Seaborn
!conda install -y seaborn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rc
plt.rcParams["axes.unicode_minus"] = False
rc("font", family="Arial Unicode MS") # Windows: Malgun Gothic
%matplotlib inline
get_ipython().run_line_magic("matplotlib", "inline")
- np.linspace(0, 14, 100)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 310 entries, 0 to 309
Data columns (total 4 columns):Column Non-Null Count Dtype
0 구분 310 non-null object
1 죄종 310 non-null object
2 발생검거 310 non-null object
3 건수 310 non-null float64
dtypes: float64(1), object(3)
memory usage: 12.1+ KB
구분 죄종 발생검거 건수
0 중부 살인 발생 2.0
1 중부 살인 검거 2.0
2 중부 강도 발생 3.0
3 중부 강도 검거 3.0
4 중부 강간 발생 141.0
구분 죄종 발생검거 건수
305 수서 강간 검거 144.0
306 수서 절도 발생 1149.0
307 수서 절도 검거 789.0
308 수서 폭력 발생 1666.0
309 수서 폭력 검거 1431.0
Account Name Rep Manager Product Quantity Price Status
0 714466 Trantow-Barrows Craig Booker Debra Henley CPU 1 30000 presented
1 714466 Trantow-Barrows Craig Booker Debra Henley Software 1 10000 presented
2 714466 Trantow-Barrows Craig Booker Debra Henley Maintenance 2 5000 pending
3 737550 Fritsch, Russel and Anderson Craig Booker Debra Henley CPU 1 35000 declined
4 146832 Kiehn-Spinka Daniel Hilton Debra Henley CPU 2 65000 won
C:\Users\SAMSUNG\AppData\Local\Temp\ipykernel_26600\955051846.py:3: FutureWarning: pivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.
df.pivot_table(index="Name")
Account Price Quantity
Name
Barton LLC 740150 35000 1.000000
Fritsch, Russel and Anderson 737550 35000 1.000000
Herman LLC 141962 65000 2.000000
Jerde-Hilpert 412290 5000 2.000000
Kassulke, Ondricka and Metz 307599 7000 3.000000
Keeling LLC 688981 100000 5.000000
Kiehn-Spinka 146832 65000 2.000000
Koepp Ltd 729833 35000 2.000000
Kulas Inc 218895 25000 1.500000
Purdy-Kunde 163416 30000 1.000000
Stokes LLC 239344 7500 1.000000
Trantow-Barrows 714466 15000 1.333333
C:\Users\SAMSUNG\AppData\Local\Temp\ipykernel_26600\1366576855.py:2: FutureWarning: pivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.
df.pivot_table(index=["Name", "Rep", "Manager"])
Account Price Quantity
Name Rep Manager
Barton LLC John Smith Debra Henley 740150 35000 1.000000
Fritsch, Russel and Anderson Craig Booker Debra Henley 737550 35000 1.000000
Herman LLC Cedric Moss Fred Anderson 141962 65000 2.000000
Jerde-Hilpert John Smith Debra Henley 412290 5000 2.000000
Kassulke, Ondricka and Metz Wendy Yule Fred Anderson 307599 7000 3.000000
Keeling LLC Wendy Yule Fred Anderson 688981 100000 5.000000
Kiehn-Spinka Daniel Hilton Debra Henley 146832 65000 2.000000
Koepp Ltd Wendy Yule Fred Anderson 729833 35000 2.000000
Kulas Inc Daniel Hilton Debra Henley 218895 25000 1.500000
Purdy-Kunde Cedric Moss Fred Anderson 163416 30000 1.000000
Stokes LLC Cedric Moss Fred Anderson 239344 7500 1.000000
Trantow-Barrows Craig Booker Debra Henley 714466 15000 1.333333
C:\Users\SAMSUNG\AppData\Local\Temp\ipykernel_26600\1060175823.py:2: FutureWarning: pivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.
