Pandas Replace NaN with blank/empty string

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Question :

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

Answer #1:

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

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

Answered By: nEO

Answer #2:

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.

Answered By: fantabolous

Answer #3:

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 By: Natesh bhat

Answer #4:

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
Answered By: Steve Schulist

Answer #5:

Try this,

add inplace=True

import numpy as np
df.replace(np.NaN, ' ', inplace=True)
Answered By: Vineesh TP

Answer #6:

using keep_default_na=False should help you:

df = pd.read_csv(filename, keep_default_na=False)
Answered By: Bendy Latortue

Answer #7:

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)
Answered By: Dinesh Khetarpal

Answer #8:

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 By: Subbu VidyaSekar

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