import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# 경고 메세지를 숨겨주는 warnings
import warnings
warnings.filterwarnings("ignore")

# 한글 설정
plt.rc("font", family="Malgun Gothic")

# 시각화 기본 사이즈 정의
plt.rcParams["figure.figsize"] = (5, 3) #(가로, 세로)

x = [1,2,3,4,5,6,7,8,9,10]
y = [0, -0.5, -1.6 ,3 ,2.9 ,1.8 ,6, 6.4, 6.6, 10.4]
print(x)
y

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

[0, -0.5, -1.6, 3, 2.9, 1.8, 6, 6.4, 6.6, 10.4]

# line 형태로 출력
plt.plot(x, y)

[<matplotlib.lines.Line2D at 0x193ab7db050>]

# 분포를 점으로 찍어서 출력
plt.scatter(x, y)

<matplotlib.collections.PathCollection at 0x193abf3ec10>

# bar 차트로 출력
plt.bar(x, y)

<BarContainer object of 10 artists>

data_1_x = [1,2,3]
data_1_y = [3,6,9]
data_2_x = [1,2,3]
data_2_y = [2,5,9]
plt.plot(data_1_x, data_1_y)
plt.plot(data_2_x, data_2_y)

# 타이틀(제목) 설정, 폰트 사이즈 설정
plt.title("타이틀 예시", fontsize=20)

Text(0.5, 1.0, '타이틀 예시')

# 그래프의 크기를 다시 지정하여 출력
fig, ax = plt.subplots(figsize=(10, 3))
ax.plot(data_1_x, data_1_y)
ax.plot(data_2_x, data_2_y)
ax.set_title("타이틀")

Text(0.5, 1.0, '타이틀')

plt.plot(data_1_x, data_1_y)
plt.plot(data_2_x, data_2_y)
plt.title("타이틀")

# x축을 90도 회전
plt.xticks(rotation=90)

# y축을 45도 회전
plt.yticks(rotation=45)

plt.show()

# 그래프의 크기를 다시 지정하여 출력
fig, ax = plt.subplots(figsize=(10, 3))
ax.plot(data_1_x, data_1_y)
ax.plot(data_2_x, data_2_y)
ax.set_title("타이틀")


ax.tick_params(axis="y", rotation=30)
ax.tick_params(axis="x", rotation=90)

plt.plot(data_1_x, data_1_y)
plt.plot(data_2_x, data_2_y)
plt.title("타이틀")

# 범례 설정
plt.legend(["legend1", "22222"], fontsize=10)

plt.show()

# 그래프의 크기를 다시 지정하여 출력
fig, ax = plt.subplots(figsize=(10, 3))

# 범례 이름 설정 1111, 2222
ax.plot(data_1_x, data_1_y, label='1111')
ax.plot(data_2_x, data_2_y, label='2222')
ax.set_title("타이틀")

# 범례 사이즈 설정
ax.legend(fontsize="20")

ax.tick_params(axis="y", rotation=30)
ax.tick_params(axis="x", rotation=90)

# 데이터를 1행, 2열로 출력
# 행 하나에 그래프가 2개 들어감
fg, ax = plt.subplots(1,2, figsize=(10,3))
ax[0].plot(data_1_x, data_1_y)
ax[1].scatter(x, y)

<matplotlib.collections.PathCollection at 0x193af1d7c10>

# 데이터를 2행, 1열로 출력
# 행 하나에 그래프가 2개씩 2줄 들어감
fg, ax = plt.subplots(2,2, figsize=(10,3))

# 위치를 [][] 로 표시
ax[0][0].plot(x, y)
ax[1][1].scatter(x, y)

<matplotlib.collections.PathCollection at 0x193af2d7c10>

# 데이터를 2행, 1열로 출력
# 행 하나에 그래프가 2개씩 2줄 들어감
fg, ax = plt.subplots(2,2, figsize=(10,3))

# 위치를 [][] 로 표시
ax[0][0].plot(x, y)
ax[1][1].scatter(x, y)
t_x = [10, 20, 30]

# pie 차트 출력
ax[0][1].pie(t_x, labels=["일", "이", "삼"])

h = [1, 1, 2, 3, 4, 5, 6, 7, 8, 9]
ax[1][0].hist(h, bins=9)