df.pivot_table(index=["Manager", "Rep"])
Account Price Quantity
Manager Rep
Debra Henley Craig Booker 720237.0 20000.000000 1.250000
Daniel Hilton 194874.0 38333.333333 1.666667
John Smith 576220.0 20000.000000 1.500000
Fred Anderson Cedric Moss 196016.5 27500.000000 1.250000
Wendy Yule 614061.5 44250.000000 3.000000
Account Name Rep Manager Product Quantity Price Status
0 714466 Trantow-Barrows Craig Booker Debra Henley CPU 1 30000 presented
1 714466 Trantow-Barrows Craig Booker Debra Henley Software 1 10000 presented
2 714466 Trantow-Barrows Craig Booker Debra Henley Maintenance 2 5000 pending
3 737550 Fritsch, Russel and Anderson Craig Booker Debra Henley CPU 1 35000 declined
4 146832 Kiehn-Spinka Daniel Hilton Debra Henley CPU 2 65000 won
Price
Manager Rep
Debra Henley Craig Booker 20000.000000
Daniel Hilton 38333.333333
John Smith 20000.000000
Fred Anderson Cedric Moss 27500.000000
Wendy Yule 44250.000000
Price
Manager Rep
Debra Henley Craig Booker 80000
Daniel Hilton 115000
John Smith 40000
Fred Anderson Cedric Moss 110000
Wendy Yule 177000
sum len
Price Price
Manager Rep
Debra Henley Craig Booker 80000 4
Daniel Hilton 115000 3
John Smith 40000 2
Fred Anderson Cedric Moss 110000 4
Wendy Yule 177000 4
Account Name Rep Manager Product Quantity Price Status
0 714466 Trantow-Barrows Craig Booker Debra Henley CPU 1 30000 presented
1 714466 Trantow-Barrows Craig Booker Debra Henley Software 1 10000 presented
2 714466 Trantow-Barrows Craig Booker Debra Henley Maintenance 2 5000 pending
3 737550 Fritsch, Russel and Anderson Craig Booker Debra Henley CPU 1 35000 declined
4 146832 Kiehn-Spinka Daniel Hilton Debra Henley CPU 2 65000 won
Product CPU Maintenance Monitor Software
Manager Rep
Debra Henley Craig Booker 65000.0 5000.0 NaN 10000.0
Daniel Hilton 105000.0 NaN NaN 10000.0
John Smith 35000.0 5000.0 NaN NaN
Fred Anderson Cedric Moss 95000.0 5000.0 NaN 10000.0
Wendy Yule 165000.0 7000.0 5000.0 NaN
Product CPU Maintenance Monitor Software
Manager Rep
Debra Henley Craig Booker 65000 5000 0 10000
Daniel Hilton 105000 0 0 10000
John Smith 35000 5000 0 0
Fred Anderson Cedric Moss 95000 5000 0 10000
Wendy Yule 165000 7000 5000 0
Price Quantity
Manager Rep Product
Debra Henley Craig Booker CPU 65000 2
Maintenance 5000 2
Software 10000 1
Daniel Hilton CPU 105000 4
Software 10000 1
John Smith CPU 35000 1
Maintenance 5000 2
Fred Anderson Cedric Moss CPU 95000 3
Maintenance 5000 1
Software 10000 1
Wendy Yule CPU 165000 7
Maintenance 7000 3
Monitor 5000 2
sum mean
Price Quantity Price Quantity
Manager Rep Product
Debra Henley Craig Booker CPU 65000 2 32500.000000 1.000000
Maintenance 5000 2 5000.000000 2.000000
Software 10000 1 10000.000000 1.000000
Daniel Hilton CPU 105000 4 52500.000000 2.000000
Software 10000 1 10000.000000 1.000000
John Smith CPU 35000 1 35000.000000 1.000000
Maintenance 5000 2 5000.000000 2.000000
Fred Anderson Cedric Moss CPU 95000 3 47500.000000 1.500000
Maintenance 5000 1 5000.000000 1.000000
Software 10000 1 10000.000000 1.000000
Wendy Yule CPU 165000 7 82500.000000 3.500000
Maintenance 7000 3 7000.000000 3.000000
Monitor 5000 2 5000.000000 2.000000
All 522000 30 30705.882353 1.764706
구분 죄종 발생검거 건수
0 중부 살인 발생 2.0
1 중부 살인 검거 2.0
2 중부 강도 발생 3.0
3 중부 강도 검거 3.0
4 중부 강간 발생 141.0
sum
건수
죄종 강간 강도 살인 절도 폭력
발생검거 검거 발생 검거 발생 검거 발생 검거 발생 검거 발생
구분
강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0
강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0
강북 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0
강서 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0
관악 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0
MultiIndex([('sum', '건수', '강간', '검거'),
('sum', '건수', '강간', '발생'),
('sum', '건수', '강도', '검거'),
('sum', '건수', '강도', '발생'),
('sum', '건수', '살인', '검거'),
('sum', '건수', '살인', '발생'),
('sum', '건수', '절도', '검거'),
('sum', '건수', '절도', '발생'),
('sum', '건수', '폭력', '검거'),
('sum', '건수', '폭력', '발생')],
names=[None, None, '죄종', '발생검거'])
구분
강남 26.0
강동 13.0
강북 4.0
강서 10.0
관악 10.0
Name: (sum, 건수, 강도, 검거), dtype: float64
MultiIndex([('강간', '검거'),
