W2 Income

import numpy as np # module for creating and manipulating arrays import matplotlib.pyplot as plt # plotting import pandas as pd # dataframes

url = 'https://raw.githubusercontent.com/data-8/materials-sp18/master/lec/san_francisco_2015.csv' df0 = pd.read_csv(url)

print("Employee compensations in the City of San Francisco in the"\ "year 2015\nShape: {} \nColumns: {}".format(df0.shape, df0.columns)) df0.head()

df0.sort_values(['Total Compensation'], ascending=False)[:3]

df0.sort_values(['Total Compensation'], ascending=True)[:3]

df = df0.loc[df0['Salaries'] > 10000]

TC = df['Total Compensation'] / 1000 # scale the Total compensation TC

# you can use NumPy to compute the statistics TC_mean = TC.sum() / TC.size a = TC.sum() - TC_mean b = a**2 c = b / TC.size TC_std = np.sqrt(c) TC_min = TC.min() TC_max = TC.max() # Report the mean 'Total Compensation' using the standard deviation and uncertainties print("The mean Total Compensation is {:.0f} +- {:.0f} (mean +- SD) "\ "thousand dollars annually, \nwith a range "\ "({:.2f}, {:.2f})".format(TC_mean, TC_std, TC_min, TC_max))

plt.hist(TC, range=(0, 700), bins=20, density=True) plt.xlabel("Total Compensation (k$)")

TC_median = np.percentile(TC, 50, axis=-1) print("The median is {:.1f} thousand dollars annually".format(TC_median))

print("10, 50 and 90 percentiles {}".format( np.percentile(TC, [10, 50, 90])))

# draw sample with 500 elements n = 500 our_sample = np.random.choice(TC, size=n, replace=None, p=None) # plot an histogram plt.hist(our_sample, range=(0,700)) # Calcualte the median of the sample sample_median = np.percentile(TC, 50, axis=-1) print("Sample median {:.2f}".format(sample_median))

# draw a sample with replacement from the original sample our_sample resample_1 = np.random.choice(our_sample, n, replace=True) # plot histogram of the drawed sample # plt. ... your code here...

def bootstrap_median(original_sample, replications): """Returns an array of bootstrapped sample medians Parameters ---------- original_sample: array containing the original sample replications: number of bootstrap samples """ n = len(original_sample) medians = np.zeros(replications) for i in np.arange(replications): bootstrap_sample = np.random.choice(original_sample, n, replace=True) resampled_median = np.median(bootstrap_sample) medians[i] = resampled_median return medians

bstrap_medians = bootstrap_median(our_sample, 5000)

# calculate the right percentile 2.5 and left percentile 97.5 of bstrap_medians leftp = np.percentile(TC, 2.5, axis=-1) rightp = np.percentile(TC, 97.5, axis=-1) print("The sample median {:.1f}\n95% confidence interval of the median ({:.1f}, {:.1f})".format(sample_median, leftp, rightp)) # plot histogram of the bstrap_medians with the 95% confidence intervals and the sample median plt.hist(bstrap_medians, density=True) # indicate the middle 95% with an horizontal line plt.hlines(0.0, leftp, rightp, 'y', lw=8) # 5 and 95 percentiles # indicate the sample median with a red dot plt.plot(sample_median, 0.002, 'ro', ms=10) # median plt.xlabel('Bootstrapped medians')