Pandas keep_default_na=False does not work












2














Issue



I have an Excel file (.xlsx) that contains a sheet with some values equals to '#N/A'.





When reading the Excel sheet as a DataFrame using pandas, '#N/A' values are interpreted as NaN.



Based on the pandas.read_excel guide, I expect that '#N/A' can be read 'as is' into the DataFrame by adding the keep_default_na=False parameter.



Unfortunately '#N/A' is still interpreted as NaN.



Code



Here is the code used:



df = pd.read_excel(io='TestWorkbook.xlsx',

              sheet_name="Sheet1",

              keep_default_na=False)


And the result:





It seems that keep_default_na=False worked on 'N/A' and 'NA' values but not '#N/A'.



Question



Do you know any workaround to reading '#N/A' as-is into the DataFrame?






python excel pandas







share|improve this question
























  • Could you upload your excel sheet somewhere so I can try it?
    – Scotty1-
    Nov 19 '18 at 13:43










  • @Scotty1- Probably not a good idea to download random files on the internet ;)
    – J100
    Nov 19 '18 at 14:02










  • True, but that would have made it easier to try finding a solution. :)
    – Scotty1-
    Nov 19 '18 at 14:08










  • worth exploring na_filter=False instead keep_default_na=False
    – pygo
    Nov 19 '18 at 14:32


















2














Issue



I have an Excel file (.xlsx) that contains a sheet with some values equals to '#N/A'.





When reading the Excel sheet as a DataFrame using pandas, '#N/A' values are interpreted as NaN.



Based on the pandas.read_excel guide, I expect that '#N/A' can be read 'as is' into the DataFrame by adding the keep_default_na=False parameter.



Unfortunately '#N/A' is still interpreted as NaN.



Code



Here is the code used:



df = pd.read_excel(io='TestWorkbook.xlsx',

              sheet_name="Sheet1",

              keep_default_na=False)


And the result:





It seems that keep_default_na=False worked on 'N/A' and 'NA' values but not '#N/A'.



Question



Do you know any workaround to reading '#N/A' as-is into the DataFrame?






python excel pandas







share|improve this question
























  • Could you upload your excel sheet somewhere so I can try it?
    – Scotty1-
    Nov 19 '18 at 13:43










  • @Scotty1- Probably not a good idea to download random files on the internet ;)
    – J100
    Nov 19 '18 at 14:02










  • True, but that would have made it easier to try finding a solution. :)
    – Scotty1-
    Nov 19 '18 at 14:08










  • worth exploring na_filter=False instead keep_default_na=False
    – pygo
    Nov 19 '18 at 14:32
















2












2








2







Issue



I have an Excel file (.xlsx) that contains a sheet with some values equals to '#N/A'.





When reading the Excel sheet as a DataFrame using pandas, '#N/A' values are interpreted as NaN.



Based on the pandas.read_excel guide, I expect that '#N/A' can be read 'as is' into the DataFrame by adding the keep_default_na=False parameter.



Unfortunately '#N/A' is still interpreted as NaN.



Code



Here is the code used:



df = pd.read_excel(io='TestWorkbook.xlsx',

              sheet_name="Sheet1",

              keep_default_na=False)


And the result:





It seems that keep_default_na=False worked on 'N/A' and 'NA' values but not '#N/A'.



Question



Do you know any workaround to reading '#N/A' as-is into the DataFrame?






python excel pandas







share|improve this question















Issue



I have an Excel file (.xlsx) that contains a sheet with some values equals to '#N/A'.





When reading the Excel sheet as a DataFrame using pandas, '#N/A' values are interpreted as NaN.



Based on the pandas.read_excel guide, I expect that '#N/A' can be read 'as is' into the DataFrame by adding the keep_default_na=False parameter.



Unfortunately '#N/A' is still interpreted as NaN.