('강간', '발생'),
('강도', '검거'),
('강도', '발생'),
('살인', '검거'),
('살인', '발생'),
('절도', '검거'),
('절도', '발생'),
('폭력', '검거'),
('폭력', '발생')],
names=['죄종', '발생검거'])
죄종 강간 강도 살인 절도 폭력
발생검거 검거 발생 검거 발생 검거 발생 검거 발생 검거 발생
구분
강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0
강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0
강북 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0
강서 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0
관악 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0
Index(['강남', '강동', '강북', '강서', '관악', '광진', '구로', '금천', '남대문', '노원', '도봉',
'동대문', '동작', '마포', '방배', '서대문', '서부', '서초', '성동', '성북', '송파', '수서',
'양천', '영등포', '용산', '은평', '종로', '종암', '중랑', '중부', '혜화'],
dtype='object', name='구분')
Active code page: 65001
Package Version
anyio 3.5.0
appdirs 1.4.4
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
asttokens 2.0.5
attrs 22.1.0
Babel 2.11.0
backcall 0.2.0
beautifulsoup4 4.11.1
bleach 4.1.0
Bottleneck 1.3.5
branca 0.6.0
brotlipy 0.7.0
certifi 2022.12.7
cffi 1.15.1
charset-normalizer 2.0.4
colorama 0.4.6
comm 0.1.2
contourpy 1.0.5
cryptography 39.0.1
cycler 0.11.0
debugpy 1.5.1
decorator 5.1.1
defusedxml 0.7.1
entrypoints 0.4
et-xmlfile 1.1.0
executing 0.8.3
fake-useragent 1.1.3
fastjsonschema 2.16.2
folium 0.14.0
fonttools 4.25.0
googlemaps 2.5.1
idna 3.4
ipykernel 6.19.2
ipython 8.12.0
ipython-genutils 0.2.0
ipywidgets 8.0.4
jedi 0.18.1
Jinja2 3.1.2
joblib 1.1.1
json5 0.9.6
jsonschema 4.17.3
jupyter 1.0.0
jupyter_client 8.1.0
jupyter-console 6.6.3
jupyter_core 5.3.0
jupyter-server 1.23.4
jupyterlab 3.5.3
jupyterlab-pygments 0.1.2
jupyterlab_server 2.22.0
jupyterlab-widgets 3.0.5
kiwisolver 1.4.4
koreanize-matplotlib 0.1.1
lxml 4.9.2
MarkupSafe 2.1.1
matplotlib 3.7.1
matplotlib-inline 0.1.6
mistune 0.8.4
mkl-fft 1.3.1
mkl-random 1.2.2
mkl-service 2.4.0
munkres 1.1.4
nbclassic 0.5.5
nbclient 0.5.13
nbconvert 6.5.4
nbformat 5.7.0
nest-asyncio 1.5.6
notebook 6.5.4
notebook_shim 0.2.2
numexpr 2.8.4
numpy 1.24.3
openpyxl 3.1.2
packaging 23.0
pandas 1.5.3
pandocfilters 1.5.0
parso 0.8.3
patsy 0.5.3
pickleshare 0.7.5
Pillow 9.4.0
pip 23.0.1
platformdirs 2.5.2
ply 3.11
pooch 1.4.0
prometheus-client 0.14.1
prompt-toolkit 3.0.36
psutil 5.9.0
pure-eval 0.2.2
pycparser 2.21
Pygments 2.11.2
pyOpenSSL 23.0.0
pyparsing 3.0.9
PyQt5 5.15.7
PyQt5-sip 12.11.0
pyrsistent 0.18.0
PySocks 1.7.1
python-dateutil 2.8.2
pytz 2022.7
pywin32 305.1
pywinpty 2.0.10
pyzmq 25.0.2
qtconsole 5.4.2
QtPy 2.2.0
requests 2.29.0
scikit-learn 1.2.2
scipy 1.10.1
seaborn 0.12.2
Send2Trash 1.8.0
setuptools 66.0.0
sip 6.6.2
six 1.16.0
sniffio 1.2.0
soupsieve 2.4
stack-data 0.2.0
statsmodels 0.14.0
terminado 0.17.1
threadpoolctl 2.2.0
tinycss2 1.2.1
...
wheel 0.38.4
widgetsnbextension 4.0.5
win-inet-pton 1.1.0
xlrd 2.0.1
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
[{'address_components': [{'long_name': '608',
'short_name': '608',
'types': ['premise']},
{'long_name': '국회대로',
'short_name': '국회대로',
'types': ['political', 'sublocality', 'sublocality_level_4']},
{'long_name': '영등포구',
'short_name': '영등포구',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': '서울특별시',
'short_name': '서울특별시',
'types': ['administrative_area_level_1', 'political']},
{'long_name': '대한민국',
'short_name': 'KR',
'types': ['country', 'political']},
{'long_name': '150-043',
'short_name': '150-043',
'types': ['postal_code']}],
'formatted_address': '대한민국 서울특별시 영등포구 국회대로 608',
'geometry': {'location': {'lat': 37.5260441, 'lng': 126.9008091},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 37.5273930802915,
'lng': 126.9021580802915},
'southwest': {'lat': 37.5246951197085, 'lng': 126.8994601197085}}},
'partial_match': True,
'place_id': 'ChIJ1TimJLaffDURptXOs0Tj6sY',
'plus_code': {'compound_code': 'GWG2+C8 대한민국 서울특별시',
'global_code': '8Q98GWG2+C8'},
'types': ['establishment', 'point_of_interest', 'police']}]
Number is 1
Number is 2
Number is 3
Number is 4
0
1
4
9
16
25
36
49
64
81
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]{'address_components': [{'long_name': '608',
