Enhancement Request: control extrapolation on .interpolate

[WBare]

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

dfMain = pd.DataFrame({
    'a': [0, 1, np.NAN, 3, 4],
    'b': [np.NaN, np.NaN, np.NaN, 3, 4],
    'c': [0 , 1, 2, 3, np.NaN]})

for col in dfMain:
    start = dfMain[col].first_valid_index()
    end = dfMain[col].last_valid_index()
    dfMain.loc[start:end, col] = dfMain.loc[start:end, col].interpolate()

print(dfMain)

Problem description

It would be very nice to have a limit_direction='inside' that would make interpolate only fill values that are surrounded (both in front and behind) with valid values.

This would allow an interpolate to only fill missing values in a series and not extend the series beyond its original limits. The key here is that it is sometimes important to maintain the original range of a series, but still fill in the gaps.

The example shows a simple DataFrame with an 'inside' interpolation.

Expected Output

     a    b    c
0  0.0  NaN  0.0
1  1.0  NaN  1.0
2  2.0  NaN  2.0
3  3.0  3.0  3.0
4  4.0  4.0  NaN

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.0.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-75-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.4.1
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: 0.2.1
None

[TomAugspurger]

So, this kind of already works when you use the scipy methods, since that's the default for scipy when you extraploate

In [31]: dfMain.interpolate(method='slinear')
Out[31]:
     a    b    c
0  0.0  NaN  0.0
1  1.0  NaN  1.0
2  2.0  NaN  2.0
3  3.0  3.0  3.0
4  4.0  4.0  NaN

This is an implementation detail that the user shouldn't need to worry about... But I'm not sure that we can make this consistent across methods in a backwards-compatible way.

[WBare]

Thank you very much, @TomAugspurger ! I did not know that, and that solves the problem for me, but I agree completely that it would be nice to somehow make this more visible to the user.

I don't know, but I'm guessing method='slinear' will not have an option to respect the limit= on number of NaNs to fill in, otherwise the code could just intercept something like limit_direction='inside' and save a bunch of work.

Thanks again!!

[WBare]

This needs to run through the existing interpolate function so that it will respect the limit=n parameter correctly.

[TomAugspurger]

Continuing from #16307 (comment)

Hmm I hadn't considered the interaction of limit and extrapolation... I think that's covered by our current limit handling though.

My initial (very rough) thoughts are something like

def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
                limit_direction='forward', downcast=None, extrapolate=None, **kwargs)
    """
    ...
    extrapolate : array-like, two-tuple, "extrapolate", or None, optional

        This is similar to scipy.interpolate.interp1d's `fill_value` keyword,
        with special handling for pandas interpolation methods. By default,
        pandas interpolation methods (...) will extrapolate forward only by
        repeating the last valid observation, while scipy methods will not
        interpolate (following the default for scipy). To disable extrapolation
        for pandas methods, use `extrapolate=np.nan`.

The difference between pandas and scipy methods is unfortunate, but I don't think it's worth deprecating one or the other (willing to change my mind on this).

I don't think an interpolate=True argument is necessary. @WBare d

[WBare]

I don't think an interpolate=True argument is necessary.

@TomAugspurger I agree in that I personally do not have a case where I would only want to extrapolate only, BUT, I'm concerned we are arbitrarily eliminating a use case that should get completed while we are in the code.

More generally, as a complete set of options, if we have the ability to interpolate only, why not the ability to extrapolate only? I can imagine a user that would like to extend a series, but not disturb existing NaNs inside the series.

I have not needed that myself so I'm basing this more on a logically complete set options than personal experience.

With that said, if we do choose to have an option for both, I'm changing my mind on the parameter. I like your initial idea better (something like limit_type='interpolate' | 'extrapolate') as opposed to extrapolate=False because the logical flags allow for a combination that would never make sense (i.e. both interpolate=False and extrapolate=False). The single parameter more clearly conveys the idea of limiting to one or the other.

[TomAugspurger]

BUT, I'm concerned we are arbitrarily eliminating a use case that should get completed while we are in the code.

Yeah, that makes sense. You'll just have to balance ease of implementation with not breaking existing code :)

[WBare]

OK, cool. Any ideas on the actual parameter?