Code



Here is the code used:



df = pd.read_excel(io='TestWorkbook.xlsx',

              sheet_name="Sheet1",

              keep_default_na=False)


And the result:





It seems that keep_default_na=False worked on 'N/A' and 'NA' values but not '#N/A'.



Question



Do you know any workaround to reading '#N/A' as-is into the DataFrame?






python excel pandas




python excel pandas






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 19 '18 at 13:50









jpp

91.9k2052102




91.9k2052102










asked Nov 19 '18 at 13:25









J100

951413




951413












  • Could you upload your excel sheet somewhere so I can try it?
    – Scotty1-
    Nov 19 '18 at 13:43










  • @Scotty1- Probably not a good idea to download random files on the internet ;)
    – J100
    Nov 19 '18 at 14:02










  • True, but that would have made it easier to try finding a solution. :)
    – Scotty1-
    Nov 19 '18 at 14:08










  • worth exploring na_filter=False instead keep_default_na=False
    – pygo
    Nov 19 '18 at 14:32




















  • Could you upload your excel sheet somewhere so I can try it?
    – Scotty1-
    Nov 19 '18 at 13:43










  • @Scotty1- Probably not a good idea to download random files on the internet ;)
    – J100
    Nov 19 '18 at 14:02










  • True, but that would have made it easier to try finding a solution. :)
    – Scotty1-
    Nov 19 '18 at 14:08










  • worth exploring na_filter=False instead keep_default_na=False
    – pygo
    Nov 19 '18 at 14:32


















Could you upload your excel sheet somewhere so I can try it?
– Scotty1-
Nov 19 '18 at 13:43




Could you upload your excel sheet somewhere so I can try it?
– Scotty1-
Nov 19 '18 at 13:43












@Scotty1- Probably not a good idea to download random files on the internet ;)
– J100
Nov 19 '18 at 14:02




@Scotty1- Probably not a good idea to download random files on the internet ;)
– J100
Nov 19 '18 at 14:02












True, but that would have made it easier to try finding a solution. :)
– Scotty1-
Nov 19 '18 at 14:08




True, but that would have made it easier to try finding a solution. :)
– Scotty1-
Nov 19 '18 at 14:08












worth exploring na_filter=False instead keep_default_na=False
– pygo
Nov 19 '18 at 14:32






worth exploring na_filter=False instead keep_default_na=False
– pygo
Nov 19 '18 at 14:32














2 Answers
2






active

oldest

votes


















1














That's because Excel isn't storing those #N/A values in column B as strings. There's a visual indication of this if you notice those #N/A cells are centre-aligned.



Pandas won't differentiate between different types of Excel errors: #N/A / #NUM! / #NAME? / #VALUE! etc will all come through as NaN. Which makes sense, there isn't a parallel Python/C type for every Excel error.



So, in short, with pd.read_excel there's nothing you can do except override all errors with a specific string, e.g. '#N/A', and lose all knowledge of the specific error type(s) you find by opening the file in Excel:



df['Column2'] = df['Column2'].fillna('#N/A')


The alternative is to force Excel to use text values, e.g. by inserting into an Excel cell:



=TEXT("#N/A", "")


Then read using pd.read_excel with keep_default_na=False. This seems more trouble than it's worth.






share|improve this answer























  • Oh right. But why is my solution working on my computer? Is there some Excel-internals for defining NA-values depending on the region or version number of Excel? I'm using a german version of excel 2010 and #N/A is definitely stored as a string even when the format of the cell is chosen as number format...
    – Scotty1-
    Nov 19 '18 at 13:41










  • @Scotty1-, No idea. Neither of your solutions work for me.
    – jpp
    Nov 19 '18 at 13:42










  • Will not that fillna('#N/A') will replace all Nan to #N/A regardless if its NaN or #N/A
    – pygo
    Nov 19 '18 at 13:43






  • 1




    @pygo, No, fillna will only replace NaN with '#N/A'. It won't replace '#N/A' because those are strings (not NaN).
    – jpp
    Nov 19 '18 at 13:45








  • 1




    @pygo- How will that preserve the '#N/A'? Specifying it in na_values is basically telling pandas to interpret '#N/A' as NaN.
    – J100
    Nov 19 '18 at 14:01



















0














Try:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values='', keep_default_na=False

)


If you specify keep_default_na=False, the values given in na_values will overwrite the default NA-values. Since your NA-values are in the default NA-values, you need to specify some na_values='some_dummy_na_value' the use this to overwrite the default NA-values.



