Handling Zeros or NaNs in a Pandas DataFrame operations












0














I have a DataFrame (df) like shown below where each column is sorted from largest to smallest for frequency analysis. That leaves some values either zeros or NaN values as each column has a different length.



   08FB006 08FC001 08FC003 08FC005 08GD004
----------------------------------------------
0 253 872 256 11.80 2660
1 250 850 255 10.60 2510
2 246 850 241 10.30 2130
3 241 827 235 9.32 1970
4 241 821 229 9.17 1900
5 232 0 228 8.93 1840
6 231 0 225 8.05 1710
7 0 0 225 0 1610
8 0 0 224 0 1590
9 0 0 0 0 1590
10 0 0 0 0 1550


I need to perform the following calculation as if each column has different lengths or number of records (ignoring zero values). I have tried using NaN but for some reason operations on Nan values are not possible.



Here is what I am trying to do with my df columns :



shape_list1=
location_list1=
scale_list1=

for column in df.columns:
shape1, location1, scale1=stats.genpareto.fit(df[column])

shape_list1.append(shape1)
location_list1.append(location1)
scale_list1.append(scale1)









share|improve this question





























    0














    I have a DataFrame (df) like shown below where each column is sorted from largest to smallest for frequency analysis. That leaves some values either zeros or NaN values as each column has a different length.



       08FB006 08FC001 08FC003 08FC005 08GD004
    ----------------------------------------------
    0 253 872 256 11.80 2660
    1 250 850 255 10.60 2510
    2 246 850 241 10.30 2130
    3 241 827 235 9.32 1970
    4 241 821 229 9.17 1900
    5 232 0 228 8.93 1840
    6 231 0 225 8.05 1710
    7 0 0 225 0 1610
    8 0 0 224 0 1590
    9 0 0 0 0 1590
    10 0 0 0 0 1550


    I need to perform the following calculation as if each column has different lengths or number of records (ignoring zero values). I have tried using NaN but for some reason operations on Nan values are not possible.



    Here is what I am trying to do with my df columns :



    shape_list1=
    location_list1=
    scale_list1=

    for column in df.columns:
    shape1, location1, scale1=stats.genpareto.fit(df[column])

    shape_list1.append(shape1)
    location_list1.append(location1)
    scale_list1.append(scale1)









    share|improve this question



























      0












      0








      0







      I have a DataFrame (df) like shown below where each column is sorted from largest to smallest for frequency analysis. That leaves some values either zeros or NaN values as each column has a different length.



         08FB006 08FC001 08FC003 08FC005 08GD004
      ----------------------------------------------
      0 253 872 256 11.80 2660
      1 250 850 255 10.60 2510
      2 246 850 241 10.30 2130
      3 241 827 235 9.32 1970
      4 241 821 229 9.17 1900
      5 232 0 228 8.93 1840
      6 231 0 225 8.05 1710
      7 0 0 225 0 1610
      8 0 0 224 0 1590
      9 0 0 0 0 1590
      10 0 0 0 0 1550


      I need to perform the following calculation as if each column has different lengths or number of records (ignoring zero values). I have tried using NaN but for some reason operations on Nan values are not possible.



      Here is what I am trying to do with my df columns :



      shape_list1=
      location_list1=
      scale_list1=

      for column in df.columns:
      shape1, location1, scale1=stats.genpareto.fit(df[column])

      shape_list1.append(shape1)
      location_list1.append(location1)
      scale_list1.append(scale1)









      share|improve this question















      I have a DataFrame (df) like shown below where each column is sorted from largest to smallest for frequency analysis. That leaves some values either zeros or NaN values as each column has a different length.



         08FB006 08FC001 08FC003 08FC005 08GD004
      ----------------------------------------------
      0 253 872 256 11.80 2660
      1 250 850 255 10.60 2510
      2 246 850 241 10.30 2130
      3 241 827 235 9.32 1970
      4 241 821 229 9.17 1900
      5 232 0 228 8.93 1840
      6 231 0 225 8.05 1710
      7 0 0 225 0 1610
      8 0 0 224 0 1590
      9 0 0 0 0 1590
      10 0 0 0 0 1550


      I need to perform the following calculation as if each column has different lengths or number of records (ignoring zero values). I have tried using NaN but for some reason operations on Nan values are not possible.



      Here is what I am trying to do with my df columns :



      shape_list1=
      location_list1=
      scale_list1=

      for column in df.columns:
      shape1, location1, scale1=stats.genpareto.fit(df[column])

      shape_list1.append(shape1)
      location_list1.append(location1)
      scale_list1.append(scale1)






      python pandas nan zero






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      edited Nov 22 at 22:00

























      asked Nov 22 at 21:44









      Sina Shabani

      287




      287
























          2 Answers
          2






          active

          oldest

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          1














          Assuming all values are positive (as seems from your example and description), try:



          stats.genpareto.fit(df[df[column] > 0][column])


          This filters every column to operate just on the positive values.
          Or, if negative values are allowed,



          stats.genpareto.fit(df[df[column] != 0][column])





          share|improve this answer

















          • 2




            Thanks both of these answers worked :)
            – Sina Shabani
            Nov 22 at 22:13












          • @andersource's syntax is a lot cleaner than mine!
            – Peter Leimbigler
            Nov 22 at 22:41



















          0














          The syntax is messy, but change



          shape1, location1, scale1=stats.genpareto.fit(df[column])


          to



          shape1, location1, scale1=stats.genpareto.fit(df[column][df[column].nonzero()[0]])