Is everyone cool with

limit_type= ('interpolate' | 'extrapolate' | None=default to both which is current_behavior)

This seems consistent with existing parameters limit=n and limit_direction

[naifrec]

hey guys, if you let me jump in. I have the feeling the limit kwarg does not behave as you would expect it to when working with time series. To cite @rhkarls in the issue #1892 :

Say limit=2, if there is a NaN gap of 2 it would be completely filled with interpolated values. If there is a NaN gap of 4 nothing is filled, which is different from the fillna limit where the two first entries would be filled when using forward filling. This is very applicable for time series where it is often valid to interpolate between small gaps, while larger gaps should not be filled.

So lemme write an example:

import pandas as pd


df = pd.DataFrame(
    index=pd.date_range(
        start='02-01-2017 06:00:00',
        end='02-07-2017 06:00:00'),
    data={'A': range(7)})
df = df.drop(pd.to_datetime('2017-02-02 06:00:00'), axis=0)

df.head()

                     A
2017-02-01 06:00:00  0
2017-02-03 06:00:00  2
2017-02-04 06:00:00  3
2017-02-05 06:00:00  4
2017-02-06 06:00:00  5

Now what I want is to resample and interpolate the time series every 12 hours, but only for the consecutive days, so as not to make too big assumptions on the behavior of the time series for larger time deltas. That is not immediately possible currently, because of how limit works. See below, where putting limit of 2 (i.e. limit of a day) means that if two consecutive values are NaN, please do not fill in:

df.resample(rule='12H',base=6).interpolate('time', limit=2)

                       A
2017-02-01 06:00:00  0.0
2017-02-01 18:00:00  0.5  # I would expect this to be NaN
2017-02-02 06:00:00  1.0  # I would expect this to be NaN
2017-02-02 18:00:00  NaN
2017-02-03 06:00:00  2.0
2017-02-03 18:00:00  2.5
2017-02-04 06:00:00  3.0
2017-02-04 18:00:00  3.5
2017-02-05 06:00:00  4.0
2017-02-05 18:00:00  4.5
2017-02-06 06:00:00  5.0
2017-02-06 18:00:00  5.5
2017-02-07 06:00:00  6.0
In [ ]:

To achieve what I want now, I have to use these functions I made:

def interpolate_consecutive(df, frequency):
    """
    Only interpolates value at the frequency asked if the
    values where separated by a day.
    
    Paramteres
    ----------
    df : pd.DataFrame
        Dataframe with Time series index
    frequency : basestring
        Frequency to use to resample then interpolate.
        Only expects 'H' or 'T' based rules, but that's
        because I only need to support these in my case.
    
    Returns
    -------
    df : pd.DataFrame
        Resampled and interpolated dataframe.

    """
    base = 6 if 'H' in frequency else 0
    start_indices, end_indices = get_non_consecutive(
        df, pd.Timedelta(days=1))
    df = df.resample(rule=frequency, base=base).interpolate('time')

    indices_to_drop = []
    for start_date, end_date in zip(start_indices, end_indices):
        indices_to_drop.extend(list(df.index[
            np.logical_and(start_date < df.index,
                           df.index < end_date)]))
    df.drop(indices_to_drop, axis=0, inplace=True)
    return df


def get_non_consecutive(df, timedelta):
    """
    Get the tuple start_indices, end_indices of all
    non consecutive period in the dataframe index.
    Two timestamps separated with more than timedelta
    are considered non consecutive.
    
    Parameters
    ----------
    df : pandas.DataFrame
        Dataframe with Time series index
    timedelta : pd.Timedelta
        Time delta.
    
    Returns
    -------
    start_dates : array-like
        List of start dates of non consecutive periods
    end_dates : array-like
        List of end dates of non consecutive periods

    """
    where = np.where(
        df.index[1:] - df.index[:-1] > timedelta)[0]
    return df.index[where], df.index[where + 1]

using these function I now get my desired output:

interpolate_consecutive(df, '12H')

                       A
2017-02-01 06:00:00  0.0
2017-02-03 06:00:00  2.0
2017-02-03 18:00:00  2.5
2017-02-04 06:00:00  3.0
2017-02-04 18:00:00  3.5
2017-02-05 06:00:00  4.0
2017-02-05 18:00:00  4.5
2017-02-06 06:00:00  5.0
2017-02-06 18:00:00  5.5
2017-02-07 06:00:00  6.0

tldr, limit should actually not always do forward filling, but check the length of the NaN gap and not fill in anything if this gap is longer than the limit.

Thank you for taking the time to read this, hope I made myself clear.