Combine Two Pandas Dataframes, Resample On One Time Column, Interpolate

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 rates. One is very

Solution 1:

If you construct a single DataFrame from Series, using time values as index, like this:

>>>t1 = np.array([0, 0.5, 1.0, 1.5, 2.0])>>>y1 = pd.Series(t1, index=t1)>>>t2 = np.array([0, 0.34, 1.01, 1.4, 1.6, 1.7, 2.01])>>>y2 = pd.Series(3*t2, index=t2)>>>df = pd.DataFrame({'y1': y1, 'y2': y2})>>>df
       y1    y2
0.00  0.0  0.00
0.34  NaN  1.02
0.50  0.5   NaN
1.00  1.0   NaN
1.01  NaN  3.03
1.40  NaN  4.20
1.50  1.5   NaN
1.60  NaN  4.80
1.70  NaN  5.10
2.00  2.0   NaN
2.01  NaN  6.03

You can simply interpolate it, and select only the part where y1 is defined:

>>>df.interpolate('index').reindex(y1)
      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
2.0  2.0  6.0

Solution 2:

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