:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
what about the index, dtype, etc?
– Alechan
Nov 22 '18 at 23:46
the dtypes should carry over automatically, if you want to copy the index over as well, pass inindex=df.indexto the dataframe constructor
– Haleemur Ali
Nov 22 '18 at 23:52
what I meant was that the copy method took care of copying everything to make both dataframes equivalent. If I make the copy "by hand" then I have to make sure to copy all the metadata correctly and that may result in errors and my code is coupled to the pandas DataFrame representation.
– Alechan
Nov 23 '18 at 0:06
add a comment |
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1 Answer
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1 Answer
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oldest
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0
you can create a new object copying the underlying value yourself.
example:
df = pd.DataFrame(np.random.random(12).reshape(4,3), columns=list('abc'))
df2 = pd.DataFrame(df.values.copy(), columns=df.columns)
df2.values.flags
outputs:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
what about the index, dtype, etc?
– Alechan
Nov 22 '18 at 23:46
the dtypes should carry over automatically, if you want to copy the index over as well, pass inindex=df.indexto the dataframe constructor
– Haleemur Ali
Nov 22 '18 at 23:52
what I meant was that the copy method took care of copying everything to make both dataframes equivalent. If I make the copy "by hand" then I have to make sure to copy all the metadata correctly and that may result in errors and my code is coupled to the pandas DataFrame representation.
– Alechan
Nov 23 '18 at 0:06
add a comment |
0
you can create a new object copying the underlying value yourself.
example:
df = pd.DataFrame(np.random.random(12).reshape(4,3), columns=list('abc'))
df2 = pd.DataFrame(df.values.copy(), columns=df.columns)
df2.values.flags
outputs:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
what about the index, dtype, etc?
– Alechan
Nov 22 '18 at 23:46
the dtypes should carry over automatically, if you want to copy the index over as well, pass inindex=df.indexto the dataframe constructor
– Haleemur Ali
Nov 22 '18 at 23:52
what I meant was that the copy method took care of copying everything to make both dataframes equivalent. If I make the copy "by hand" then I have to make sure to copy all the metadata correctly and that may result in errors and my code is coupled to the pandas DataFrame representation.
– Alechan
Nov 23 '18 at 0:06
add a comment |
0
0
0
you can create a new object copying the underlying value yourself.
example:
df = pd.DataFrame(np.random.random(12).reshape(4,3), columns=list('abc'))
df2 = pd.DataFrame(df.values.copy(), columns=df.columns)
df2.values.flags
outputs:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
you can create a new object copying the underlying value yourself.
example:
df = pd.DataFrame(np.random.random(12).reshape(4,3), columns=list('abc'))
df2 = pd.DataFrame(df.values.copy(), columns=df.columns)
df2.values.flags
outputs:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
answered Nov 22 '18 at 23:45
Haleemur AliHaleemur Ali
12.3k21739
12.3k21739
what about the index, dtype, etc?
– Alechan
Nov 22 '18 at 23:46
the dtypes should carry over automatically, if you want to copy the index over as well, pass inindex=df.indexto the dataframe constructor
– Haleemur Ali
Nov 22 '18 at 23:52
what I meant was that the copy method took care of copying everything to make both dataframes equivalent. If I make the copy "by hand" then I have to make sure to copy all the metadata correctly and that may result in errors and my code is coupled to the pandas DataFrame representation.
– Alechan
Nov 23 '18 at 0:06
add a comment |
what about the index, dtype, etc?
– Alechan
Nov 22 '18 at 23:46
the dtypes should carry over automatically, if you want to copy the index over as well, pass inindex=df.indexto the dataframe constructor
– Haleemur Ali
Nov 22 '18 at 23:52
what I meant was that the copy method took care of copying everything to make both dataframes equivalent. If I make the copy "by hand" then I have to make sure to copy all the metadata correctly and that may result in errors and my code is coupled to the pandas DataFrame representation.
– Alechan
Nov 23 '18 at 0:06
what about the index, dtype, etc?
– Alechan
Nov 22 '18 at 23:46
what about the index, dtype, etc?
– Alechan
Nov 22 '18 at 23:46
the dtypes should carry over automatically, if you want to copy the index over as well, pass in
index=df.index to the dataframe constructor– Haleemur Ali
Nov 22 '18 at 23:52
the dtypes should carry over automatically, if you want to copy the index over as well, pass in
index=df.index to the dataframe constructor– Haleemur Ali
Nov 22 '18 at 23:52
what I meant was that the copy method took care of copying everything to make both dataframes equivalent. If I make the copy "by hand" then I have to make sure to copy all the metadata correctly and that may result in errors and my code is coupled to the pandas DataFrame representation.
– Alechan
Nov 23 '18 at 0:06
what I meant was that the copy method took care of copying everything to make both dataframes equivalent. If I make the copy "by hand" then I have to make sure to copy all the metadata correctly and that may result in errors and my code is coupled to the pandas DataFrame representation.
– Alechan
Nov 23 '18 at 0:06
add a comment |
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