I don't know of a source for the CSV data directly but if you are OK with a little bit of Python, this can be done with the NASA Exoplanet Archive. Looking at one famous example (HD 189733b), if you do a search for this object on the front page it should bring you to a page of detailed information and links to datasets. Expanding the 'Ancillary Information' section will show the links for the data (this link should be equivalent)
The data files are in IPAC table format but this is easily readable by AstroPy's Table
class. A short example of this is below:
from astropy.table import Table
import matplotlib.pyplot as plt
# Read photometric table
phot_table = Table.read("https://exoplanetarchive.ipac.caltech.edu/data/ExoData/0098/0098505/data/UID_0098505_PLC_025.tbl", format="ipac")
# Subtract off integer part of first JD to make plotting easier
t0 = int(phot_table['HJD'][0])
plt.figure()
plt.errorbar(phot_table['HJD']-t0, phot_table['Relative_Flux'], yerr=phot_table['Relative_Flux_Uncertainty'], color='r', fmt="+", capsize=3)
plt.minorticks_on()
plt.xlabel('HJD-{:.1f} [days]'.format(t0))
plt.ylabel("Relative Flux")
plt.title("HD 189733b Transit Light Curve")
plt.savefig("transit_lc.png")
# Read radial velocity data
rv_table = Table.read("https://exoplanetarchive.ipac.caltech.edu/data/ExoData/0098/0098505/data/UID_0098505_RVC_001.tbl", format="ipac")
t0 = int(rv_table['JD'][0])
plt.clf()
plt.errorbar(rv_table['JD']-t0, rv_table['Radial_Velocity'], yerr=rv_table['Radial_Velocity_Uncertainty'], color='r', fmt='+')
# Zoom in on one of the nights where Rossiter_McLaughlin effect was being measured
plt.xlim(395.45, 395.70)
plt.minorticks_on()
plt.xlabel('JD-{:.1f} [days]'.format(t0))
plt.ylabel('Radial Velocity [m/s]')
plt.title("HD 189733b Radial Velocity Curve")
plt.savefig("transit_rv.png")
# Example export to CSV format
rv_table.write("transit_RV.csv", format='csv')

The top part of the exported CSV file looks like:
JD,Radial_Velocity,Radial_Velocity_Uncertainty
2453946.612661,-2167.93,0.89
2453946.620219,-2155.63,0.85
2453946.627291,-2142.88,0.78
if this is easier for further analysis.