numpy - set new index for pandas DataFrame (interpolating?)

Question

I have a DataFrame where the index is NOT time. I need to rescale all of the values from an old index which is not equispaced, to a new index which has different limits and is equispaced.

The first and last values in the columns should stay as they are (although they will have the new, stretched index values assigned to them).

Example code is:

import numpy as np
import pandas as pd
%matplotlib inline

index = np.asarray((2, 2.5, 3, 6, 7, 12, 15, 18, 20, 27))
x = np.sin(index / 10)

df = pd.DataFrame(x, index=index)
df.plot();

newindex = np.linspace(0, 29, 100)

How do I create a DataFrame where the index is newindex and the new x values are interpolated from the old x values?

The first new x value should be the same as the first old x value. Ditto for the last x value. That is, there should not be NaNs at the beginning and copies of the last old x repeated at the end.

The others should be interpolated to fit the new equispaced index.

I tried df.interpolate() but couldn't work out how to interpolate against the newindex.

Thanks in advance for any help.

Answer 1

This is works well:

import numpy as np
import pandas as pd

def interp(df, new_index):
    """Return a new DataFrame with all columns values interpolated
    to the new_index values."""
    df_out = pd.DataFrame(index=new_index)
    df_out.index.name = df.index.name

    for colname, col in df.iteritems():
        df_out[colname] = np.interp(new_index, df.index, col)

    return df_out