pandas to_csv() fails to save the results












0














I am using Pandas for creating a data frame that reads values from a comma separated file initially. The CSV file contains a dataset related to the employees. The data from the csv file is normalized between the range of 0-1 using MinMaxScaler. The normalization step works fine and the print statement prints out the normalizaed results of the attributes. But when I try to save these results (normalized values) against each attribute in a new CSV file, it creates a new file "Employees_modified" with the same values as that of the "Employees" dataset that were given as input to the MinMaxScaler function. I am new to Pandas and couldn't figure out where possibly is the mistake.
My code is given below:



import pandas as pd

from sklearn.preprocessing import MinMaxScaler

dframe = pd.read_csv('Employees.csv')

one_scaler = MinMaxScaler(feature_range=(0, 1))

one_scaler.partial_fit(dframe)

a_scaled = one_scaler.transform(dframe)

print(a_scaled)

dframe.to_csv('Employees_modified.csv')


I want to save normalized values in my Employees_modified file but the results are being lost somewhere in between.






python pandas csv







share|improve this question




















  • 1




    What do you mean it's lost? Do you get any output file at all? What's in it and what do you expect to be in it?
    – kabdulla
    Nov 22 at 19:47






  • 2




    you're saving the original dataframe, not the result of the transformation...
    – godot
    Nov 22 at 19:52
















0














I am using Pandas for creating a data frame that reads values from a comma separated file initially. The CSV file contains a dataset related to the employees. The data from the csv file is normalized between the range of 0-1 using MinMaxScaler. The normalization step works fine and the print statement prints out the normalizaed results of the attributes. But when I try to save these results (normalized values) against each attribute in a new CSV file, it creates a new file "Employees_modified" with the same values as that of the "Employees" dataset that were given as input to the MinMaxScaler function. I am new to Pandas and couldn't figure out where possibly is the mistake.
My code is given below:



import pandas as pd

from sklearn.preprocessing import MinMaxScaler

dframe = pd.read_csv('Employees.csv')

one_scaler = MinMaxScaler(feature_range=(0, 1))

one_scaler.partial_fit(dframe)

a_scaled = one_scaler.transform(dframe)

print(a_scaled)

dframe.to_csv('Employees_modified.csv')


I want to save normalized values in my Employees_modified file but the results are being lost somewhere in between.






python pandas csv







share|improve this question




















  • 1




    What do you mean it's lost? Do you get any output file at all? What's in it and what do you expect to be in it?
    – kabdulla
    Nov 22 at 19:47






  • 2




    you're saving the original dataframe, not the result of the transformation...
    – godot
    Nov 22 at 19:52














0












0








0







I am using Pandas for creating a data frame that reads values from a comma separated file initially. The CSV file contains a dataset related to the employees. The data from the csv file is normalized between the range of 0-1 using MinMaxScaler. The normalization step works fine and the print statement prints out the normalizaed results of the attributes. But when I try to save these results (normalized values) against each attribute in a new CSV file, it creates a new file "Employees_modified" with the same values as that of the "Employees" dataset that were given as input to the MinMaxScaler function. I am new to Pandas and couldn't figure out where possibly is the mistake.
My code is given below:



import pandas as pd

from sklearn.preprocessing import MinMaxScaler

dframe = pd.read_csv('Employees.csv')

one_scaler = MinMaxScaler(feature_range=(0, 1))

one_scaler.partial_fit(dframe)

a_scaled = one_scaler.transform(dframe)

print(a_scaled)

dframe.to_csv('Employees_modified.csv')


I want to save normalized values in my Employees_modified file but the results are being lost somewhere in between.






python pandas csv







share|improve this question















I am using Pandas for creating a data frame that reads values from a comma separated file initially. The CSV file contains a dataset related to the employees. The data from the csv file is normalized between the range of 0-1 using MinMaxScaler. The normalization step works fine and the print statement prints out the normalizaed results of the attributes. But when I try to save these results (normalized values) against each attribute in a new CSV file, it creates a new file "Employees_modified" with the same values as that of the "Employees" dataset that were given as input to the MinMaxScaler function. I am new to Pandas and couldn't figure out where possibly is the mistake.
My code is given below:



import pandas as pd

from sklearn.preprocessing import MinMaxScaler

dframe = pd.read_csv('Employees.csv')

one_scaler = MinMaxScaler(feature_range=(0, 1))

one_scaler.partial_fit(dframe)

a_scaled = one_scaler.transform(dframe)

print(a_scaled)

dframe.to_csv('Employees_modified.csv')


I want to save normalized values in my Employees_modified file but the results are being lost somewhere in between.






