PYTHON

pandas columns to int64 with nan

# single column
df['column_name'].apply(pd.to_numeric).astype('Int64')

# range of cols
df.loc[:, 'col_n':'col_m'] = df.loc[:, 'col_n':'col_m'].apply(pd.to_numeric).astype('Int64')

'''
From Pandas v0.24, introduces Nullable Integer Data Types 
which allows integers to coexist with NaNs.
'''

replace nan in pandas

df['DataFrame Column'] = df['DataFrame Column'].fillna(0)

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)

how to replace nan values with 0 in pandas

df.fillna(0)

pandas nan to None

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

python dataframe replace nan with 0

In [7]: df
Out[7]: 
          0         1
0       NaN       NaN
1 -0.494375  0.570994
2       NaN       NaN
3  1.876360 -0.229738
4       NaN       NaN

In [8]: df.fillna(0)
Out[8]: 
          0         1
0  0.000000  0.000000
1 -0.494375  0.570994
2  0.000000  0.000000
3  1.876360 -0.229738
4  0.000000  0.000000

replace nan with 0 pandas

DataFrame.fillna()

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)