How to replace NaN values by Zeroes in a column of a Pandas Dataframe

Question

I have a Pandas Dataframe as below        itm Date                  Amount  67    420 2012-09-30 00 00 00   65211 68    421 2012-09-09 00 00 00   29424 69    421 2012-09-16 00 00 00   29877 70    421 2012-09-23 00 00 00   30990 71    421 2012-09-30 00 00 00   61303 72    485 2012-09-09 00 00 00   71781 73    485 2012-09-16 00 00 00     NaN 74    485 2012-09-23 00 00 00   11072 75    485 2012-09-30 00 00 00  113702 76    489 2012-09-09 00 00 00   64731 77    489 2012-09-16 00 00 00     NaN  When I try to apply a function to the Amount column  I get the following error  ValueError  cannot convert float NaN to integer  I have tried applying a function using  isnan from the Math Module I have tried the pandas  replace attribute I tried the  sparse data attribute from pandas 0 9 I have also tried if NaN    NaN statement in a function  I have also looked at this article How do I replace NA values with zeros in an R dataframe  whilst looking at some other articles  All the methods I have tried have not worked or do not recognise NaN  Any Hints or solutions would be appreciated

User · Answer

Easy way to fill the missing values -  filling string columns  when string columns have missing values and NaN values   df  string column name   fillna df  string column name   mode   values 0   inplace   True    filling numeric columns  when the numeric columns have missing values and NaN values   df  numeric column name   fillna df  numeric column name   mean    inplace   True    filling NaN with zero   df  column name   fillna 0  inplace   True

User · Answer

If you want to fill NaN for a specific column  you can use loc  d1     quot Col1 quot      A    B    C          quot fruits quot     Avocado    Banana    NaN    d1  pd DataFrame d1   output   Col1    fruits 0   A   Avocado 1   B   Banana 2   C   NaN   d1 loc  d1 Col1   C    fruits        Carrot    output   Col1    fruits 0   A   Avocado 1   B   Banana 2   C   Carrot

User · Answer

The below code worked for me   import pandas  df   pandas read csv  somefile txt    df   df fillna 0

User · Answer

It is not guaranteed that the slicing returns a view or a copy  You can do  df  column     df  column   fillna value

User · Answer

You can also use dictionaries to fill NaN values of the specific columns in the DataFrame rather to fill all the DF with some oneValue   import pandas as pd  df   pd read excel  example xlsx   df fillna             column1    Write your values here            column2    Write your values here            column3    Write your values here            column4    Write your values here                                          column-n    Write your values here     inplace True

User · Answer

You could use replace to change NaN to 0   import pandas as pd import numpy as np    for column df  column     df  column   replace np nan  0     for whole dataframe df   df replace np nan  0     inplace df replace np nan  0  inplace True

User · Answer

If you were to convert it to a pandas dataframe  you can also accomplish this by using fillna   import numpy as np df np array   1 2 3  np nan     import pandas as pd df pd DataFrame df  df fillna 0    This will return the following        0    1    2   3 0  1 0  2 0  3 0 NaN  gt  gt  gt  df fillna 0       0    1    2    3 0  1 0  2 0  3 0  0 0

User · Answer

To replace nan in different columns with different ways      replacement    column A   0   column B   -999   column C   -99999     df fillna value replacement

User · Answer

I just wanted to provide a bit of an update special case since it looks like people still come here  If you re using a multi-index or otherwise using an index-slicer the inplace True option may not be enough to update the slice you ve chosen  For example in a 2x2 level multi-index this will not change any values  as of pandas 0 15    idx   pd IndexSlice df loc idx   mask 1  idx mask 2     fillna value 0 inplace True    The  problem  is that the chaining breaks the fillna ability to update the original dataframe  I put  problem  in quotes because there are good reasons for the design decisions that led to not interpreting through these chains in certain situations  Also  this is a complex example  though I really ran into it   but the same may apply to fewer levels of indexes depending on how you slice   The solution is DataFrame update   df update df loc idx   mask 1  idx  mask 2      fillna value 0     It s one line  reads reasonably well  sort of  and eliminates any unnecessary messing with intermediate variables or loops while allowing you to apply fillna to any multi-level slice you like   If anybody can find places this doesn t work please post in the comments  I ve been messing with it and looking at the source and it seems to solve at least my multi-index slice problems

User · Answer

To replace na values in pandas   df  column name   fillna value to be replaced inplace True    if inplace   False  instead of updating the df  dataframe  it will return the modified values

User · Answer

I believe DataFrame fillna   will do this for you   Link to Docs for a dataframe and for a Series    Example    In  7   df Out 7              0         1 0       NaN       NaN 1 -0 494375  0 570994 2       NaN       NaN 3  1 876360 -0 229738 4       NaN       NaN  In  8   df fillna 0  Out 8              0         1 0  0 000000  0 000000 1 -0 494375  0 570994 2  0 000000  0 000000 3  1 876360 -0 229738 4  0 000000  0 000000   To fill the NaNs in only one column  select just that column  in this case I m using inplace True to actually change the contents of df    In  12   df 1  fillna 0  inplace True  Out 12    0    0 000000 1    0 570994 2    0 000000 3   -0 229738 4    0 000000 Name  1  In  13   df Out 13              0         1 0       NaN  0 000000 1 -0 494375  0 570994 2       NaN  0 000000 3  1 876360 -0 229738 4       NaN  0 000000   EDIT   To avoid a SettingWithCopyWarning  use the built in column-specific functionality   df fillna  1 0   inplace True

User · Answer

There are two options available primarily  in case of imputation or filling of missing values NaN   np nan with only numerical replacements  across column s    df  Amount   fillna value None  method   axis 1   is sufficient   From the Documentation    value   scalar  dict  Series  or DataFrame     Value to use to fill holes  e g  0   alternately a     dict Series DataFrame of values specifying which value to use for     each index  for a Series  or column  for a DataFrame    values not     in the dict Series DataFrame will not be filled   This value cannot     be a list   Which means  strings  or  constants  are no longer permissable to be imputed   For more specialized imputations use SimpleImputer     from sklearn impute import SimpleImputer si   SimpleImputer strategy  constant   missing values np nan  fill value  Replacement Value   df   Col-1    Col-2      si fit transform X df   C-1    C-2

User · Answer

Considering the particular column Amount in the above table is of integer type  The following would be a solution    df  Amount     df Amount fillna 0  astype int    Similarly  you can fill it with various data types like float  str and so on    In particular  I would consider datatype to compare various values of the same column

[python] How to replace NaN values by Zeroes in a column of a Pandas Dataframe?

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