'short_name': '608',
'types': ['premise']},
{'long_name': '국회대로',
'short_name': '국회대로',
'types': ['political', 'sublocality', 'sublocality_level_4']},
{'long_name': '영등포구',
'short_name': '영등포구',
'types': ['political', 'sublocality', 'sublocality_level_1']},
{'long_name': '서울특별시',
'short_name': '서울특별시',
'types': ['administrative_area_level_1', 'political']},
{'long_name': '대한민국',
'short_name': 'KR',
'types': ['country', 'political']},
{'long_name': '150-043',
'short_name': '150-043',
'types': ['postal_code']}],
'formatted_address': '대한민국 서울특별시 영등포구 국회대로 608',
'geometry': {'location': {'lat': 37.5260441, 'lng': 126.9008091},
'location_type': 'ROOFTOP',
'viewport': {'northeast': {'lat': 37.5273930802915,
'lng': 126.9021580802915},
'southwest': {'lat': 37.5246951197085, 'lng': 126.8994601197085}}},
'partial_match': True,
'place_id': 'ChIJ1TimJLaffDURptXOs0Tj6sY',
'plus_code': {'compound_code': 'GWG2+C8 대한민국 서울특별시',
'global_code': '8Q98GWG2+C8'},
'types': ['establishment', 'point_of_interest', 'police']}]
1
dict
37.5260441
126.9008091
'영등포구'
죄종 강간 강도 살인 절도 폭력
발생검거 검거 발생 검거 발생 검거 발생 검거 발생 검거 발생
구분
강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0
강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0
강북 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0
강서 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0
관악 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0
죄종 강간 강도 살인 절도 폭력 구별 lat lng
발생검거 검거 발생 검거 발생 검거 발생 검거 발생 검거 발생
구분
강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0 NaN NaN NaN
강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0 NaN NaN NaN
강북 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0 NaN NaN NaN
강서 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0 NaN NaN NaN
관악 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0 NaN NaN NaN
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
죄종 강간 강도 살인 절도 폭력 구별 lat lng
발생검거 검거 발생 검거 발생 검거 발생 검거 발생 검거 발생
구분
강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0 강남구 37.509435 127.066958
강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0 강동구 37.528511 127.126822
강북 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0 강북구 37.637197 127.027305
강서 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0 강서구 37.551362 126.850280
관악 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0 관악구 37.474395 126.951349
'강도검거'
13
['강간검거',
'강간발생',
'강도검거',
'강도발생',
'살인검거',
'살인발생',
'절도검거',
'절도발생',
'폭력검거',
'폭력발생',
'구별',
'lat',
'lng']
(['강간검거',
'강간발생',
'강도검거',
'강도발생',
'살인검거',
'살인발생',
'절도검거',
'절도발생',
'폭력검거',
'폭력발생',
'구별',
'lat',
'lng'],
13,
13)
강간검거 강간발생 강도검거 강도발생 살인검거 살인발생 절도검거 절도발생 폭력검거 폭력발생 구별 lat lng
구분
강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0 강남구 37.509435 127.066958
강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0 강동구 37.528511 127.126822
강북 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0 강북구 37.637197 127.027305
강서 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0 강서구 37.551362 126.850280
관악 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0 관악구 37.474395 126.951349
구분 강간검거 강간발생 강도검거 강도발생 살인검거 살인발생 절도검거 절도발생 폭력검거 폭력발생 구별 lat lng
0 강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0 강남구 37.509435 127.066958
1 강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0 강동구 37.528511 127.126822
강간검거 강간발생 강도검거 강도발생 살인검거 살인발생 절도검거 절도발생 폭력검거 폭력발생 구별 lat lng
구분
강남 269.0 339.0 26.0 24.0 3.0 3.0 1129.0 2438.0 2096.0 2336.0 강남구 37.509435 127.066958
강동 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0 강동구 37.528511 127.126822
강북 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0 강북구 37.637197 127.027305
강서 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0 강서구 37.551362 126.850280
관악 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0 관악구 37.474395 126.951349
강간검거 강간발생 강도검거 강도발생 살인검거 살인발생 절도검거 절도발생 폭력검거 폭력발생
구별
강남구 413.0 516.0 42.0 39.0 5.0 5.0 1918.0 3587.0 3527.0 4002.0
강동구 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0
강북구 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0
강서구 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0
