albertyumol

import numpy as np
def standard_deviation(x):
    x_bar = np.mean(x)
    N = len(x)
    deviation_mean = [x_i - x_bar for x_i in x]
    return  np.sqrt(np.dot(deviation_mean, deviation_mean) / N)

albertyumol

def quantile(x, p):
  p_index = int(p*len(x))
  return sorted(x)[p_index]

def interquartile_range(x):
  return quantile(x, 0.75) - quantile(x, 0.25)

albertyumol

import numpy as np
def variance(x):
    x_bar = np.mean(x)
    N = len(x)
    deviation_mean = [x_i - x_bar for x_i in x]
    return  np.dot(deviation_mean, deviation_mean) / N

albertyumol

def range_(x):
  return max(x) - min(x)

albertyumol

def quantile(x, p):
  p_index = int(p*len(x))
  return sorted(x)[p_index]

albertyumol

from collections import Counter
def mode(x):
    counts = Counter(x)
    max_count = max(counts.values())
    return [i for i, j in counts.items() if j == max_count]

albertyumol

import random
import pandas as pd
import matplotlib.pyplot as plt

random.seed( 30 ) #for replicability

n = 100
y = []
for i in range(n):
    y += [random.randint(25, 50)]

albertyumol

def median(x):
  sorted_ = sorted(x)
  N = len(x)
  middle_value = N // 2
  if n % 2 != 0:        #CASE 1: if N is odd
    M = sorted_[middle_value]
  else:                 #CASE 2: if N is odd
    M = (sorted_[(middle_value) - 1] + sorted_[middle_value]) / 2
  return M

albertyumol

#assuming that x is a list of values and is not empty
#because we are not allowed to divide by 0
def mean(x):
  return sum(x) / len(x)

albertyumol

### Set county location values, drough level values, marker sizes (according to county size), colormap and title 
x, y = m(dr_m.nth(0).longitude.tolist(), dr_m.nth(0).latitude.tolist())
colors = (dr_m.nth(0).code).tolist()
sizes = (dr_m.nth(0).count1*80).tolist()
cmap = plt.cm.plasma#autumn_r
#cmap = plt.cm.hot_r
sm = ScalarMappable(cmap=cmap)
plt.title('Social Movement (Year-Month): '+dr_m.nth(0).event_date.iloc[0].strftime('%Y-%m'))

### Display the scatter plot and its colorbar (0-5)