Convert columns to string in Pandas

Question

I have the following DataFrame from a SQL query    Pdb  pp total rows      ColumnID  RespondentCount 0          -1                2 1  3030096843                1 2  3030096845                1   and I want to pivot it like this   total data   total rows pivot table cols   ColumnID      Pdb  pp total data ColumnID         -1            3030096843   3030096845 RespondentCount            2            1            1   1 rows x 3 columns    total rows pivot table cols   ColumnID    to dict  records   0    3030096843  1  3030096845  1  -1  2    but I want to make sure the 303 columns are casted as strings instead of integers so that I get this     3030096843   1   3030096845   1  -1  2

User · Answer

If you need to convert ALL columns to strings, you can simply use:

df = df.astype(str)

This is useful if you need everything except a few columns to be strings/objects, then go back and convert the other ones to whatever you need (integer in this case):

 df[["D", "E"]] = df[["D", "E"]].astype(int)

User · Answer

pandas  gt   1 0  It s time to stop using astype str   Prior to pandas 1 0  well  0 25 actually  this was the defacto way of declaring a Series column as as string    pandas  lt   0 25   Note to pedants  specifying the type is unnecessary since pandas will    automagically infer the type as object s   pd Series   a    b    c    dtype str  s dtype   dtype  O     From pandas 1 0 onwards  consider using  quot string quot  type instead    pandas  gt   1 0 s   pd Series   a    b    c    dtype  quot string quot   s dtype   StringDtype  Here s why  as quoted by the docs    You can accidentally store a mixture of strings and non-strings in an object dtype array  It   s better to have a dedicated dtype   object dtype breaks dtype-specific operations like DataFrame select dtypes    There isn   t a clear way to select just text while excluding non-text but still object-dtype columns   When reading code  the contents of an object dtype array is less clear than  string      See also the section on Behavioral Differences between  quot string quot  and object  Extension types  introduced in 0 24 and formalized in 1 0  are closer to pandas than numpy  which is good because numpy types are not powerful enough  For example NumPy does not have any way of representing missing data in integer data  since type NaN     float   But pandas can using Nullable Integer columns   Why should I stop using it  Accidentally mixing dtypes The first reason  as outlined in the docs is that you can accidentally store non-text data in object columns    pandas  lt   0 25 pd Series   a    b   1 23       whoops  this should have been  quot 1 23 quot   0       a 1       b 2    1 23 dtype  object  pd Series   a    b   1 23   tolist       a    b   1 23      oops  pandas was storing this as float all the time      pandas  gt   1 0 pd Series   a    b   1 23   dtype  quot string quot    0       a 1       b 2    1 23 dtype  string  pd Series   a    b   1 23   dtype  quot string quot   tolist       a    b    1 23       it s a string and we just averted some potentially nasty bugs   Challenging to differentiate strings and other python objects Another obvious example example is that it s harder to distinguish between  quot strings quot  and  quot objects quot   Objects are essentially the blanket type for any type that does not support vectorizable operations  Consider    Setup df   pd DataFrame   A     a    b    c     B         1  2  3   123    df      A          B 0  a            1  b   1  2  3  2  c        123  Upto pandas 0 25  there was virtually no way to distinguish that  quot A quot  and  quot B quot  do not have the same type of data    pandas  lt   0 25   df dtypes  A    object B    object dtype  object  df select dtypes object      A          B 0  a            1  b   1  2  3  2  c        123  From pandas 1 0  this becomes a lot simpler    pandas  gt   1 0   Convenience function I call to help illustrate my point  df   df convert dtypes   df dtypes  A    string B    object dtype  object  df select dtypes  quot string quot       A 0  a 1  b 2  c  Readability This is self-explanatory  -   OK  so should I stop using it right now     No  As of writing this answer  version 1 1   there are no performance benefits but the docs expect future enhancements to significantly improve performance and reduce memory usage for  quot string quot  columns as opposed to objects  With that said  however  it s never too early to form good habits

User · Answer

I usually use this one  pd  Column   map str

User · Answer

Here s the other one  particularly useful to convert the multiple columns to string instead of just single column   In  76   import numpy as np In  77   import pandas as pd In  78   df   pd DataFrame                 A    20  30 0  np nan                 B     a45a    a3    b1                  C    10  5  np nan               In  79   df dtypes    Current datatype Out 79    A    float64 B     object C    float64 dtype  object     Multiple columns string conversion In  80   df   A    C      df   A    C    astype str    In  81   df dtypes    Updated datatype after string conversion Out 81    A    object B    object C    object dtype  object

User · Answer

Using  apply   with a lambda conversion function also works in this case   total rows  ColumnID     total rows  ColumnID   apply lambda x  str x    For entire dataframes you can use  applymap     but in any case probably  astype   is faster

User · Answer

One way to convert to string is to use astype   total rows  ColumnID     total rows  ColumnID   astype str    However  perhaps you are looking for the to json function  which will convert keys to valid json  and therefore your keys to strings    In  11   df   pd DataFrame    A   2     A   4     B   6     In  12   df to json   Out 12      0    0   A   1   A   2   B    1    0  2  1  4  2  6     In  13   df 0  to json   Out 13      0   A   1   A   2   B      Note  you can pass in a buffer file to save this to  along with some other options

[python] Convert columns to string in Pandas

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