Numpy array from pandas dataframe












1












$begingroup$


I am new in using python for data science.

What is the difference between selecting a a column with: df['name'].values and df.iloc[:,1].values and df.iloc[:,1:2].values they return differnt types of numpy vectors. why?






python dataset pandas numpy dataframe







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  • 2




    $begingroup$
    assuming 'name' is the second column. they should be identical. please includes some example codes, so we can help you.
    $endgroup$
    – Louis T
    5 hours ago


















1












$begingroup$


I am new in using python for data science.

What is the difference between selecting a a column with: df['name'].values and df.iloc[:,1].values and df.iloc[:,1:2].values they return differnt types of numpy vectors. why?






python dataset pandas numpy dataframe







share|improve this question









$endgroup$








  • 2




    $begingroup$
    assuming 'name' is the second column. they should be identical. please includes some example codes, so we can help you.
    $endgroup$
    – Louis T
    5 hours ago
















1












1








1





$begingroup$


I am new in using python for data science.

What is the difference between selecting a a column with: df['name'].values and df.iloc[:,1].values and df.iloc[:,1:2].values they return differnt types of numpy vectors. why?






python dataset pandas numpy dataframe







share|improve this question









$endgroup$




I am new in using python for data science.

What is the difference between selecting a a column with: df['name'].values and df.iloc[:,1].values and df.iloc[:,1:2].values they return differnt types of numpy vectors. why?






python dataset pandas numpy dataframe




python dataset pandas numpy dataframe






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked 14 hours ago









3nomis3nomis

1186




1186








  • 2




    $begingroup$
    assuming 'name' is the second column. they should be identical. please includes some example codes, so we can help you.
    $endgroup$
    – Louis T
    5 hours ago
















  • 2




    $begingroup$
    assuming 'name' is the second column. they should be identical. please includes some example codes, so we can help you.
    $endgroup$
    – Louis T
    5 hours ago










2




2




$begingroup$
assuming 'name' is the second column. they should be identical. please includes some example codes, so we can help you.
$endgroup$
– Louis T
5 hours ago






$begingroup$
assuming 'name' is the second column. they should be identical. please includes some example codes, so we can help you.
$endgroup$
– Louis T
5 hours ago












2 Answers
2






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$begingroup$

Assuming 'name' is the second column. they should be identical. Pandas use the 0-based index. So the first element is index 0. That might the 'gotcha' bit here.






share|improve this answer









$endgroup$





















    0












    $begingroup$

    Not entirely sure what you mean by "numpy vectors" but am assuming the question is why each of these methods return essentially (almost but not quite) the same output...



    Reference: pandas docs: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html



    df['name'].values is a "Series corresponding to colname". In other words, you're just calling the data from that column and putting the in an array by calling .values.



    .iloc is a "Purely integer-location based indexing for selection by position". Same as above but you're calling the indexed location of the column where df.iloc[:, 1] is df.iloc[all rows, col 2]. Probably an easier method to call multiple consecutive columns in a DataFrame then writing out each individual column name.



    df.iloc[:,1:2].values <-- creates an array of arrays where the main array is the column that you called (col2) and each row values is contained in a subarray. This is--I think-- because you're slicing the dataframe between column index locations 1 and 2 (rather than just calling loc 1 like above). This would mean that each row is being called individually so that a new array is created for each row that exists between column index locations 1 and 2 (which is the 'parent' array).






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      2 Answers
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      2 Answers
      2






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      0












      $begingroup$

      Assuming 'name' is the second column. they should be identical. Pandas use the 0-based index. So the first element is index 0. That might the 'gotcha' bit here.






      share|improve this answer









      $endgroup$


















        0












        $begingroup$

        Assuming 'name' is the second column. they should be identical. Pandas use the 0-based index. So the first element is index 0. That might the 'gotcha' bit here.






        share|improve this answer









        $endgroup$
















          0












          0








          0





          $begingroup$

          Assuming 'name' is the second column. they should be identical. Pandas use the 0-based index. So the first element is index 0. That might the 'gotcha' bit here.






          share|improve this answer









          $endgroup$



          Assuming 'name' is the second column. they should be identical. Pandas use the 0-based index. So the first element is index 0. That might the 'gotcha' bit here.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 5 hours ago









          Louis TLouis T

          668219




          668219























              0












              $begingroup$

              Not entirely sure what you mean by "numpy vectors" but am assuming the question is why each of these methods return essentially (almost but not quite) the same output...