관악구 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0
구별
강남구 1.076923
강동구 0.928571
강북구 0.800000
강서구 1.000000
관악구 0.833333
광진구 0.545455
구로구 1.300000
금천구 1.000000
노원구 1.500000
도봉구 1.000000
동대문구 1.200000
동작구 1.000000
마포구 1.750000
서대문구 0.800000
서초구 0.769231
성동구 1.666667
성북구 1.000000
송파구 0.800000
양천구 1.000000
영등포구 0.736842
용산구 1.111111
은평구 0.777778
종로구 0.750000
중구 0.875000
중랑구 1.000000
dtype: float64
강도검거 살인검거
구별
강남구 1.076923 0.128205
강동구 0.928571 0.357143
강북구 0.800000 1.200000
강간검거 강도검거 살인검거 절도검거 폭력검거
구별
강남구 0.800388 1.076923 1.000000 0.534709 0.881309
강동구 0.950000 0.928571 1.250000 0.514253 0.869960
강북구 0.732719 0.800000 0.857143 0.549918 0.893449
강서구 0.869091 1.000000 1.111111 0.548156 0.863920
관악구 0.819876 0.833333 1.166667 0.445554 0.836785
강간검거 강간발생 강도검거 강도발생 살인검거 살인발생 절도검거 절도발생 폭력검거 폭력발생 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율
구별
강남구 413.0 516.0 42.0 39.0 5.0 5.0 1918.0 3587.0 3527.0 4002.0 80.038760 107.692308 100.000000 53.470867 88.130935
강동구 152.0 160.0 13.0 14.0 5.0 4.0 902.0 1754.0 2201.0 2530.0 95.000000 92.857143 125.000000 51.425314 86.996047
강북구 159.0 217.0 4.0 5.0 6.0 7.0 672.0 1222.0 2482.0 2778.0 73.271889 80.000000 85.714286 54.991817 89.344852
강서구 239.0 275.0 10.0 10.0 10.0 9.0 1070.0 1952.0 2768.0 3204.0 86.909091 100.000000 111.111111 54.815574 86.392010
관악구 264.0 322.0 10.0 12.0 7.0 6.0 937.0 2103.0 2707.0 3235.0 81.987578 83.333333 116.666667 44.555397 83.678516
강간발생 강도발생 살인발생 절도발생 폭력발생 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율
구별
강남구 516.0 39.0 5.0 3587.0 4002.0 80.038760 107.692308 100.000000 53.470867 88.130935
강동구 160.0 14.0 4.0 1754.0 2530.0 95.000000 92.857143 125.000000 51.425314 86.996047
강북구 217.0 5.0 7.0 1222.0 2778.0 73.271889 80.000000 85.714286 54.991817 89.344852
강서구 275.0 10.0 9.0 1952.0 3204.0 86.909091 100.000000 111.111111 54.815574 86.392010
관악구 322.0 12.0 6.0 2103.0 3235.0 81.987578 83.333333 116.666667 44.555397 83.678516
강간발생 강도발생 살인발생 절도발생 폭력발생 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율
구별
강남구 516.0 39.0 5.0 3587.0 4002.0 80.038760 100.000000 100.000000 53.470867 88.130935
강동구 160.0 14.0 4.0 1754.0 2530.0 95.000000 92.857143 100.000000 51.425314 86.996047
강북구 217.0 5.0 7.0 1222.0 2778.0 73.271889 80.000000 85.714286 54.991817 89.344852
강서구 275.0 10.0 9.0 1952.0 3204.0 86.909091 100.000000 100.000000 54.815574 86.392010
관악구 322.0 12.0 6.0 2103.0 3235.0 81.987578 83.333333 100.000000 44.555397 83.678516
강간 강도 살인 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율
구별
강남구 516.0 39.0 5.0 3587.0 4002.0 80.038760 100.000000 100.000000 53.470867 88.130935
강동구 160.0 14.0 4.0 1754.0 2530.0 95.000000 92.857143 100.000000 51.425314 86.996047
강북구 217.0 5.0 7.0 1222.0 2778.0 73.271889 80.000000 85.714286 54.991817 89.344852
강서구 275.0 10.0 9.0 1952.0 3204.0 86.909091 100.000000 100.000000 54.815574 86.392010
관악구 322.0 12.0 6.0 2103.0 3235.0 81.987578 83.333333 100.000000 44.555397 83.678516
강간 강도 살인 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율
구별
강남구 516.0 39.0 5.0 3587.0 4002.0 80.038760 100.000000 100.000000 53.470867 88.130935
강동구 160.0 14.0 4.0 1754.0 2530.0 95.000000 92.857143 100.000000 51.425314 86.996047
강북구 217.0 5.0 7.0 1222.0 2778.0 73.271889 80.000000 85.714286 54.991817 89.344852
강서구 275.0 10.0 9.0 1952.0 3204.0 86.909091 100.000000 100.000000 54.815574 86.392010
관악구 322.0 12.0 6.0 2103.0 3235.0 81.987578 83.333333 100.000000 44.555397 83.678516
구별
강남구 1.000000
강동구 0.358974
강북구 0.128205
강서구 0.256410
관악구 0.307692
광진구 0.282051
구로구 0.256410
금천구 0.179487
노원구 0.153846
도봉구 0.128205
동대문구 0.256410
동작구 0.179487
마포구 0.102564
서대문구 0.128205
서초구 0.333333
성동구 0.076923
성북구 0.205128
송파구 0.384615
양천구 0.179487
영등포구 0.487179
용산구 0.230769
은평구 0.230769
종로구 0.307692
중구 0.205128
중랑구 0.358974
Name: 강도, dtype: float64
살인 강도 강간 절도 폭력
구별
강남구 0.384615 1.000000 1.000000 1.000000 1.000000
강동구 0.307692 0.358974 0.310078 0.488988 0.632184
강북구 0.538462 0.128205 0.420543 0.340675 0.694153
강서구 0.692308 0.256410 0.532946 0.544187 0.800600
