Combine two Pandas dataframes, resample on one time column, interpolate

Question

This is my first question on stackoverflow. Go easy on me!

I have two data sets acquired simultaneously by different acquisition systems with different sampling rate

Answer 1

It's not exactly clear to me how you're getting rid of some of the values in y2, but it seems like if there is more than one for a given timepoint, you only want the first one. Also, it seems like your time values should be in the index. I also added column labels. It looks like this:

import pandas as pd

# evenly spaced times
t1 = [0,0.5,1.0,1.5,2.0]
y1 = t1

# unevenly spaced times
t2 = [0,0.34,1.01,1.4,1.6,1.7,2.01]

# round t2 values to the nearest half
new_t2 = [round(num * 2)/2 for num in t2]

# set y2 values
y2 = [3*z for z in new_t2]

# eliminate entries that have the same index value
for x in range(1, len(new_t2), -1):
    if new_t2[x] == new_t2[x-1]:
        new_t2.delete(x)
        y2.delete(x)


ser1 = pd.Series(y1, index=t1)
ser2 = pd.Series(y2, index=new_t2)

df = pd.concat((ser1, ser2), axis=1)
df.columns = ('Y1', 'Y2')

print df

This prints:

      Y1   Y2
0.0  0.0  0.0
0.5  0.5  1.5
1.0  1.0  3.0
1.5  1.5  4.5
1.5  1.5  4.5
1.5  1.5  4.5
2.0  2.0  6.0