Creating a zero-filled pandas data frame

Question

What is the best way to create a zero-filled pandas data frame of a given size   I have used    zero data   np zeros shape  len data  len feature list    d   pd DataFrame zero data  columns feature list    Is there a better way to do it

User · Answer

If you would like the new data frame to have the same index and columns as an existing data frame  you can just multiply the existing data frame by zero   df zeros   df   0

User · Answer

If you already have a dataframe  this is the fastest way   In  1   columns     col    format i  for i in range 10   In  2   orig df   pd DataFrame np ones  10  10    columns columns  In  3    timeit d   pd DataFrame np zeros like orig df   index orig df index  columns orig df columns  10000 loops  best of 3  60 2   s per loop   Compare to   In  4    timeit d   pd DataFrame 0  index   np arange 10   columns columns  10000 loops  best of 3  110   s per loop  In  5   temp   np zeros  10  10   In  6    timeit d   pd DataFrame temp  columns columns  10000 loops  best of 3  95 7   s per loop

User · Answer

You can try this   d   pd DataFrame 0  index np arange len data    columns feature list

User · Answer

Assuming having a template DataFrame  which one would like to copy with zero values filled here     If you have no NaNs in your data set  multiplying by zero can be significantly faster   In  19   columns     col    format i  for i in xrange 3000                                                                                           In  20   indices   xrange 2000   In  21   orig df   pd DataFrame 42 0  index indices  columns columns   In  22    timeit d   pd DataFrame np zeros like orig df   index orig df index  columns orig df columns  100 loops  best of 3  12 6 ms per loop  In  23    timeit d   orig df   0 0 100 loops  best of 3  7 17 ms per loop   Improvement depends on DataFrame size  but never found it slower   And just for the heck of it   In  24    timeit d   orig df   0 0   1 0 100 loops  best of 3  13 6 ms per loop  In  25    timeit d   pd eval  orig df   0 0   1 0   100 loops  best of 3  8 36 ms per loop   But   In  24    timeit d   orig df copy   10 loops  best of 3  24 ms per loop   EDIT     Assuming you have a frame using float64  this will be the fastest by a huge margin  It is also able to generate any value by replacing 0 0 to the desired fill number   In  23    timeit d   pd eval  orig df  gt  1 7976931348623157e 308   0 0   100 loops  best of 3  3 68 ms per loop   Depending on taste  one can externally define nan  and do a general solution  irrespective of the particular float type   In  39   nan   np nan In  40    timeit d   pd eval  orig df  gt  nan   0 0   100 loops  best of 3  4 39 ms per loop

User · Answer

Similar to  Shravan  but without the use of numpy     height   10   width   20   df 0   pd DataFrame 0  index range height   columns range width     Then you can do whatever you want with it   post instantiation fcn   lambda x  str x  df ready for whatever   df 0 applymap post instantiation fcn

User · Answer

It s best to do this with numpy in my opinion  import numpy as np import pandas as pd d   pd DataFrame np zeros  N rows  N cols

[python] Creating a zero-filled pandas data frame

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