          Explanation: df[column].nonzero() returns a tuple of size (1,) whose only element, element [0], is a numpy array that holds the index labels where df is nonzero. To index df[column] by these nonzero labels, you can use df[column][df[column].nonzero()[0]].






          share|improve this answer





















          • Thanks for the explanation :)
            – Sina Shabani
            Nov 22 at 22:13











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          2 Answers
          2






          active

          oldest

          votes








          2 Answers
          2






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          1














          Assuming all values are positive (as seems from your example and description), try:



          stats.genpareto.fit(df[df[column] > 0][column])


          This filters every column to operate just on the positive values.
          Or, if negative values are allowed,



          stats.genpareto.fit(df[df[column] != 0][column])





          share|improve this answer

















          • 2




            Thanks both of these answers worked :)
            – Sina Shabani
            Nov 22 at 22:13












          • @andersource's syntax is a lot cleaner than mine!
            – Peter Leimbigler
            Nov 22 at 22:41
















          1














          Assuming all values are positive (as seems from your example and description), try:



          stats.genpareto.fit(df[df[column] > 0][column])


          This filters every column to operate just on the positive values.
          Or, if negative values are allowed,



          stats.genpareto.fit(df[df[column] != 0][column])





          share|improve this answer

















          • 2




            Thanks both of these answers worked :)
            – Sina Shabani
            Nov 22 at 22:13












          • @andersource's syntax is a lot cleaner than mine!
            – Peter Leimbigler
            Nov 22 at 22:41














          1












          1








          1






          Assuming all values are positive (as seems from your example and description), try:



          stats.genpareto.fit(df[df[column] > 0][column])


          This filters every column to operate just on the positive values.
          Or, if negative values are allowed,



          stats.genpareto.fit(df[df[column] != 0][column])





          share|improve this answer












          Assuming all values are positive (as seems from your example and description), try:



          stats.genpareto.fit(df[df[column] > 0][column])


          This filters every column to operate just on the positive values.
          Or, if negative values are allowed,



          stats.genpareto.fit(df[df[column] != 0][column])






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 22 at 22:06









          andersource

          37416




          37416








          • 2




            Thanks both of these answers worked :)
            – Sina Shabani
            Nov 22 at 22:13












          • @andersource's syntax is a lot cleaner than mine!
            – Peter Leimbigler
            Nov 22 at 22:41














          • 2




            Thanks both of these answers worked :)
            – Sina Shabani
            Nov 22 at 22:13












          • @andersource's syntax is a lot cleaner than mine!
            – Peter Leimbigler
            Nov 22 at 22:41








          2




          2




          Thanks both of these answers worked :)
          – Sina Shabani
          Nov 22 at 22:13






          Thanks both of these answers worked :)
          – Sina Shabani
          Nov 22 at 22:13














          @andersource's syntax is a lot cleaner than mine!
          – Peter Leimbigler
          Nov 22 at 22:41




          @andersource's syntax is a lot cleaner than mine!
          – Peter Leimbigler
          Nov 22 at 22:41













          0














          The syntax is messy, but change



          shape1, location1, scale1=stats.genpareto.fit(df[column])


          to



          shape1, location1, scale1=stats.genpareto.fit(df[column][df[column].nonzero()[0]])


          Explanation: df[column].nonzero() returns a tuple of size (1,) whose only element, element [0], is a numpy array that holds the index labels where df is nonzero. To index df[column] by these nonzero labels, you can use df[column][df[column].nonzero()[0]].






          share|improve this answer





















          • Thanks for the explanation :)
            – Sina Shabani
            Nov 22 at 22:13
















          0














          The syntax is messy, but change



          shape1, location1, scale1=stats.genpareto.fit(df[column])


          to



          shape1, location1, scale1=stats.genpareto.fit(df[column][df[column].nonzero()[0]])


          Explanation: df[column].nonzero() returns a tuple of size (1,) whose only element, element [0], is a numpy array that holds the index labels where df is nonzero. To index df[column] by these nonzero labels, you can use df[column][df[column].nonzero()[0]].






          share|improve this answer





















          • Thanks for the explanation :)
            – Sina Shabani
            Nov 22 at 22:13














          0












          0








          0






          The syntax is messy, but change



          shape1, location1, scale1=stats.genpareto.fit(df[column])


          to



          shape1, location1, scale1=stats.genpareto.fit(df[column][df[column].nonzero()[0]])


          Explanation: df[column].nonzero() returns a tuple of size (1,) whose only element, element [0], is a numpy array that holds the index labels where df is nonzero. To index df[column] by these nonzero labels, you can use df[column][df[column].nonzero()[0]].






          share|improve this answer












          The syntax is messy, but change



          shape1, location1, scale1=stats.genpareto.fit(df[column])


          to



          shape1, location1, scale1=stats.genpareto.fit(df[column][df[column].nonzero()[0]])


          Explanation: df[column].nonzero() returns a tuple of size (1,) whose only element, element [0], is a numpy array that holds the index labels where df is nonzero. To index df[column] by these nonzero labels, you can use df[column][df[column].nonzero()[0]].







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 22 at 22:06









          Peter Leimbigler

          3,7041415




          3,7041415












          • Thanks for the explanation :)
            – Sina Shabani
            Nov 22 at 22:13


















          • Thanks for the explanation :)
            – Sina Shabani
            Nov 22 at 22:13
















          Thanks for the explanation :)
          – Sina Shabani
          Nov 22 at 22:13




          Thanks for the explanation :)
          – Sina Shabani
          Nov 22 at 22:13


















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