If you for example want to keep interpreting N/A and NA as NA-values (while keeping #N/A as a string), you can specify them in the na_values parameter:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values=['N/A', 'NA'], keep_default_na=False

)





share|improve this answer



















  • 1




    No luck. Thanks for the suggestion.
    – J100
    Nov 19 '18 at 13:30










  • You are welcome. But it should work. I just tried it... In my case #N/A is loaded into the df as a string '#N/A'.
    – Scotty1-
    Nov 19 '18 at 13:33










  • That's odd. I don't suppose my operating system and installed libraries have anything to do with it. I'll set up another virtual environment and with new installation of pandas and other necessary libraries before trying again.
    – J100
    Nov 19 '18 at 13:36











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2 Answers
2






active

oldest

votes








2 Answers
2






active

oldest

votes









active

oldest

votes






active

oldest

votes









1














That's because Excel isn't storing those #N/A values in column B as strings. There's a visual indication of this if you notice those #N/A cells are centre-aligned.



Pandas won't differentiate between different types of Excel errors: #N/A / #NUM! / #NAME? / #VALUE! etc will all come through as NaN. Which makes sense, there isn't a parallel Python/C type for every Excel error.



So, in short, with pd.read_excel there's nothing you can do except override all errors with a specific string, e.g. '#N/A', and lose all knowledge of the specific error type(s) you find by opening the file in Excel:



df['Column2'] = df['Column2'].fillna('#N/A')


The alternative is to force Excel to use text values, e.g. by inserting into an Excel cell:



=TEXT("#N/A", "")


Then read using pd.read_excel with keep_default_na=False. This seems more trouble than it's worth.






share|improve this answer























  • Oh right. But why is my solution working on my computer? Is there some Excel-internals for defining NA-values depending on the region or version number of Excel? I'm using a german version of excel 2010 and #N/A is definitely stored as a string even when the format of the cell is chosen as number format...
    – Scotty1-
    Nov 19 '18 at 13:41










  • @Scotty1-, No idea. Neither of your solutions work for me.
    – jpp
    Nov 19 '18 at 13:42










  • Will not that fillna('#N/A') will replace all Nan to #N/A regardless if its NaN or #N/A
    – pygo
    Nov 19 '18 at 13:43






  • 1




    @pygo, No, fillna will only replace NaN with '#N/A'. It won't replace '#N/A' because those are strings (not NaN).
    – jpp
    Nov 19 '18 at 13:45








  • 1




    @pygo- How will that preserve the '#N/A'? Specifying it in na_values is basically telling pandas to interpret '#N/A' as NaN.
    – J100
    Nov 19 '18 at 14:01
















1














That's because Excel isn't storing those #N/A values in column B as strings. There's a visual indication of this if you notice those #N/A cells are centre-aligned.



Pandas won't differentiate between different types of Excel errors: #N/A / #NUM! / #NAME? / #VALUE! etc will all come through as NaN. Which makes sense, there isn't a parallel Python/C type for every Excel error.



