В предыдущей статье мы рассмотрели некоторые базовые методы анализа данных, а теперь давайте рассмотрим визуальные методы.
Давайте рассмотрим основные методы
# Loading Libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import trim_mean
# Loading Data
data = pd.read_csv("state.csv")
# Check the type of data
print ("Type : ", type(data), "\n\n")
# Printing Top 10 Records
print ("Head -- \n", data.head(10))
# Printing last 10 Records
print ("\n\n Tail -- \n", data.tail(10))
# Adding a new column with derived data
data['PopulationInMillions'] = data['Population']/1000000
# Changed data
print (data.head(5))
# Rename column heading as it
# has '.' in it which will create
# problems when dealing functions
data.rename(columns ={'Murder.Rate': 'MurderRate'},
inplace = True)
# Lets check the column headings
list(data)
Вывод :
Type : class 'pandas.core.frame.DataFrame'
Head --
State Population Murder.Rate Abbreviation
0 Alabama 4779736 5.7 AL
1 Alaska 710231 5.6 AK
2 Arizona 6392017 4.7 AZ
3 Arkansas 2915918 5.6 AR
4 California 37253956 4.4 CA
5 Colorado 5029196 2.8 CO
6 Connecticut 3574097 2.4 CT
7 Delaware 897934 5.8 DE
8 Florida 18801310 5.8 FL
9 Georgia 9687653 5.7 GA
Tail --
State Population Murder.Rate Abbreviation
40 South Dakota 814180 2.3 SD
41 Tennessee 6346105 5.7 TN
42 Texas 25145561 4.4 TX
43 Utah 2763885 2.3 UT
44 Vermont 625741 1.6 VT
45 Virginia 8001024 4.1 VA
46 Washington 6724540 2.5 WA
47 West Virginia 1852994 4.0 WV
48 Wisconsin 5686986 2.9 WI
49 Wyoming 563626 2.7 WY
State Population Murder.Rate Abbreviation PopulationInMillions
0 Alabama 4779736 5.7 AL 4.779736
1 Alaska 710231 5.6 AK 0.710231
2 Arizona 6392017 4.7 AZ 6.392017
3 Arkansas 2915918 5.6 AR 2.915918
4 California 37253956 4.4 CA 37.253956
['State', 'Population', 'MurderRate', 'Abbreviation']
Визуализация численности населения на миллион
# Plot Population In Millions
fig, ax1 = plt.subplots()
fig.set_size_inches(15, 9)
ax1 = sns.barplot(x ="State", y ="Population",
data = data.sort_values('MurderRate'),
palette ="Set2")
ax1.set(xlabel ='States', ylabel ='Population In Millions')
ax1.set_title('Population in Millions by State', size = 20)
plt.xticks(rotation =-90)
Вывод:
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),
a list of 50 Text xticklabel objects)
Визуализация количества убийств на человека
# Plot Murder Rate per 1, 00, 000
fig, ax2 = plt.subplots()
fig.set_size_inches(15, 9)
ax2 = sns.barplot(
x ="State", y ="MurderRate",
data = data.sort_values('MurderRate', ascending = 1),
palette ="husl")
ax2.set(xlabel ='States', ylabel ='Murder Rate per 100000')
ax2.set_title('Murder Rate by State', size = 20)
plt.xticks(rotation =-90)
Вывод :
(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),
a list of 50 Text xticklabel objects)
Хотя Луизиана занимает 17-е место по численности населения (около 4,53 млн человек), в ней самый высокий уровень убийств — 10,3 на 1 млн человек.
Код № 1 : стандартное отклонение
Population_std = data.Population.std()
print ("Population std : ", Population_std)
MurderRate_std = data.MurderRate.std()
print ("\nMurderRate std : ", MurderRate_std)
Вывод :
Population std : 6848235.347401142 MurderRate std : 1.915736124302923
Код № 2 : Дисперсия
Population_var = data.Population.var()
print ("Population var : ", Population_var)
MurderRate_var = data.MurderRate.var()
print ("\nMurderRate var : ", MurderRate_var)
Вывод :
Population var : 46898327373394.445 MurderRate var : 3.670044897959184
Код №3 : Межквартильный диапазон
# Inter Quartile Range of Population
population_IQR = data.Population.describe()['75 %'] -
data.Population.describe()['25 %']
print ("Population IQR : ", population_IRQ)
# Inter Quartile Range of Murder Rate
MurderRate_IQR = data.MurderRate.describe()['75 %'] -
data.MurderRate.describe()['25 %']
print ("\nMurderRate IQR : ", MurderRate_IQR)
Вывод :
Population IQR : 4847308.0 MurderRate IQR : 3.124999999999999
Код №4 : Среднее абсолютное отклонение (MAD)
Population_mad = data.Population.mad()
print ("Population mad : ", Population_mad)
MurderRate_mad = data.MurderRate.mad()
print ("\nMurderRate mad : ", MurderRate_mad)
Вывод :
Population mad : 4450933.356000001 MurderRate mad : 1.5526400000000005