Convert Nan to Empty String in PandasUse df. replace(np. nan,'',regex=True) method to replace all NaN values to an empty string in the Pandas DataFrame column.
In order to check null values in Pandas Dataframe, we use notnull() function this function return dataframe of Boolean values which are False for NaN values.
This is what Pandas documentation gives: na_values : scalar, str, list-like, or dict, optional Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.
read_csv disregard any empty line and taking the first non-empty line as the header.
I was still confused after reading the other answers and comments. But the answer now seems simpler, so here you go.
Since Pandas version 0.9 (from 2012), you can read your csv with empty cells interpreted as empty strings by simply setting keep_default_na=False
:
pd.read_csv('test.csv', keep_default_na=False)
This issue is more clearly explained in
That was fixed on on Aug 19, 2012 for Pandas version 0.9 in
I added a ticket to add an option of some sort here:
https://github.com/pydata/pandas/issues/1450
In the meantime, result.fillna('')
should do what you want
EDIT: in the development version (to be 0.8.0 final) if you specify an empty list of na_values
, empty strings will stay empty strings in the result
We have a simple argument in Pandas read_csv()
for this:
Use:
df = pd.read_csv('test.csv', na_filter= False)
What pandas
defines by default as missing value while read_csv()
can be found here.
import pandas
default_missing = pandas._libs.parsers.STR_NA_VALUES
print(default_missing)
The output
{'', '<NA>', 'nan', '1.#QNAN', 'NA', 'null', 'n/a', '-nan', '1.#IND', '#N/A N/A', 'N/A', 'NULL', 'NaN', '-1.#IND', '-1.#QNAN', '#NA', '#N/A', '-NaN'}
With that you can do an opt-out.
import pandas
default_missing = pandas._libs.parsers.STR_NA_VALUES
default_missing = default_missing.remove('')
default_missing = default_missing.remove('na')
with open('test.csv', 'r') as csv_file:
pandas.read_csv(csv_file, na_values=default_missing)
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With