otp.Source.fillna#

Source.fillna(value=None, columns=None, inplace=False)#

Replace NaN values in floating point type fields with the value.

Parameters

  • value (float, Operation) – The value to replace NaN. If not specified then the value from the previous tick will be used.

  • columns (list) – List of strings with column names or Column objects. Only the values in specified columns will be replaced. By default the values in all floating point type fields will be replaced.

  • inplace (bool) – The flag controls whether operation should be applied inplace or not. If inplace=True, then it returns nothing. Otherwise method returns a new modified object.

  • self (Source) –

Return type

Source or None

Examples

By default, the value of the previous tick is used as a value to replace NaN (for the first tick the previous value do not exist, so it will still be NaN):

>>> data = otp.Ticks({'A': [0, 1, 2, 3], 'B': [otp.nan, 2.2, otp.nan, 3.3]})
>>> data = data.fillna()
>>> otp.run(data)
                     Time  A    B
0 2003-12-01 00:00:00.000  0  NaN
1 2003-12-01 00:00:00.001  1  2.2
2 2003-12-01 00:00:00.002  2  2.2
3 2003-12-01 00:00:00.003  3  3.3

The value can also be a constant:

>>> data = otp.Ticks({'A': [0, 1, 2, 3], 'B': [otp.nan, 2.2, otp.nan, 3.3]})
>>> data = data.fillna(777)
>>> otp.run(data)
                     Time  A     B
0 2003-12-01 00:00:00.000  0  777.0
1 2003-12-01 00:00:00.001  1    2.2
2 2003-12-01 00:00:00.002  2  777.0
3 2003-12-01 00:00:00.003  3    3.3

Operation objects can also be used as a value:

>>> data = otp.Ticks({'A': [0, 1, 2, 3], 'B': [otp.nan, 2.2, otp.nan, 3.3]})
>>> data = data.fillna(data['A'])
>>> otp.run(data)
                     Time  A     B
0 2003-12-01 00:00:00.000  0    0.0
1 2003-12-01 00:00:00.001  1    2.2
2 2003-12-01 00:00:00.002  2    2.0
3 2003-12-01 00:00:00.003  3    3.3

Parameter columns can be used to specify the columns where values will be replaced:

data = otp.Ticks({'A': [0, 1, 2, 3], 'B': [otp.nan, 2.2, otp.nan, 3.3], 'C': [otp.nan, 2.2, otp.nan, 3.3]})
data = data.fillna(columns=['B'])
df = otp.run(data)
print(df)

                     Time  A    B    C
0 2003-12-01 00:00:00.000  0  NaN  NaN
1 2003-12-01 00:00:00.001  1  2.2  2.2
2 2003-12-01 00:00:00.002  2  2.2  NaN
3 2003-12-01 00:00:00.003  3  3.3  3.3

See also

onetick.py.Operation.fillna()