How do i replace nan with na in python?

I have a Pandas Dataframe as shown below:

    1    2       3
 0  a  NaN    read
 1  b    l  unread
 2  c  NaN    read

I want to remove the NaN values with an empty string so that it looks like so:

    1    2       3
 0  a   ""    read
 1  b    l  unread
 2  c   ""    read

asked Nov 10, 2014 at 6:29

user1452759user1452759

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0

df = df.fillna('')

This will fill na's (e.g. NaN's) with ''.

inplace is possible but should be avoided as it will be deprecated:

df.fillna('', inplace=True)

To fill only a single column:

df.column1 = df.column1.fillna('')

One can use df['column1'] instead of df.column1.

answered Feb 8, 2015 at 5:44

fantabolousfantabolous

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import numpy as np
df1 = df.replace(np.nan, '', regex=True)

This might help. It will replace all NaNs with an empty string.

Ninjakannon

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answered Nov 10, 2014 at 6:40

8

If you are reading the dataframe from a file (say CSV or Excel) then use :

df.read_csv(path , na_filter=False)

df.read_excel(path , na_filter=False)

This will automatically consider the empty fields as empty strings ''

If you already have the dataframe

df = df.replace(np.nan, '', regex=True)

df = df.fillna('')

answered Jul 19, 2017 at 15:16

Natesh bhatNatesh bhat

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Use a formatter, if you only want to format it so that it renders nicely when printed. Just use the df.to_string(... formatters to define custom string-formatting, without needlessly modifying your DataFrame or wasting memory:

df = pd.DataFrame({
    'A': ['a', 'b', 'c'],
    'B': [np.nan, 1, np.nan],
    'C': ['read', 'unread', 'read']})
print df.to_string(
    formatters={'B': lambda x: '' if pd.isnull(x) else '{:.0f}'.format(x)})

To get:

   A B       C
0  a      read
1  b 1  unread
2  c      read

smci

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answered Jun 21, 2018 at 22:41

Steve SchulistSteve Schulist

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3

Try this,

add inplace=True

import numpy as np
df.replace(np.NaN, '', inplace=True)

answered Aug 23, 2019 at 12:27

Vineesh TPVineesh TP

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1

using keep_default_na=False should help you:

df = pd.read_csv(filename, keep_default_na=False)

answered Jun 28, 2019 at 9:29

If you are converting DataFrame to JSON, NaN will give error so best solution is in this use case is to replace NaN with None.
Here is how:

df1 = df.where((pd.notnull(df)), None)

taras

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answered Mar 15, 2018 at 20:48

I tried with one column of string values with nan.

To remove the nan and fill the empty string:

df.columnname.replace(np.nan,'',regex = True)

To remove the nan and fill some values:

df.columnname.replace(np.nan,'value',regex = True)

I tried df.iloc also. but it needs the index of the column. so you need to look into the table again. simply the above method reduced one step.

answered Jul 4, 2019 at 4:07

Subbu VidyaSekarSubbu VidyaSekar

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