              Reference: pandas docs: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html



              df['name'].values is a "Series corresponding to colname". In other words, you're just calling the data from that column and putting the in an array by calling .values.



              .iloc is a "Purely integer-location based indexing for selection by position". Same as above but you're calling the indexed location of the column where df.iloc[:, 1] is df.iloc[all rows, col 2]. Probably an easier method to call multiple consecutive columns in a DataFrame then writing out each individual column name.



              df.iloc[:,1:2].values <-- creates an array of arrays where the main array is the column that you called (col2) and each row values is contained in a subarray. This is--I think-- because you're slicing the dataframe between column index locations 1 and 2 (rather than just calling loc 1 like above). This would mean that each row is being called individually so that a new array is created for each row that exists between column index locations 1 and 2 (which is the 'parent' array).






              share|improve this answer









              $endgroup$


















                0












                $begingroup$

                Not entirely sure what you mean by "numpy vectors" but am assuming the question is why each of these methods return essentially (almost but not quite) the same output...



                Reference: pandas docs: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html



                df['name'].values is a "Series corresponding to colname". In other words, you're just calling the data from that column and putting the in an array by calling .values.



                .iloc is a "Purely integer-location based indexing for selection by position". Same as above but you're calling the indexed location of the column where df.iloc[:, 1] is df.iloc[all rows, col 2]. Probably an easier method to call multiple consecutive columns in a DataFrame then writing out each individual column name.



                df.iloc[:,1:2].values <-- creates an array of arrays where the main array is the column that you called (col2) and each row values is contained in a subarray. This is--I think-- because you're slicing the dataframe between column index locations 1 and 2 (rather than just calling loc 1 like above). This would mean that each row is being called individually so that a new array is created for each row that exists between column index locations 1 and 2 (which is the 'parent' array).






                share|improve this answer









                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  Not entirely sure what you mean by "numpy vectors" but am assuming the question is why each of these methods return essentially (almost but not quite) the same output...



                  Reference: pandas docs: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html



                  df['name'].values is a "Series corresponding to colname". In other words, you're just calling the data from that column and putting the in an array by calling .values.



                  .iloc is a "Purely integer-location based indexing for selection by position". Same as above but you're calling the indexed location of the column where df.iloc[:, 1] is df.iloc[all rows, col 2]. Probably an easier method to call multiple consecutive columns in a DataFrame then writing out each individual column name.



                  df.iloc[:,1:2].values <-- creates an array of arrays where the main array is the column that you called (col2) and each row values is contained in a subarray. This is--I think-- because you're slicing the dataframe between column index locations 1 and 2 (rather than just calling loc 1 like above). This would mean that each row is being called individually so that a new array is created for each row that exists between column index locations 1 and 2 (which is the 'parent' array).






                  share|improve this answer









                  $endgroup$



                  Not entirely sure what you mean by "numpy vectors" but am assuming the question is why each of these methods return essentially (almost but not quite) the same output...



                  Reference: pandas docs: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html



                  df['name'].values is a "Series corresponding to colname". In other words, you're just calling the data from that column and putting the in an array by calling .values.



                  .iloc is a "Purely integer-location based indexing for selection by position". Same as above but you're calling the indexed location of the column where df.iloc[:, 1] is df.iloc[all rows, col 2]. Probably an easier method to call multiple consecutive columns in a DataFrame then writing out each individual column name.



                  df.iloc[:,1:2].values <-- creates an array of arrays where the main array is the column that you called (col2) and each row values is contained in a subarray. This is--I think-- because you're slicing the dataframe between column index locations 1 and 2 (rather than just calling loc 1 like above). This would mean that each row is being called individually so that a new array is created for each row that exists between column index locations 1 and 2 (which is the 'parent' array).







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered 3 hours ago









                  Cat C.Cat C.

                  11




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