In this recipe, we will take a look at the daily mean sea level pressure (in 0.1 hPa) calculated from 24 hourly values. This includes printing descriptive statistics and visualizing the probability distribution. In nature, we often deal with the normal distribution, so the normality test from Chapter 10, Fun with Scikits, will come in handy.
The complete code is in the exploring.py file in this book's code bundle:
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.stats.adnorm import normal_ad
data = np.load('cbk12.npy')
# Multiply to get hPa values
meanp = .1 * data[:,1]
# Filter out 0 values
meanp = meanp[ meanp > 0]
# Get descriptive statistics
print("Max", meanp.max())
print("Min", meanp.min())
mean = meanp.mean()
print("Mean", mean)
print("Median", np.median(meanp))
std = meanp.std()
print("Std dev", std)
# Check for normality
print("Normality", normal_ad(meanp))
#histogram with Gaussian PDF...