Formatting floats in a numpy array

Question

If I have a numpy array like this    2 15295647e 01  8 12531501e 00  3 97113829e 00  1 00777250e 01    how can I move the decimal point and format the numbers so I end up with a numpy array like this    21 53  8 13  3 97  10 08    np around a  decimals 2  only gives me  2 15300000e 01  8 13000000e 00  3 97000000e 00  1 00800000e 01  Which I don t want and I haven t found another way to do it

User · Answer

round x 2  for x in  2 15295647e 01  8 12531501e 00  3 97113829e 00  1 00777250e 01

User · Answer

You can use round function  Here some example  numpy round  2 15295647e 01  8 12531501e 00  3 97113829e 00  1 00777250e 01  2  array   21 53    8 13    3 97   10 08     IF you want change just display representation  I would not recommended to alter printing format globally  as it suggested above  I would format my output in place    gt  gt a np array  2 15295647e 01  8 12531501e 00  3 97113829e 00  1 00777250e 01    gt  gt  gt  print      0 2f   format x  for x in a      21 53    8 13    3 97    10 08

User · Answer

You re confusing actual precision and display precision  Decimal rounding cannot be represented exactly in binary  You should try    gt  np set printoptions precision 2   gt  np array  5 333333   array   5 33

User · Answer

In order to make numpy display float arrays in an arbitrary format  you can define a custom function that takes a float value as its input and returns a formatted string   In  1   float formatter       2f   format   The f here means fixed-point format  not  scientific    and the  2 means two decimal places  you can read more about string formatting here    Let s test it out with a float value   In  2   float formatter 1 234567E3  Out 2    1234 57    To make numpy print all float arrays this way  you can pass the formatter  argument to np set printoptions   In  3   np set printoptions formatter   float kind  float formatter     Now numpy will print all float arrays this way   In  4   np random randn 5    10 Out 4   array  5 25  3 91  0 04  -1 53  6 68    Note that this only affects numpy arrays  not scalars   In  5   np pi Out 5   3 141592653589793   It also won t affect non-floats  complex floats etc - you will need to define separate formatters for other scalar types   You should also be aware that this only affects how numpy displays float values - the actual values that will be used in computations will retain their original precision   For example   In  6   a   np array  1E-9    In  7   a Out 7   array  0 00    In  8   a    0 Out 8   array  False   dtype bool    numpy prints a as if it were equal to 0  but it is not - it still equals 1E-9   If you actually want to round the values in your array in a way that affects how they will be used in calculations  you should use np round  as others have already pointed out

[python] Formatting floats in a numpy array

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