[python] Pandas Replace NaN with blank/empty string

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

df = df.fillna('')

or just

df.fillna('', inplace=True)

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

If you want to fill a single column, you can use:

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

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

using keep_default_na=False should help you:

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

Try this,

add inplace=True

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

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('')

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

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.

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)