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BUG: Shift on DataFrame which has more than 1 block creates wrong result #35488

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@qinxuye

Description

@qinxuye
  • I have checked that this issue has not already been reported.

  • I have confirmed this bug exists on the latest version of pandas.

  • (optional) I have confirmed this bug exists on the master branch of pandas.


Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

# Your code here
In [1]: import pandas as pd                                                     

In [2]: import numpy as np                                                      

In [3]: df1 = pd.DataFrame(np.random.randint(1000, size=(5, 3)))                

In [4]: df2 = pd.DataFrame(np.random.randint(1000, size=(5, 2)))                

In [5]: df3 = pd.concat([df1, df2], axis=1)                                     

In [6]: df3                                                                     
Out[6]: 
     0    1    2    0    1
0   61  536  154  766  179
1  484   18   15  787  766
2  391  171  715  836  654
3  914  969  765  824  950
4  169  414  759   16  666

In [7]: len(df3._data.blocks)                                                   
Out[7]: 2

In [9]: df3.shift(2, axis=1)                                                    
Out[9]: 
    0   1      2   0   1
0 NaN NaN   61.0 NaN NaN
1 NaN NaN  484.0 NaN NaN
2 NaN NaN  391.0 NaN NaN
3 NaN NaN  914.0 NaN NaN
4 NaN NaN  169.0 NaN NaN

Problem description

[this should explain why the current behaviour is a problem and why the expected output is a better solution]

I guess shift is applied to both of the internal blocks.

Expected Output

I forced consolidate, the result is right.

In [12]: df3._data._consolidate_inplace()                                       

In [13]: df3.shift(2, axis=1)                                                   
Out[13]: 
    0   1      2      0      1
0 NaN NaN   61.0  536.0  154.0
1 NaN NaN  484.0   18.0   15.0
2 NaN NaN  391.0  171.0  715.0
3 NaN NaN  914.0  969.0  765.0
4 NaN NaN  169.0  414.0  759.0

Output of pd.show_versions()

In [14]: pd.show_versions()
/Users/qinxuye/miniconda3/envs/test_pandas_1.1/lib/python3.7/site-packages/setuptools/distutils_patch.py:26: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.
"Distutils was imported before Setuptools. This usage is discouraged "

ImportError Traceback (most recent call last)
in
----> 1 pd.show_versions()

~/miniconda3/envs/test_pandas_1.1/lib/python3.7/site-packages/pandas/util/_print_versions.py in show_versions(as_json)
104 """
105 sys_info = _get_sys_info()
--> 106 deps = _get_dependency_info()
107
108 if as_json:

~/miniconda3/envs/test_pandas_1.1/lib/python3.7/site-packages/pandas/util/_print_versions.py in _get_dependency_info()
82 for modname in deps:
83 mod = import_optional_dependency(
---> 84 modname, raise_on_missing=False, on_version="ignore"
85 )
86 result[modname] = _get_version(mod) if mod else None

~/miniconda3/envs/test_pandas_1.1/lib/python3.7/site-packages/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version)
97 minimum_version = VERSIONS.get(name)
98 if minimum_version:
---> 99 version = _get_version(module)
100 if distutils.version.LooseVersion(version) < minimum_version:
101 assert on_version in {"warn", "raise", "ignore"}

~/miniconda3/envs/test_pandas_1.1/lib/python3.7/site-packages/pandas/compat/_optional.py in _get_version(module)
42
43 if version is None:
---> 44 raise ImportError(f"Can't determine version for {module.name}")
45 return version
46

ImportError: Can't determine version for numba

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    BugInternalsRelated to non-user accessible pandas implementationRegressionFunctionality that used to work in a prior pandas version

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