PYTHON

python pandas convert nan to 0

pandas.DataFrame.fillna(0)

pandas replace nan

data["Gender"].fillna("No Gender", inplace = True) 

python pandas replace nan with null

df.fillna('', inplace=True)

pandas nan to None

df = df.astype("object").where(pd.notnull(df), None)

pandas replace nan with none

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

represent NaN with pandas in python

import pandas as pd

if pd.isnull(float("Nan")):
  print("Null Value.")

turn False to nan pandas

In [1]: df = DataFrame([[True, True, False],[False, False, True]]).T

In [2]: df
Out[2]:
       0      1
0   True  False
1   True  False
2  False   True

In [3]: df.applymap(lambda x: 1 if x else np.nan)
Out[3]:
    0   1
0   1 NaN
1   1 NaN
2 NaN   1

pandas nan to none

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

pandas using eval converter excluding nans

df.fillna('()').applymap(ast.literal_eval)

pandas using eval converter excluding nans

from ast import literal_eval
from io import StringIO

# replicate csv file
x = StringIO("""A,B
,"('t1', 't2')"
"('t3', 't4')",""")

def literal_converter(val):
    # replace first val with '' or some other null identifier if required
    return val if val == '' else literal_eval(val)

df = pd.read_csv(x, delimiter=',', converters=dict.fromkeys('AB', literal_converter))

print(df)

          A         B
0            (t1, t2)
1  (t3, t4)          

Convert nan into None in df

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