So, in short, with pd.read_excel there's nothing you can do except override all errors with a specific string, e.g. '#N/A', and lose all knowledge of the specific error type(s) you find by opening the file in Excel:



df['Column2'] = df['Column2'].fillna('#N/A')


The alternative is to force Excel to use text values, e.g. by inserting into an Excel cell:



=TEXT("#N/A", "")


Then read using pd.read_excel with keep_default_na=False. This seems more trouble than it's worth.






share|improve this answer























  • Oh right. But why is my solution working on my computer? Is there some Excel-internals for defining NA-values depending on the region or version number of Excel? I'm using a german version of excel 2010 and #N/A is definitely stored as a string even when the format of the cell is chosen as number format...
    – Scotty1-
    Nov 19 '18 at 13:41










  • @Scotty1-, No idea. Neither of your solutions work for me.
    – jpp
    Nov 19 '18 at 13:42










  • Will not that fillna('#N/A') will replace all Nan to #N/A regardless if its NaN or #N/A
    – pygo
    Nov 19 '18 at 13:43






  • 1




    @pygo, No, fillna will only replace NaN with '#N/A'. It won't replace '#N/A' because those are strings (not NaN).
    – jpp
    Nov 19 '18 at 13:45








  • 1




    @pygo- How will that preserve the '#N/A'? Specifying it in na_values is basically telling pandas to interpret '#N/A' as NaN.
    – J100
    Nov 19 '18 at 14:01














1












1








1






That's because Excel isn't storing those #N/A values in column B as strings. There's a visual indication of this if you notice those #N/A cells are centre-aligned.



Pandas won't differentiate between different types of Excel errors: #N/A / #NUM! / #NAME? / #VALUE! etc will all come through as NaN. Which makes sense, there isn't a parallel Python/C type for every Excel error.



So, in short, with pd.read_excel there's nothing you can do except override all errors with a specific string, e.g. '#N/A', and lose all knowledge of the specific error type(s) you find by opening the file in Excel:



df['Column2'] = df['Column2'].fillna('#N/A')


The alternative is to force Excel to use text values, e.g. by inserting into an Excel cell:



=TEXT("#N/A", "")


Then read using pd.read_excel with keep_default_na=False. This seems more trouble than it's worth.






share|improve this answer














That's because Excel isn't storing those #N/A values in column B as strings. There's a visual indication of this if you notice those #N/A cells are centre-aligned.



Pandas won't differentiate between different types of Excel errors: #N/A / #NUM! / #NAME? / #VALUE! etc will all come through as NaN. Which makes sense, there isn't a parallel Python/C type for every Excel error.



So, in short, with pd.read_excel there's nothing you can do except override all errors with a specific string, e.g. '#N/A', and lose all knowledge of the specific error type(s) you find by opening the file in Excel:



df['Column2'] = df['Column2'].fillna('#N/A')


The alternative is to force Excel to use text values, e.g. by inserting into an Excel cell:



=TEXT("#N/A", "")


Then read using pd.read_excel with keep_default_na=False. This seems more trouble than it's worth.







share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 19 '18 at 14:03

























answered Nov 19 '18 at 13:37









jpp

91.9k2052102




91.9k2052102












  • Oh right. But why is my solution working on my computer? Is there some Excel-internals for defining NA-values depending on the region or version number of Excel? I'm using a german version of excel 2010 and #N/A is definitely stored as a string even when the format of the cell is chosen as number format...
    – Scotty1-
    Nov 19 '18 at 13:41










  • @Scotty1-, No idea. Neither of your solutions work for me.
    – jpp
    Nov 19 '18 at 13:42










  • Will not that fillna('#N/A') will replace all Nan to #N/A regardless if its NaN or #N/A
    – pygo
    Nov 19 '18 at 13:43






  • 1




    @pygo, No, fillna will only replace NaN with '#N/A'. It won't replace '#N/A' because those are strings (not NaN).
    – jpp
    Nov 19 '18 at 13:45








  • 1




    @pygo- How will that preserve the '#N/A'? Specifying it in na_values is basically telling pandas to interpret '#N/A' as NaN.
    – J100
    Nov 19 '18 at 14:01


