python pandas csv




python pandas csv






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 24 at 5:15









jwodder

32.8k35081




32.8k35081










asked Nov 22 at 19:38









user399

45




45








  • 1




    What do you mean it's lost? Do you get any output file at all? What's in it and what do you expect to be in it?
    – kabdulla
    Nov 22 at 19:47






  • 2




    you're saving the original dataframe, not the result of the transformation...
    – godot
    Nov 22 at 19:52














  • 1




    What do you mean it's lost? Do you get any output file at all? What's in it and what do you expect to be in it?
    – kabdulla
    Nov 22 at 19:47






  • 2




    you're saving the original dataframe, not the result of the transformation...
    – godot
    Nov 22 at 19:52








1




1




What do you mean it's lost? Do you get any output file at all? What's in it and what do you expect to be in it?
– kabdulla
Nov 22 at 19:47




What do you mean it's lost? Do you get any output file at all? What's in it and what do you expect to be in it?
– kabdulla
Nov 22 at 19:47




2




2




you're saving the original dataframe, not the result of the transformation...
– godot
Nov 22 at 19:52




you're saving the original dataframe, not the result of the transformation...
– godot
Nov 22 at 19:52












2 Answers
2






active

oldest

votes


















0














You are writing the original dataframe to the file.
The code below should do what you want:



import pandas as pd

from sklearn.preprocessing import MinMaxScaler

dframe = pd.read_csv('Employees.csv')

one_scaler = MinMaxScaler(feature_range=(0, 1))

one_scaler.partial_fit(dframe)

a_scaled = one_scaler.transform(dframe)

print(a_scaled)

#creates a dataframe from the scaled data

pd.DataFrame(a_scaled, columns=list(dframe)).to_csv('Employees_modified.csv')





share|improve this answer





























    0














    Try:



    one_scaler = MinMaxScaler(feature_range=(0, 1), copy=False)





    share|improve this answer





















    • thank you for providing an answer. Please provide more information as to how the changes you've proposed in your answer will help to fix the issue in question.
      – Bryce Siedschlaw
      Nov 22 at 21:29










    • Try to run the same code just changing the line I pointed out. By default, MinMaxScaler operates on a copy of your dataFrame. So when you print your original dataframe nothing changed. The argument 'copy=False' tells the scaler to not copy, but operate over your original dataframe. I hope it helps.
      – AResem
      Nov 22 at 23:06










    • @AResem I appreciate your contribution but your proposed solution didn't make any change to my Employee_modified file
      – user399
      Nov 23 at 0:55











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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    0














    You are writing the original dataframe to the file.
    The code below should do what you want:



    import pandas as pd
    
from sklearn.preprocessing import MinMaxScaler
    
dframe = pd.read_csv('Employees.csv')
    
one_scaler = MinMaxScaler(feature_range=(0, 1))
    
one_scaler.partial_fit(dframe)
    
a_scaled = one_scaler.transform(dframe)
    
print(a_scaled)
    
#creates a dataframe from the scaled data
    
pd.DataFrame(a_scaled, columns=list(dframe)).to_csv('Employees_modified.csv')





    share|improve this answer


























      0














      You are writing the original dataframe to the file.
      The code below should do what you want:



      import pandas as pd
      
from sklearn.preprocessing import MinMaxScaler
      
dframe = pd.read_csv('Employees.csv')
      
one_scaler = MinMaxScaler(feature_range=(0, 1))
      
one_scaler.partial_fit(dframe)
      
a_scaled = one_scaler.transform(dframe)
      
print(a_scaled)
      
#creates a dataframe from the scaled data
      
pd.DataFrame(a_scaled, columns=list(dframe)).to_csv('Employees_modified.csv')





      share|improve this answer
























        0












        0








        0






        You are writing the original dataframe to the file.
        The code below should do what you want:



        import pandas as pd
        
from sklearn.preprocessing import MinMaxScaler
        
dframe = pd.read_csv('Employees.csv')
        
one_scaler = MinMaxScaler(feature_range=(0, 1))
        
one_scaler.partial_fit(dframe)
        
a_scaled = one_scaler.transform(dframe)
        
print(a_scaled)
        
#creates a dataframe from the scaled data
        
pd.DataFrame(a_scaled, columns=list(dframe)).to_csv('Employees_modified.csv')





        share|improve this answer












        You are writing the original dataframe to the file.
        The code below should do what you want:



        import pandas as pd
        
from sklearn.preprocessing import MinMaxScaler
        
dframe = pd.read_csv('Employees.csv')
        
one_scaler = MinMaxScaler(feature_range=(0, 1))
        
one_scaler.partial_fit(dframe)
        
a_scaled = one_scaler.transform(dframe)
        
print(a_scaled)
        
