Convert floats to ints in Pandas

Question

I ve been working with data imported from a CSV  Pandas changed some columns to float  so now the numbers in these columns get displayed as floating points  However  I need them to be displayed as integers  or  without comma  Is there a way to convert them to integers or not display the comma

User · Answer

To convert all float columns to int  gt  gt  gt  df   pd DataFrame np random rand 5  4    10  columns list  PQRS     gt  gt  gt  print df          P           Q           R           S     0   4 395994    0 844292    8 543430    1 933934     1   0 311974    9 519054    6 171577    3 859993     2   2 056797    0 836150    5 270513    3 224497     3   3 919300    8 562298    6 852941    1 415992     4   9 958550    9 013425    8 703142    3 588733   gt  gt  gt  float col   df select dtypes include   float64      This will select float columns only  gt  gt  gt    list float col columns values    gt  gt  gt  for col in float col columns values          df col    df col  astype  int64     gt  gt  gt  print df          P   Q   R   S     0   4   0   8   1     1   0   9   6   3     2   2   0   5   3     3   3   8   6   1     4   9   9   8   3

User · Answer

Use the pandas DataFrame astype  lt type gt   function to manipulate column dtypes    gt  gt  gt  df   pd DataFrame np random rand 3 4   columns list  ABCD     gt  gt  gt  df           A         B         C         D 0  0 542447  0 949988  0 669239  0 879887 1  0 068542  0 757775  0 891903  0 384542 2  0 021274  0 587504  0 180426  0 574300  gt  gt  gt  df list  ABCD      df list  ABCD    astype int   gt  gt  gt  df    A  B  C  D 0  0  0  0  0 1  0  0  0  0 2  0  0  0  0   EDIT   To handle missing values    gt  gt  gt  df           A         B     C         D 0  0 475103  0 355453  0 66  0 869336 1  0 260395  0 200287   NaN  0 617024 2  0 517692  0 735613  0 18  0 657106  gt  gt  gt  df list  ABCD      df list  ABCD    fillna 0 0  astype int   gt  gt  gt  df    A  B  C  D 0  0  0  0  0 1  0  0  0  0 2  0  0  0  0

User · Answer

This is a quick solution in case you want to convert more columns of your pandas DataFrame from float to integer considering also the case that you can have NaN values   cols     col 1    col 2    col 3    col 4   for col in cols     df col    df col  apply lambda x  int x  if x    x else       I tried with else x  and else None   but the result is still having the float number  so I used else

User · Answer

To modify the float output do this   df  pd DataFrame range 5   columns   a    df a   df a astype float  df  Out 33              a 0 0 0000000 1 1 0000000 2 2 0000000 3 3 0000000 4 4 0000000  pd options display float format        0f   format df  Out 35       a 0  0 1  1 2  2 3  3 4  4

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gt  gt  gt  import pandas as pd  gt  gt  gt  right   pd DataFrame   C    1 002  2 003    D    1 009  4 55    key     K0    K1      gt  gt  gt  print right             C      D key     0  1 002  1 009  K0     1  2 003  4 550  K1  gt  gt  gt  right  C     right C astype int   gt  gt  gt  print right         C      D key     0  1  1 009  K0     1  2  4 550  K1

User · Answer

Considering the following data frame    gt  gt  gt  df   pd DataFrame 10 np random rand 3  4   columns list  ABCD     gt  gt  gt  print df                A         B         C         D     0  8 362940  0 354027  1 916283  6 226750     1  1 988232  9 003545  9 277504  8 522808     2  1 141432  4 935593  2 700118  7 739108   Using a list of column names  change the type for multiple columns with applymap      gt  gt  gt  cols     A    B    gt  gt  gt  df cols    df cols  applymap np int64   gt  gt  gt  print df         A  B         C         D     0  8  0  1 916283  6 226750     1  1  9  9 277504  8 522808     2  1  4  2 700118  7 739108   Or for a single column with apply      gt  gt  gt  df  C     df  C   apply np int64   gt  gt  gt  print df         A  B  C         D     0  8  0  1  6 226750     1  1  9  9  8 522808     2  1  4  2  7 739108

User · Answer

The columns that needs to be converted to int can be mentioned in a dictionary also as below  df   df astype   col1    int    col2    int    col3    int

User · Answer

Here s a simple function that will downcast floats into the smallest possible integer type that doesn t lose any information   For examples    100 0 can be converted from float to integer  but 99 9 can t  without losing information to rounding or truncation  Additionally  1 0 can be downcast all the way to int8 without losing information  but the smallest integer type for 100 000 0 is int32   Code examples   import numpy as np import pandas as pd  def float to int  s        if   s astype np int64     s   all            return pd to numeric  s  downcast  integer        else          return s    small integers are downcast into 8-bit integers float to int  np array  1 0 2 0     Out 1  array  1  2   dtype int8     larger integers are downcast into larger integer types float to int  np array  100 000  200 000      Out 2   array  100000  200000   dtype int32     if there are values to the right of the decimal   point  no conversion is made float to int  np array  1 1 2 2     Out 3   array   1 1   2 2

User · Answer

Expanding on  Ryan G mentioned usage of the pandas DataFrame astype  lt type gt   method  one can use the errors ignore argument to only convert those columns that do not produce an error  which notably simplifies the syntax  Obviously  caution should be applied when ignoring errors  but for this task it comes very handy   gt  gt  gt  df   pd DataFrame np random rand 3  4   columns list  ABCD     gt  gt  gt  df    10  gt  gt  gt  print df                A       B       C       D     0   2 16861 8 34139 1 83434 6 91706     1   5 85938 9 71712 5 53371 4 26542     2   0 50112 4 06725 1 99795 4 75698   gt  gt  gt  df  E     list  XYZ    gt  gt  gt  df astype int  errors  ignore    gt  gt  gt  print df          A   B   C   D   E     0   2   8   1   6   X     1   5   9   5   4   Y     2   0   4   1   4   Z  From pandas DataFrame astype docs   errors       raise        ignore      default    raise    Control raising of exceptions on invalid data for provided dtype   raise   allow exceptions to be raised ignore   suppress exceptions  On error return original object  New in version 0 20 0

[python] Convert floats to ints in Pandas?

To convert all float columns to int

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