관악구 0.461538 0.307692 0.624031 0.586284 0.808346
강간 강도 살인 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율
구별
강남구 516.0 39.0 5.0 3587.0 4002.0 80.03876 100.0 100.0 53.470867 88.130935
살인 강도 강간 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율
구별
강남구 0.384615 1.000000 1.000000 1.000000 1.000000 80.038760 100.000000 100.000000 53.470867 88.130935
강동구 0.307692 0.358974 0.310078 0.488988 0.632184 95.000000 92.857143 100.000000 51.425314 86.996047
강북구 0.538462 0.128205 0.420543 0.340675 0.694153 73.271889 80.000000 85.714286 54.991817 89.344852
강서구 0.692308 0.256410 0.532946 0.544187 0.800600 86.909091 100.000000 100.000000 54.815574 86.392010
관악구 0.461538 0.307692 0.624031 0.586284 0.808346 81.987578 83.333333 100.000000 44.555397 83.678516
소계 최근증가율 인구수 한국인 외국인 고령자 외국인비율 고령자비율 CCTV비율 오차
구별
강남구 3238 150.619195 561052 556164 4888 65060 0.871220 11.596073 0.577130 1549.200326
강동구 1010 166.490765 440359 436223 4136 56161 0.939234 12.753458 0.229358 -544.642322
강북구 831 125.203252 328002 324479 3523 56530 1.074079 17.234651 0.253352 -598.750923
강서구 911 134.793814 608255 601691 6564 76032 1.079153 12.500021 0.149773 -830.268578
관악구 2109 149.290780 520929 503297 17632 70046 3.384722 13.446362 0.404854 464.799395
살인 강도 강간 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율 인구수 CCTV
구별
강남구 0.384615 1.000000 1.000000 1.000000 1.000000 80.038760 100.000000 100.000000 53.470867 88.130935 561052 3238
강동구 0.307692 0.358974 0.310078 0.488988 0.632184 95.000000 92.857143 100.000000 51.425314 86.996047 440359 1010
강북구 0.538462 0.128205 0.420543 0.340675 0.694153 73.271889 80.000000 85.714286 54.991817 89.344852 328002 831
강서구 0.692308 0.256410 0.532946 0.544187 0.800600 86.909091 100.000000 100.000000 54.815574 86.392010 608255 911
관악구 0.461538 0.307692 0.624031 0.586284 0.808346 81.987578 83.333333 100.000000 44.555397 83.678516 520929 2109
살인 강도 강간 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율 인구수 CCTV 범죄
구별
강남구 0.384615 1.000000 1.000000 1.000000 1.000000 80.038760 100.000000 100.000000 53.470867 88.130935 561052 3238 0.876923
강동구 0.307692 0.358974 0.310078 0.488988 0.632184 95.000000 92.857143 100.000000 51.425314 86.996047 440359 1010 0.419583
강북구 0.538462 0.128205 0.420543 0.340675 0.694153 73.271889 80.000000 85.714286 54.991817 89.344852 328002 831 0.424407
강서구 0.692308 0.256410 0.532946 0.544187 0.800600 86.909091 100.000000 100.000000 54.815574 86.392010 608255 911 0.565290
관악구 0.461538 0.307692 0.624031 0.586284 0.808346 81.987578 83.333333 100.000000 44.555397 83.678516 520929 2109 0.557578
array([0.357143, 1. , 1. , 0.977118, 0.733773])
0.8136068
array([[0.357143, 1. , 1. , 0.977118, 0.733773],
[0.285714, 0.358974, 0.310078, 0.477799, 0.46388 ]])
array([0.8136068, 0.379289 ])
array([0.3214285, 0.679487 , 0.655039 , 0.7274585, 0.5988265])
살인 강도 강간 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율 인구수 CCTV 범죄 검거
구별
강남구 0.384615 1.000000 1.000000 1.000000 1.000000 80.038760 100.000000 100.000000 53.470867 88.130935 561052 3238 0.876923 84.328112
강동구 0.307692 0.358974 0.310078 0.488988 0.632184 95.000000 92.857143 100.000000 51.425314 86.996047 440359 1010 0.419583 85.255701
강북구 0.538462 0.128205 0.420543 0.340675 0.694153 73.271889 80.000000 85.714286 54.991817 89.344852 328002 831 0.424407 76.664569
강서구 0.692308 0.256410 0.532946 0.544187 0.800600 86.909091 100.000000 100.000000 54.815574 86.392010 608255 911 0.565290 85.623335
관악구 0.461538 0.307692 0.624031 0.586284 0.808346 81.987578 83.333333 100.000000 44.555397 83.678516 520929 2109 0.557578 78.710965
살인 강도 강간 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율 인구수 CCTV 범죄 검거
구별
강남구 0.384615 1.000000 1.000000 1.000000 1.000000 80.038760 100.000000 100.000000 53.470867 88.130935 561052 3238 0.876923 84.328112
강동구 0.307692 0.358974 0.310078 0.488988 0.632184 95.000000 92.857143 100.000000 51.425314 86.996047 440359 1010 0.419583 85.255701
강북구 0.538462 0.128205 0.420543 0.340675 0.694153 73.271889 80.000000 85.714286 54.991817 89.344852 328002 831 0.424407 76.664569
