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%matplotlib inline
import numpy as np
import scipy.stats as stats
import pandas as pd
import seaborn as sns
from nose.tools import assert_almost_equal, assert_equal, assert_is_instance, assert_is_not
import matplotlib as mpl
import matplotlib.pyplot as plt
Write a function called "probability_calc" that takes in the number of observed heads from a coin flip experiment and the total number of coin flips, and returns the probability that the coin lands on heads.
def probability_calc(num_heads,n):
"""
Inputs
------
num_heads: an integer, the number of heads observed
n: an integer, the total number of coin flips
Output
------
prob: the probability of the coin landing on heads
"""
### YOUR CODE HERE
my_prob = probability_calc(37, 233)
assert_almost_equal(my_prob, 0.15879828326180256)
Write a function called "bayes_calc" which takes in the probability of event A, and the probability of event B and return $P(A|B)$. Assume that A and B are independent events.
def bayes_calc(prob_A, prob_B):
"""
Inputs
-------
prob_A: a float, the probability of event A
prob_B: a float, the probability of event B
Output
------
prob: the probability of event A given event B
"""
### YOUR CODE HERE
my_prob = bayes_calc(15 / 66, 13 / 68)
assert_almost_equal(my_prob, 0.227272, places=5)
Write a function called "composite_prob" which takes in a numpy array of data, called "data", a value called "cutoff" and calculates the probability that a data point in "data" is greater than "cutoff":
def composite_prob(data,cutoff):
"""
Inputs
-------
data: a numpy array of data
cutoff: a float, the cutoff value
Output
------
prob: the probability of a data point in data being larger than cutoff
"""
### YOUR CODE HERE
my_prob = composite_prob(np.linspace(0,1, 365), .7)
assert_almost_equal(my_prob, 0.301369, places=5)
# Read in dow jones index dataset
df = pd.read_csv('./dow_jones_index.data')
df.head()
In this problem you'll finish writing the plot_poisson function. A dataframe of the dow jones dataset and the column name are passed in. Your task is to:
def plot_poisson(df, col='open', n_pts=100):
'''
df: dataframe containg data from dow jones index
col: column name in df variable
n_pts: number of points
returns axes object
'''
# Define plot layout
fig, ax = plt.subplots(figsize=(10, 5))
### YOUR CODE HERE
return ax
ch = plot_poisson(df,'open')
from helper import pp
print("Your Plot should look similar to this:")
sol = pp(df)
assert_is_instance(ch, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_instance(ch, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_not(len(ch.title.get_text()), 0, msg="Your plot doesn't have a title.")
assert_is_not(ch.xaxis.get_label_text(), '', msg="Change the x-axis label to something more descriptive.")
assert_is_not(ch.yaxis.get_label_text(), '', msg="Change the y-axis label to something more descriptive.")
assert_equal(len(ch.patches), len(sol.patches), msg="Your bar graph doesn't have the correct number patches. Make sure you use the n_pts parameter. ")
for student_patch, solution_patch in zip(ch.patches, sol.patches):
assert_equal(student_patch.get_width(), solution_patch.get_width(), msg='')
assert_equal(student_patch.get_height(), solution_patch.get_height(), msg='')
In this problem you'll finish writing the plot function. A dataframe of the dow jones dataset, string containing pmf, cdf, or sf and the column name are passed in. Your task is to:
def plot(df, dist, col='open', n_pts=100):
'''
df: dataframe containg data from dow jones index
dist: string speicfying function to use. This contains: 'pmf', 'cdf', or 'sf'
col: column name in df variable
returns axes object
'''
# Define plot layout
fig, ax = plt.subplots(figsize=(10, 5))
### YOUR CODE HERE
return ax
ax_pmf = plot(df, 'pmf', col='open')
from helper import p
sol1 = p(df,'pmf', col='open')
assert_is_instance(ax_pmf, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_instance(ax_pmf, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_not(len(ax_pmf.title.get_text()), 0, msg="Your plot doesn't have a title.")
assert_is_not(ax_pmf.xaxis.get_label_text(), '', msg="Change the x-axis label to something more descriptive.")
assert_is_not(ax_pmf.yaxis.get_label_text(), '', msg="Change the y-axis label to something more descriptive.")
assert_equal(len(ax_pmf.patches), len(sol1.patches), msg="Your bar graph doesn't have the correct number patches. Make sure you use the n_pts parameter. ")
for student_patch, solution_patch in zip(ax_pmf.patches, sol1.patches):
assert_equal(student_patch.get_width(), solution_patch.get_width(), msg='')
assert_equal(student_patch.get_height(), solution_patch.get_height(), msg='')
ax_cmf = plot(df, 'cdf', 'close')
print("Your plot using CMF should look like this:")
sol2 = p(df,'cdf', col='close')
assert_is_instance(ax_cmf, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_instance(ax_cmf, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_not(len(ax_cmf.title.get_text()), 0, msg="Your plot doesn't have a title.")
assert_is_not(ax_cmf.xaxis.get_label_text(), '', msg="Change the x-axis label to something more descriptive.")
assert_is_not(ax_cmf.yaxis.get_label_text(), '', msg="Change the y-axis label to something more descriptive.")
assert_equal(len(ax_cmf.patches), len(sol2.patches), msg="Your bar graph doesn't have the correct number patches. Make sure you use the n_pts parameter. ")
for student_patch, solution_patch in zip(ax_cmf.patches, sol2.patches):
assert_equal(student_patch.get_width(), solution_patch.get_width(), msg='')
assert_equal(student_patch.get_height(), solution_patch.get_height(), msg='')
ax_sf = plot(df, 'sf', 'close')
print("Your Plot should look similar to this:")
sol3 = p(df,'sf', col='close')
assert_is_instance(ax_sf, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_instance(ax_sf, mpl.axes.Axes, msg='Return a Axes object.')
assert_is_not(len(ax_sf.title.get_text()), 0, msg="Your plot doesn't have a title.")
assert_is_not(ax_sf.xaxis.get_label_text(), '', msg="Change the x-axis label to something more descriptive.")
assert_is_not(ax_sf.yaxis.get_label_text(), '', msg="Change the y-axis label to something more descriptive.")
assert_equal(len(ax_sf.patches), len(sol3.patches), msg="Your bar graph doesn't have the correct number patches. Make sure you use the n_pts parameter. ")
for student_patch, solution_patch in zip(ax_sf.patches, sol3.patches):
assert_equal(student_patch.get_width(), solution_patch.get_width(), msg='')
assert_equal(student_patch.get_height(), solution_patch.get_height(), msg='')
© 2017: Robert J. Brunner at the University of Illinois.
This notebook is released under the Creative Commons license CC BY-NC-SA 4.0. Any reproduction, adaptation, distribution, dissemination or making available of this notebook for commercial use is not allowed unless authorized in writing by the copyright holder.