# Salaries and Experience

salaries_and_tenures = [(83000, 8.7), (88000, 8.1),
                        (48000, 0.7), (76000, 6),
                        (69000, 6.5), (76000, 7.5),
                        (60000, 2.5), (83000, 10),
                        (48000, 1.9), (63000, 4.2)]

print(salaries_and_tenures)

# *************************************************
# Print the table as scatter plot

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

# df = pd.DataFrame(salaries_and_tenures)
# print(df)

# df.plot(kind="scatter", x = 1, y = 0)
# plt.show() 
# *************************************************

from collections import defaultdict

# ++++++++++++++++++++++++++++++++++++++++++++++++++++++
# ***** Store in buckets (histogram)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++

def tenure_bucket(tenure):
    if tenure < 2:
        return "less than two"
    elif tenure < 5:
        return "between two and five"
    else:
        return "more than five"

# keys are tenure buckets, values are lists of salaries for that bucket
salary_by_tenure_bucket = defaultdict(list)

for salary, tenure in salaries_and_tenures:
    bucket = tenure_bucket(tenure)
    salary_by_tenure_bucket[bucket].append(salary)

print(salary_by_tenure_bucket)

average_salary_by_bucket = {
    tenure_bucket : sum(salaries) / len(salaries)
    for tenure_bucket, salaries in salary_by_tenure_bucket.items()
    }

print(average_salary_by_bucket)