The following codes demonstrate how to build a dataframe through a csv file, calculating its correlation matrix and output the 10th column of the matrix:
$ cat << EOF > inp.csv
time,x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16
2017-03-23 06:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 06:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 06:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 06:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 07:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 07:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 07:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 07:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 08:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 08:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 08:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 08:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 09:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 09:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39999997600000003,0.0,0.0,0.0,0.0
2017-03-23 09:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 09:45:00,0.0,0.40999999600000003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39999997600000003,0.0,0.43999999799999995,0.0,0.0,0.0,0.0
2017-03-23 10:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39999997600000003,0.0,0.43999999799999995,0.0,0.0,0.0,0.0
2017-03-23 10:15:00,0.0,0.40999999600000003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.43999999799999995,0.0,0.0,0.0,0.0
2017-03-23 10:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39999997600000003,0.0,0.419999987,0.0,0.0,0.0,0.0
2017-03-23 10:45:00,0.44999998799999996,0.459999979,0.0,0.39999997600000003,0.0,0.0,0.0,0.40999999600000003,0.0,0.469999999,0.0,0.50999999,0.0,0.40999999600000003,0.0,0.0
2017-03-23 11:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 11:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39999997600000003,0.0,0.0,0.0,0.0
2017-03-23 11:30:00,0.0,0.40999999600000003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.459999979,0.0,0.0,0.0,0.0
2017-03-23 11:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.0,0.0,0.0
2017-03-23 12:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.0,0.0,0.0
2017-03-23 12:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 12:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.0,0.0,0.0
2017-03-23 12:45:00,0.539999962,0.550000012,0.48999998,0.50999999,0.42999997700000003,0.459999979,0.469999999,0.5,0.469999999,0.579999983,0.5,0.599999964,0.44999998799999996,0.519999981,0.459999979,0.0
2017-03-23 13:00:00,0.5,0.5299999710000001,0.44999998799999996,0.469999999,0.0,0.40999999600000003,0.44999998799999996,0.47999998899999996,0.44999998799999996,0.539999962,0.459999979,0.5699999929999999,0.40999999600000003,0.469999999,0.419999987,0.0
2017-03-23 13:15:00,0.469999999,0.50999999,0.42999997700000003,0.44999998799999996,0.0,0.0,0.419999987,0.459999979,0.40999999600000003,0.50999999,0.43999999799999995,0.5299999710000001,0.40999999600000003,0.44999998799999996,0.0,0.0
2017-03-23 13:30:00,0.579999983,0.620000005,0.539999962,0.560000002,0.5,0.47999998899999996,0.560000002,0.5699999929999999,0.519999981,0.7199999690000001,0.589999974,0.6599999670000001,0.519999981,0.560000002,0.5299999710000001,0.0
2017-03-23 13:45:00,0.649999976,0.689999998,0.599999964,0.649999976,0.560000002,0.539999962,0.629999995,0.610000014,0.539999962,0.75999999,0.6599999670000001,0.7099999790000001,0.550000012,0.599999964,0.5699999929999999,0.0
2017-03-23 14:00:00,0.669999957,0.7199999690000001,0.599999964,0.649999976,0.539999962,0.5699999929999999,0.649999976,0.629999995,0.560000002,0.74000001,0.6599999670000001,0.729999959,0.589999974,0.629999995,0.5699999929999999,0.0
2017-03-23 14:15:00,0.729999959,0.75999999,0.689999998,0.7199999690000001,0.629999995,0.610000014,0.7199999690000001,0.699999988,0.599999964,0.829999983,0.7199999690000001,0.7999999520000001,0.639999986,0.689999998,0.639999986,0.0
2017-03-23 14:30:00,0.560000002,0.620000005,0.539999962,0.579999983,0.47999998899999996,0.47999998899999996,0.539999962,0.550000012,0.469999999,0.649999976,0.5699999929999999,0.639999986,0.519999981,0.560000002,0.5299999710000001,0.0
2017-03-23 14:45:00,0.0,0.40999999600000003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.43999999799999995,0.0,0.0,0.0,0.0
2017-03-23 15:00:00,0.40999999600000003,0.459999979,0.0,0.39999997600000003,0.0,0.0,0.0,0.0,0.0,0.469999999,0.0,0.47999998899999996,0.0,0.40999999600000003,0.0,0.0
2017-03-23 15:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.43999999799999995,0.0,0.0,0.0,0.0
2017-03-23 15:30:00,0.519999981,0.5299999710000001,0.44999998799999996,0.48999998,0.0,0.42999997700000003,0.48999998,0.47999998899999996,0.40999999600000003,0.599999964,0.47999998899999996,0.5699999929999999,0.42999997700000003,0.469999999,0.43999999799999995,0.0
2017-03-23 15:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 16:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.419999987,0.0,0.0,0.0,0.0
2017-03-23 16:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.39999997600000003,0.0,0.0,0.0,0.0
2017-03-23 16:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 16:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 17:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 17:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 17:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 17:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 18:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 18:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 18:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 18:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 19:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 19:15:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 19:30:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
2017-03-23 19:45:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
EOF
$ cat << EOF > corr.py
import pandas as pd
df = pd.read_csv('inp.csv')
df = df.set_index('time')
corr = df.corr() # corr is a dataframe
print(corr['x10']) # get column by name (label)
print(corr.loc['x10']) # same as above
print(corr.iloc[9]) # same as above but by number, 0 based index
print(corr.iloc[2:3, 7:10]) # get subgroup of a dataframe, upper bound excluded
print(corr.iloc[2, 7]) # get value by index number
print(corr.iloc[2][7]) # same as above
EOF
$ python corr.py
$ cat << EOF > corr.R
inp <- read.csv2('inp.csv', sep = ',', row.names = 'time', dec = '.')
corr <- cor(inp) # corr is a matrix
print(corr[, "x10"]) # get column by name (label)
print(corr[, 10]) # same as above but by index number, 1 based index,
print(corr[3:4, 8:11]) # get subgroup by index number, upper bound included
print(corr[3, 8]) # get value by index number
EOF
$ Rscript corr.R
It's easy to see for dataframe and matrix manipulation, syntax of R is much more concise and consistent than that of Python.