otp.Source.dropna#

Source.dropna(how='any', subset=None, inplace=False)#

Drops ticks that contain NaN values according to the policy in the how parameter

Parameters

  • how ("any" or "all") –

    any - filters out ticks if at least one field has NaN value

    all - filters out ticks if all fields have NaN values.

  • subset (list of str) – list of columns to check for NaN values. If None then all columns are checked.

  • inplace (bool) – the flag controls whether operation should be applied inplace.

Return type

Source or None

Examples

Drop ticks where at least one field has nan value.

>>> data = otp.Ticks([[     'X',     'Y'],
...                   [     0.0,     1.0],
...                   [ otp.nan,     2.0],
...                   [     4.0, otp.nan],
...                   [ otp.nan, otp.nan],
...                   [     6.0,    7.0]])
>>> data = data.dropna()
>>> otp.run(data)[['X', 'Y']]
    X   Y
0 0.0 1.0
1 6.0 7.0

Drop ticks where all fields have nan values.

>>> data = otp.Ticks([[     'X',     'Y'],
...                   [     0.0,     1.0],
...                   [ otp.nan,     2.0],
...                   [     4.0, otp.nan],
...                   [ otp.nan, otp.nan],
...                   [     6.0,    7.0]])
>>> data = data.dropna(how='all')
>>> otp.run(data)[['X', 'Y']]
    X   Y
0 0.0 1.0
1 NaN 2.0
2 4.0 NaN
3 6.0 7.0

Drop ticks where all fields in subset of columns have nan values.

>>> data = otp.Ticks([[     'X',     'Y',    'Z'],
...                   [     0.0,     1.0, otp.nan],
...                   [ otp.nan,     2.0, otp.nan],
...                   [     4.0, otp.nan, otp.nan],
...                   [ otp.nan, otp.nan, otp.nan],
...                   [     6.0,     7.0, otp.nan]])
>>> data = data.dropna(how='all', subset=['X', 'Y'])
>>> otp.run(data)[['X', 'Y', 'Z']]
    X   Y   Z
0 0.0 1.0 NaN
1 NaN 2.0 NaN
2 4.0 NaN NaN
3 6.0 7.0 NaN