강서구 0.692308 0.256410 0.532946 0.544187 0.800600 86.909091 100.000000 100.000000 54.815574 86.392010 608255 911 0.565290 85.623335
관악구 0.461538 0.307692 0.624031 0.586284 0.808346 81.987578 83.333333 100.000000 44.555397 83.678516 520929 2109 0.557578 78.710965
광진구 0.307692 0.282051 0.540698 0.734876 0.597701 83.870968 54.545455 100.000000 40.098634 84.071906 372298 878 0.492604 72.517393
구로구 0.692308 0.256410 0.529070 0.532478 0.790605 66.300366 100.000000 100.000000 45.078534 84.702908 441559 1884 0.560174 79.216362
금천구 0.461538 0.179487 0.339147 0.352384 0.547976 81.714286 100.000000 100.000000 51.740506 88.736890 253491 1348 0.376107 84.438336
노원구 0.384615 0.153846 0.308140 0.517703 0.628686 89.308176 100.000000 100.000000 39.849219 84.419714 558075 1566 0.398598 82.715422
도봉구 0.230769 0.128205 0.238372 0.241427 0.360070 98.373984 100.000000 100.000000 56.812933 90.839695 346234 825 0.239769 89.205322
동대문구 0.384615 0.256410 0.368217 0.540842 0.660170 83.157895 100.000000 100.000000 55.206186 89.969720 366011 1870 0.442051 85.666760
동작구 0.615385 0.179487 0.629845 0.341790 0.415042 45.846154 100.000000 75.000000 45.187602 86.935581 408493 1302 0.436310 70.593867
마포구 0.307692 0.102564 0.773256 0.704488 0.734383 80.200501 100.000000 100.000000 37.198259 85.062947 385783 980 0.524477 80.492341
서대문구 0.461538 0.128205 0.339147 0.419013 0.493753 84.000000 80.000000 100.000000 50.033267 83.198381 325028 1254 0.368331 79.446329
서초구 0.384615 0.333333 0.829457 0.614720 0.584208 63.317757 76.923077 100.000000 50.204082 86.783576 445401 2297 0.549267 75.445698
성동구 0.307692 0.076923 0.201550 0.361305 0.404548 75.000000 100.000000 100.000000 69.135802 86.967264 312711 1327 0.270404 86.220613
성북구 0.307692 0.205128 0.298450 0.409813 0.526737 75.974026 100.000000 75.000000 49.319728 86.290323 455407 1651 0.349564 77.316815
송파구 0.692308 0.384615 0.453488 0.708949 0.821839 78.632479 80.000000 88.888889 41.211168 85.375494 671173 1081 0.612240 74.821606
양천구 0.384615 0.179487 0.253876 0.479231 0.562219 82.442748 100.000000 100.000000 43.920884 85.244444 475018 2482 0.371886 82.321615
영등포구 1.000000 0.487179 0.689922 0.652635 0.897801 63.202247 73.684211 100.000000 40.153780 83.690509 402024 1277 0.745508 72.146149
용산구 0.307692 0.230769 0.486434 0.415110 0.595702 85.258964 100.000000 100.000000 40.228341 84.228188 244444 2096 0.407142 81.943099
은평구 0.461538 0.230769 0.302326 0.464455 0.665667 91.025641 77.777778 100.000000 53.421369 86.636637 491202 2108 0.424951 81.772285
종로구 0.461538 0.307692 0.461240 0.540842 0.565467 74.369748 75.000000 33.333333 39.587629 87.361909 164257 1619 0.467356 61.930524
중구 0.230769 0.205128 0.383721 0.599387 0.555972 74.747475 87.500000 100.000000 42.511628 89.707865 134593 1023 0.394995 78.893394
중랑구 0.615385 0.358974 0.317829 0.471425 0.790605 91.463415 100.000000 87.500000 62.211709 85.714286 412780 916 0.510844 85.377882
살인 강도 강간 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 폭력검거율 인구수 CCTV 범죄 검거
구별
강남구 0.384615 1.000000 1.000000 1.000000 1.000000 80.038760 100.000000 100.000000 53.470867 88.130935 561052 3238 0.876923 84.328112
강동구 0.307692 0.358974 0.310078 0.488988 0.632184 95.000000 92.857143 100.000000 51.425314 86.996047 440359 1010 0.419583 85.255701
강북구 0.538462 0.128205 0.420543 0.340675 0.694153 73.271889 80.000000 85.714286 54.991817 89.344852 328002 831 0.424407 76.664569
강서구 0.692308 0.256410 0.532946 0.544187 0.800600 86.909091 100.000000 100.000000 54.815574 86.392010 608255 911 0.565290 85.623335
관악구 0.461538 0.307692 0.624031 0.586284 0.808346 81.987578 83.333333 100.000000 44.555397 83.678516 520929 2109 0.557578 78.710965
광진구 0.307692 0.282051 0.540698 0.734876 0.597701 83.870968 54.545455 100.000000 40.098634 84.071906 372298 878 0.492604 72.517393
구로구 0.692308 0.256410 0.529070 0.532478 0.790605 66.300366 100.000000 100.000000 45.078534 84.702908 441559 1884 0.560174 79.216362
금천구 0.461538 0.179487 0.339147 0.352384 0.547976 81.714286 100.000000 100.000000 51.740506 88.736890 253491 1348 0.376107 84.438336
