PYTHON

replace nan in pandas

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

replace "-" for nan in dataframe

df.replace(np.nan,0)

pandas replace empty string with nan

df = df.replace(r'^s*$', np.NaN, regex=True)

pandas replace nan

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

python pandas replace nan with null

df.fillna('', inplace=True)

pandas replce none with nan

df = df.fillna(value=np.nan)

replace nan with 0 pandas

DataFrame.fillna()

pandas replace nan with none

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

pandas replace empty string with nan

df = pd.DataFrame([
    [-0.532681, 'foo', 0],
    [1.490752, 'bar', 1],
    [-1.387326, 'foo', 2],
    [0.814772, 'baz', ' '],     
    [-0.222552, '   ', 4],
    [-1.176781,  'qux', '  '],         
], columns='A B C'.split(), index=pd.date_range('2000-01-01','2000-01-06'))

# replace field that's entirely space (or empty) with NaN
print(df.replace(r'^s*$', np.nan, regex=True))

# output
#                    A    B   C
# 2000-01-01 -0.532681  foo   0
# 2000-01-02  1.490752  bar   1
# 2000-01-03 -1.387326  foo   2
# 2000-01-04  0.814772  baz NaN
# 2000-01-05 -0.222552  NaN   4
# 2000-01-06 -1.176781  qux NaN

replace nan in pandas column with mode and printing it

def exercise4(df):
    df1 = df.select_dtypes(np.number)
    df2 = df.select_dtypes(exclude = 'float')
    mode = df2.mode()
    df3 = df1.fillna(df.mean())
    df4 = df2.fillna(mode.iloc[0,:])
    new_df = [df3,df4]
    df5 = pd.concat(new_df,axis=1)
    new_cols = list(df.columns)
    df6 = df5[new_cols]
    return df6

replace nan with mode string pandas

#nan replace mode in string 
df['Brand'].fillna(df['Brand'].mode()[0], inplace=True)