Here is a simple example that shows how to perform the operations requested with pandas.
One uses data binning to group samples and resample data.
import pandas as pd
# Creation of the dataframe
df = pd.DataFrame({\
'Time(s)':[0 ,0 ,0 ,1 ,2],\
'Pressure':[10, 9.9, 10.1, 10, 11],\
'Humidity':[5 ,5.1 ,5 ,4.9 ,6]})
# Select time increment
delta_t = 1
timeCol = 'Time(s)'
# Creation of the time sampling
v = xrange(df[timeCol].min()-delta_t,df[timeCol].max()+delta_t,delta_t)
# Pandas magic instructions with cut and groupby
df_binned = df.groupby(pd.cut(df[timeCol],v))
# Display the first element
dfFirst = df_binned.head(1)
# Evaluate the mean of each group
dfMean = df_binned.mean()
# Evaluate the median of each group
dfMedian = df_binned.median()
# Find the max of each group
dfMax = df_binned.max()
# Find the min of each group
dfMin = df_binned.min()
Result will look like this for dfFirst
Humidity Pressure Time(s)
Time(s)
(-1, 0] 0 5.0 10 0
(0, 1] 3 4.9 10 1
(1, 2] 4 6.0 11 2
Result will look like this for dfMean
Humidity Pressure Time(s)
Time(s)
(-1, 0] 5.033333 10 0
(0, 1] 4.900000 10 1
(1, 2] 6.000000 11 2