노원구 0.384615 0.153846 0.308140 0.517703 0.628686 89.308176 100.000000 100.000000 39.849219 84.419714 558075 1566 0.398598 82.715422
도봉구 0.230769 0.128205 0.238372 0.241427 0.360070 98.373984 100.000000 100.000000 56.812933 90.839695 346234 825 0.239769 89.205322
동대문구 0.384615 0.256410 0.368217 0.540842 0.660170 83.157895 100.000000 100.000000 55.206186 89.969720 366011 1870 0.442051 85.666760
동작구 0.615385 0.179487 0.629845 0.341790 0.415042 45.846154 100.000000 75.000000 45.187602 86.935581 408493 1302 0.436310 70.593867
마포구 0.307692 0.102564 0.773256 0.704488 0.734383 80.200501 100.000000 100.000000 37.198259 85.062947 385783 980 0.524477 80.492341
서대문구 0.461538 0.128205 0.339147 0.419013 0.493753 84.000000 80.000000 100.000000 50.033267 83.198381 325028 1254 0.368331 79.446329
서초구 0.384615 0.333333 0.829457 0.614720 0.584208 63.317757 76.923077 100.000000 50.204082 86.783576 445401 2297 0.549267 75.445698
성동구 0.307692 0.076923 0.201550 0.361305 0.404548 75.000000 100.000000 100.000000 69.135802 86.967264 312711 1327 0.270404 86.220613
성북구 0.307692 0.205128 0.298450 0.409813 0.526737 75.974026 100.000000 75.000000 49.319728 86.290323 455407 1651 0.349564 77.316815
송파구 0.692308 0.384615 0.453488 0.708949 0.821839 78.632479 80.000000 88.888889 41.211168 85.375494 671173 1081 0.612240 74.821606
양천구 0.384615 0.179487 0.253876 0.479231 0.562219 82.442748 100.000000 100.000000 43.920884 85.244444 475018 2482 0.371886 82.321615
영등포구 1.000000 0.487179 0.689922 0.652635 0.897801 63.202247 73.684211 100.000000 40.153780 83.690509 402024 1277 0.745508 72.146149
용산구 0.307692 0.230769 0.486434 0.415110 0.595702 85.258964 100.000000 100.000000 40.228341 84.228188 244444 2096 0.407142 81.943099
은평구 0.461538 0.230769 0.302326 0.464455 0.665667 91.025641 77.777778 100.000000 53.421369 86.636637 491202 2108 0.424951 81.772285
종로구 0.461538 0.307692 0.461240 0.540842 0.565467 74.369748 75.000000 33.333333 39.587629 87.361909 164257 1619 0.467356 61.930524
중구 0.230769 0.205128 0.383721 0.599387 0.555972 74.747475 87.500000 100.000000 42.511628 89.707865 134593 1023 0.394995 78.893394
중랑구 0.615385 0.358974 0.317829 0.471425 0.790605 91.463415 100.000000 87.500000 62.211709 85.714286 412780 916 0.510844 85.377882
array([ 0. , 0.14141414, 0.28282828, 0.42424242, 0.56565657,
0.70707071, 0.84848485, 0.98989899, 1.13131313, 1.27272727,
1.41414141, 1.55555556, 1.6969697 , 1.83838384, 1.97979798,
2.12121212, 2.26262626, 2.4040404 , 2.54545455, 2.68686869,
2.82828283, 2.96969697, 3.11111111, 3.25252525, 3.39393939,
3.53535354, 3.67676768, 3.81818182, 3.95959596, 4.1010101 ,
4.24242424, 4.38383838, 4.52525253, 4.66666667, 4.80808081,
4.94949495, 5.09090909, 5.23232323, 5.37373737, 5.51515152,
5.65656566, 5.7979798 , 5.93939394, 6.08080808, 6.22222222,
6.36363636, 6.50505051, 6.64646465, 6.78787879, 6.92929293,
7.07070707, 7.21212121, 7.35353535, 7.49494949, 7.63636364,
7.77777778, 7.91919192, 8.06060606, 8.2020202 , 8.34343434,
8.48484848, 8.62626263, 8.76767677, 8.90909091, 9.05050505,
9.19191919, 9.33333333, 9.47474747, 9.61616162, 9.75757576,
9.8989899 , 10.04040404, 10.18181818, 10.32323232, 10.46464646,
10.60606061, 10.74747475, 10.88888889, 11.03030303, 11.17171717,
11.31313131, 11.45454545, 11.5959596 , 11.73737374, 11.87878788,
12.02020202, 12.16161616, 12.3030303 , 12.44444444, 12.58585859,
12.72727273, 12.86868687, 13.01010101, 13.15151515, 13.29292929,
13.43434343, 13.57575758, 13.71717172, 13.85858586, 14. ])
x = np.linspace(0, 14, 100)
y1 = np.sin(x)
y2 = 2 np.sin(x + 0.5)
y3 = 3 np.sin(x + 1.0)
y4 = 4 * np.sin(x + 1.5)
plt.figure(figsize=(10, 6))
plt.plot(x, y1, x, y2, x, y3, x, y4)
plt.show()
- tips["day"].unique()
['Sun', 'Sat', 'Thur', 'Fri']
Categories (4, object): ['Thur', 'Fri', 'Sat', 'Sun']
lmplot: total_bil과 tip 사이 관계 파악
sns.set_style("darkgrid")
sns.lmplot(x="total_bill", y="tip", data=tips, height=7) # size => height
plt.show()
hue option
sns.set_style("darkgrid")
sns.lmplot(x="total_bill", y="tip", data=tips, height=7, hue="smoker")
plt.show()