  • Oh right. But why is my solution working on my computer? Is there some Excel-internals for defining NA-values depending on the region or version number of Excel? I'm using a german version of excel 2010 and #N/A is definitely stored as a string even when the format of the cell is chosen as number format...
    – Scotty1-
    Nov 19 '18 at 13:41










  • @Scotty1-, No idea. Neither of your solutions work for me.
    – jpp
    Nov 19 '18 at 13:42










  • Will not that fillna('#N/A') will replace all Nan to #N/A regardless if its NaN or #N/A
    – pygo
    Nov 19 '18 at 13:43






  • 1




    @pygo, No, fillna will only replace NaN with '#N/A'. It won't replace '#N/A' because those are strings (not NaN).
    – jpp
    Nov 19 '18 at 13:45








  • 1




    @pygo- How will that preserve the '#N/A'? Specifying it in na_values is basically telling pandas to interpret '#N/A' as NaN.
    – J100
    Nov 19 '18 at 14:01
















Oh right. But why is my solution working on my computer? Is there some Excel-internals for defining NA-values depending on the region or version number of Excel? I'm using a german version of excel 2010 and #N/A is definitely stored as a string even when the format of the cell is chosen as number format...
– Scotty1-
Nov 19 '18 at 13:41




Oh right. But why is my solution working on my computer? Is there some Excel-internals for defining NA-values depending on the region or version number of Excel? I'm using a german version of excel 2010 and #N/A is definitely stored as a string even when the format of the cell is chosen as number format...
– Scotty1-
Nov 19 '18 at 13:41












@Scotty1-, No idea. Neither of your solutions work for me.
– jpp
Nov 19 '18 at 13:42




@Scotty1-, No idea. Neither of your solutions work for me.
– jpp
Nov 19 '18 at 13:42












Will not that fillna('#N/A') will replace all Nan to #N/A regardless if its NaN or #N/A
– pygo
Nov 19 '18 at 13:43




Will not that fillna('#N/A') will replace all Nan to #N/A regardless if its NaN or #N/A
– pygo
Nov 19 '18 at 13:43




1




1




@pygo, No, fillna will only replace NaN with '#N/A'. It won't replace '#N/A' because those are strings (not NaN).
– jpp
Nov 19 '18 at 13:45






@pygo, No, fillna will only replace NaN with '#N/A'. It won't replace '#N/A' because those are strings (not NaN).
– jpp
Nov 19 '18 at 13:45






1




1




@pygo- How will that preserve the '#N/A'? Specifying it in na_values is basically telling pandas to interpret '#N/A' as NaN.
– J100
Nov 19 '18 at 14:01




@pygo- How will that preserve the '#N/A'? Specifying it in na_values is basically telling pandas to interpret '#N/A' as NaN.
– J100
Nov 19 '18 at 14:01













0














Try:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values='', keep_default_na=False

)


If you specify keep_default_na=False, the values given in na_values will overwrite the default NA-values. Since your NA-values are in the default NA-values, you need to specify some na_values='some_dummy_na_value' the use this to overwrite the default NA-values.



If you for example want to keep interpreting N/A and NA as NA-values (while keeping #N/A as a string), you can specify them in the na_values parameter:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values=['N/A', 'NA'], keep_default_na=False

)





share|improve this answer



















  • 1




    No luck. Thanks for the suggestion.
    – J100
    Nov 19 '18 at 13:30










  • You are welcome. But it should work. I just tried it... In my case #N/A is loaded into the df as a string '#N/A'.
    – Scotty1-
    Nov 19 '18 at 13:33










  • That's odd. I don't suppose my operating system and installed libraries have anything to do with it. I'll set up another virtual environment and with new installation of pandas and other necessary libraries before trying again.
    – J100
    Nov 19 '18 at 13:36
















0














Try:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values='', keep_default_na=False

)


If you specify keep_default_na=False, the values given in na_values will overwrite the default NA-values. Since your NA-values are in the default NA-values, you need to specify some na_values='some_dummy_na_value' the use this to overwrite the default NA-values.