#creates a dataframe from the scaled data
        
pd.DataFrame(a_scaled, columns=list(dframe)).to_csv('Employees_modified.csv')






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 22 at 19:55









        Pedro Torres

        683413




        683413

























            0














            Try:



            one_scaler = MinMaxScaler(feature_range=(0, 1), copy=False)





            share|improve this answer





















            • thank you for providing an answer. Please provide more information as to how the changes you've proposed in your answer will help to fix the issue in question.
              – Bryce Siedschlaw
              Nov 22 at 21:29










            • Try to run the same code just changing the line I pointed out. By default, MinMaxScaler operates on a copy of your dataFrame. So when you print your original dataframe nothing changed. The argument 'copy=False' tells the scaler to not copy, but operate over your original dataframe. I hope it helps.
              – AResem
              Nov 22 at 23:06










            • @AResem I appreciate your contribution but your proposed solution didn't make any change to my Employee_modified file
              – user399
              Nov 23 at 0:55
















            0














            Try:



            one_scaler = MinMaxScaler(feature_range=(0, 1), copy=False)





            share|improve this answer





















            • thank you for providing an answer. Please provide more information as to how the changes you've proposed in your answer will help to fix the issue in question.
              – Bryce Siedschlaw
              Nov 22 at 21:29










            • Try to run the same code just changing the line I pointed out. By default, MinMaxScaler operates on a copy of your dataFrame. So when you print your original dataframe nothing changed. The argument 'copy=False' tells the scaler to not copy, but operate over your original dataframe. I hope it helps.
              – AResem
              Nov 22 at 23:06










            • @AResem I appreciate your contribution but your proposed solution didn't make any change to my Employee_modified file
              – user399
              Nov 23 at 0:55














            0












            0








            0






            Try:



            one_scaler = MinMaxScaler(feature_range=(0, 1), copy=False)





            share|improve this answer












            Try:



            one_scaler = MinMaxScaler(feature_range=(0, 1), copy=False)






            share|improve this answer












            share|improve this answer



            share|improve this answer










            answered Nov 22 at 19:53









            AResem

            1114




            1114












            • thank you for providing an answer. Please provide more information as to how the changes you've proposed in your answer will help to fix the issue in question.
              – Bryce Siedschlaw
              Nov 22 at 21:29










            • Try to run the same code just changing the line I pointed out. By default, MinMaxScaler operates on a copy of your dataFrame. So when you print your original dataframe nothing changed. The argument 'copy=False' tells the scaler to not copy, but operate over your original dataframe. I hope it helps.
              – AResem
              Nov 22 at 23:06










            • @AResem I appreciate your contribution but your proposed solution didn't make any change to my Employee_modified file
              – user399
              Nov 23 at 0:55


















            • thank you for providing an answer. Please provide more information as to how the changes you've proposed in your answer will help to fix the issue in question.
              – Bryce Siedschlaw
              Nov 22 at 21:29










            • Try to run the same code just changing the line I pointed out. By default, MinMaxScaler operates on a copy of your dataFrame. So when you print your original dataframe nothing changed. The argument 'copy=False' tells the scaler to not copy, but operate over your original dataframe. I hope it helps.
              – AResem
              Nov 22 at 23:06










            • @AResem I appreciate your contribution but your proposed solution didn't make any change to my Employee_modified file
              – user399
              Nov 23 at 0:55
















            thank you for providing an answer. Please provide more information as to how the changes you've proposed in your answer will help to fix the issue in question.
            – Bryce Siedschlaw
            Nov 22 at 21:29




            thank you for providing an answer. Please provide more information as to how the changes you've proposed in your answer will help to fix the issue in question.
            – Bryce Siedschlaw
            Nov 22 at 21:29












            Try to run the same code just changing the line I pointed out. By default, MinMaxScaler operates on a copy of your dataFrame. So when you print your original dataframe nothing changed. The argument 'copy=False' tells the scaler to not copy, but operate over your original dataframe. I hope it helps.
            – AResem
            Nov 22 at 23:06




            Try to run the same code just changing the line I pointed out. By default, MinMaxScaler operates on a copy of your dataFrame. So when you print your original dataframe nothing changed. The argument 'copy=False' tells the scaler to not copy, but operate over your original dataframe. I hope it helps.
            – AResem
            Nov 22 at 23:06












            @AResem I appreciate your contribution but your proposed solution didn't make any change to my Employee_modified file
            – user399
            Nov 23 at 0:55




            @AResem I appreciate your contribution but your proposed solution didn't make any change to my Employee_modified file
            – user399
            Nov 23 at 0:55


















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