Edward Lance Lorilla

import pandas as pd

df = pd.read_csv('itunes_data.csv')

df['Minutes'] = df['Milliseconds'] / (1000 * 60)

df['MB'] = df['Bytes'] / 1000000

df.drop(['Milliseconds', 'Bytes'], axis=1, inplace=True)

import matplotlib.pyplot as plt

df['Minutes'].plot.box()

plt.show()

df['Minutes'].plot.box()

import seaborn as sns

_ = sns.boxenplot(y=df['Minutes'])

# plot multiple columns at once

sns.boxenplot(data=df[['Minutes', 'MB']])

# save figure for book

f = plt.figure(figsize=(5.5, 5.5)) # this changes the size of the image -- more on this is chapter 5

f.patch.set_facecolor('w') # sets background color behind axis labels

sns.boxenplot(y=df['Minutes'])

plt.tight_layout() # auto-adjust margins

plt.savefig('B17030_05_02.png', dpi=300)

sns.boxenplot(y=df['Minutes'])

plt.yscale('log')

#@title Default title text

# save figure for book

f = plt.figure(figsize=(5.5, 5.5)) # this changes the size of the image -- more on this is chapter 5

f.patch.set_facecolor('w') # sets background color behind axis labels

sns.boxenplot(y=df['Minutes'])

plt.yscale('log')

plt.tight_layout() # auto-adjust margins

plt.savefig('B17030_05_03.png', dpi=300)

df['Minutes'].plot.box()

plt.yscale('log')

df['Minutes'].plot.box()

plt.yscale('log')

# another way to use a log scale

df['Minutes'].plot.box(logy=True)

df['Minutes'].describe()

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