If you for example want to keep interpreting N/A and NA as NA-values (while keeping #N/A as a string), you can specify them in the na_values parameter:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values=['N/A', 'NA'], keep_default_na=False

)





share|improve this answer



















  • 1




    No luck. Thanks for the suggestion.
    – J100
    Nov 19 '18 at 13:30










  • You are welcome. But it should work. I just tried it... In my case #N/A is loaded into the df as a string '#N/A'.
    – Scotty1-
    Nov 19 '18 at 13:33










  • That's odd. I don't suppose my operating system and installed libraries have anything to do with it. I'll set up another virtual environment and with new installation of pandas and other necessary libraries before trying again.
    – J100
    Nov 19 '18 at 13:36














0












0








0






Try:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values='', keep_default_na=False

)


If you specify keep_default_na=False, the values given in na_values will overwrite the default NA-values. Since your NA-values are in the default NA-values, you need to specify some na_values='some_dummy_na_value' the use this to overwrite the default NA-values.



If you for example want to keep interpreting N/A and NA as NA-values (while keeping #N/A as a string), you can specify them in the na_values parameter:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values=['N/A', 'NA'], keep_default_na=False

)





share|improve this answer














Try:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values='', keep_default_na=False

)


If you specify keep_default_na=False, the values given in na_values will overwrite the default NA-values. Since your NA-values are in the default NA-values, you need to specify some na_values='some_dummy_na_value' the use this to overwrite the default NA-values.



If you for example want to keep interpreting N/A and NA as NA-values (while keeping #N/A as a string), you can specify them in the na_values parameter:



df = pd.read_excel(

    io='TestWorkbook.xlsx',

    sheet_name="Sheet1",

    na_values=['N/A', 'NA'], keep_default_na=False

)






share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 19 '18 at 13:37

























answered Nov 19 '18 at 13:27









Scotty1-

1,4461321




1,4461321








  • 1




    No luck. Thanks for the suggestion.
    – J100
    Nov 19 '18 at 13:30










  • You are welcome. But it should work. I just tried it... In my case #N/A is loaded into the df as a string '#N/A'.
    – Scotty1-
    Nov 19 '18 at 13:33










  • That's odd. I don't suppose my operating system and installed libraries have anything to do with it. I'll set up another virtual environment and with new installation of pandas and other necessary libraries before trying again.
    – J100
    Nov 19 '18 at 13:36














  • 1




    No luck. Thanks for the suggestion.
    – J100
    Nov 19 '18 at 13:30










  • You are welcome. But it should work. I just tried it... In my case #N/A is loaded into the df as a string '#N/A'.
    – Scotty1-
    Nov 19 '18 at 13:33










  • That's odd. I don't suppose my operating system and installed libraries have anything to do with it. I'll set up another virtual environment and with new installation of pandas and other necessary libraries before trying again.
    – J100
    Nov 19 '18 at 13:36








1




1




No luck. Thanks for the suggestion.
– J100
Nov 19 '18 at 13:30




No luck. Thanks for the suggestion.
– J100
Nov 19 '18 at 13:30












You are welcome. But it should work. I just tried it... In my case #N/A is loaded into the df as a string '#N/A'.
– Scotty1-
Nov 19 '18 at 13:33




You are welcome. But it should work. I just tried it... In my case #N/A is loaded into the df as a string '#N/A'.
– Scotty1-
Nov 19 '18 at 13:33












That's odd. I don't suppose my operating system and installed libraries have anything to do with it. I'll set up another virtual environment and with new installation of pandas and other necessary libraries before trying again.
– J100
Nov 19 '18 at 13:36




That's odd. I don't suppose my operating system and installed libraries have anything to do with it. I'll set up another virtual environment and with new installation of pandas and other necessary libraries before trying again.
– J100
Nov 19 '18 at 13:36


















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