Extrapolate Pandas DataFrame

Extrapolating a DataFrame with a DatetimeIndex index

This can be done with two steps:

1. Extend the DatetimeIndex
2. Extrapolate the data

Extend the Index

Overwrite df with a new DataFrame where the data is resampled onto a new extended index based on original index's start, period and frequency. This allows the original df to come from anywhere, as in the csv example case. With this the columns get conveniently filled with NaNs!

# Fake DataFrame for example (could come from anywhere)X1 = range(10)X2 = map(lambda x: x**2, X1)df = pd.DataFrame({'x1': X1, 'x2': X2},  index=pd.date_range('20130101',periods=10,freq='M'))# Number of months to extendextend = 5# Extrapolate the index first based on original indexdf = pd.DataFrame(    data=df,    index=pd.date_range(        start=df.index[0],        periods=len(df.index) + extend,        freq=df.index.freq    ))# Displayprint df

    x1  x22013-01-31   0   02013-02-28   1   12013-03-31   2   42013-04-30   3   92013-05-31   4  162013-06-30   5  252013-07-31   6  362013-08-31   7  492013-09-30   8  642013-10-31   9  812013-11-30 NaN NaN2013-12-31 NaN NaN2014-01-31 NaN NaN2014-02-28 NaN NaN2014-03-31 NaN NaN

Extrapolate the data

Most extrapolators will require the inputs to be numeric instead of dates. This can be done with

# Temporarily remove dates and make index numericdi = df.indexdf = df.reset_index().drop('index', 1)

See this answer for how to extrapolate the values of each column of a DataFrame with a 3^rd order polynomial.

Snippet from answer

# Curve fit each columnfor col in fit_df.columns:    # Get x & y    x = fit_df.index.astype(float).values    y = fit_df[col].values    # Curve fit column and get curve parameters    params = curve_fit(func, x, y, guess)    # Store optimized parameters    col_params[col] = params[0]# Extrapolate each columnfor col in df.columns:    # Get the index values for NaNs in the column    x = df[pd.isnull(df[col])].index.astype(float).values    # Extrapolate those points with the fitted function    df[col][x] = func(x, *col_params[col])

Once the columns are extrapolated, put the dates back

# Put date index backdf.index = di# Displayprint df

x1   x22013-01-31   0    02013-02-28   1    12013-03-31   2    42013-04-30   3    92013-05-31   4   162013-06-30   5   252013-07-31   6   362013-08-31   7   492013-09-30   8   642013-10-31   9   812013-11-30  10  1002013-12-31  11  1212014-01-31  12  1442014-02-28  13  1692014-03-31  14  196