(array([2., 1., 1., 1., 1., 1., 1., 1., 1.]),
 array([1.        , 1.88888889, 2.77777778, 3.66666667, 4.55555556,
        5.44444444, 6.33333333, 7.22222222, 8.11111111, 9.        ]),
 <BarContainer object of 9 artists>)

import seaborn as sns

# seaborn 에서 기본으로 제공해주는 dataset 인 tips
#지불한 총 요금, 팁, 성별, 흡연, 날짜, 시간, 사이즈를 가지고 있다
tips = sns.load_dataset('tips')
tips

# 요일 확인
tips['day'].unique()

['Sun', 'Sat', 'Thur', 'Fri']
Categories (4, object): ['Thur', 'Fri', 'Sat', 'Sun']

# 요일 별로 그룹화해서 몇 명이 왔는지 카운트
tips.groupby("day").count()

group = tips.groupby("day").count()['total_bill']

# list 형으로 형변환
x = list(group.index)
y = list(group) # 갯수만 얻어옴

# 요일별 방문 사람 수 시각화
plt.bar(x, y)

<BarContainer object of 4 artists>

# 요일 별 총 금액 확인
group = tips.groupby("day")['total_bill'].sum()

# list 형으로 형변환
x = list(group.index)
y = list(group) # 갯수만 얻어옴

# 요일별 금액 확인
plt.bar(x, y)

<BarContainer object of 4 artists>

# 요일 별 총 금액 확인
group = tips.groupby("day")['total_bill'].sum()

# list 형으로 형변환
x = list(group.index)
y = list(group) # 갯수만 얻어옴

# 요일별 금액 확인
sns.barplot(x=x, y=y)

<Axes: >

tips.groupby('day').describe()

fig, axes = plt.subplots(1, 2, figsize=(10,3))

# palette : 색상 설정
# ci : 분포 삭제
sns.barplot(x="day", y="total_bill", data=tips, palette="deep", ax=axes[1], ci=None)

<Axes: xlabel='day', ylabel='total_bill'>

tips.head()

# 판매량 당 팁 발생 확인
sns.relplot(x = "total_bill", y = "tip", data = tips)

<seaborn.axisgrid.FacetGrid at 0x193b2af9ed0>

# hue 를 사용해서 요일 별로 그룹화하여 시각화해줌
sns.relplot(x = "total_bill", y = "tip", hue="day", data = tips)

<seaborn.axisgrid.FacetGrid at 0x193b905c250>

# 날짜 별로 그래프를 분리
sns.relplot(x = "total_bill", y = "tip", hue="day", data = tips, col="day")

<seaborn.axisgrid.FacetGrid at 0x193b9091710>

# scatterplot 을 사용해서도 요일 별로 그래프를 분류할 수 있다
sns.scatterplot(x = "total_bill", y = "tip", hue="day", data = tips)

<Axes: xlabel='total_bill', ylabel='tip'>

# scatterplot 의 타이틀 지정 ( 변수로 그래프를 저장하여 타이틀 지정해야 함 )
g = sns.scatterplot(x = "total_bill", y = "tip", hue="day", data = tips)
g.set_title("scatter test", fontsize=20, color="blue")

Text(0.5, 1.0, 'scatter test')

# lineplot 을 사용하여 선으로 그래프를 표현
g = sns.lineplot(x = "total_bill", y = "tip", hue="day", data = tips)

# titanic 에 대한 dataset 도 seaborn 에서 기본 제공
t = sns.load_dataset("titanic")
t.head()

# 연령별 탑승 수 확인
# color 를 skyblue 로 변환
sns.histplot(x="age", data=t, bins=10, color="skyblue")

<Axes: xlabel='age', ylabel='Count'>

# sns.barplot(x="sex", y="tip", data=tips)
r = sns.barplot(x=tips["sex"], y=tips["tip"])
r.set_xlabel("성별", fontsize=10)
r.set_ylabel("팁", fontsize=10)
r.set_title("bar")

Text(0.5, 1.0, 'bar')

sns.boxplot(x="day", y="tip", data=tips)

<Axes: xlabel='day', ylabel='tip'>

# 담배를 피는 사람과 피지 않는 사람의 팁 차이 를 요일별로 확인
sns.boxplot(x="day", y="tip", hue="smoker", data=tips)

<Axes: xlabel='day', ylabel='tip'>

# 각 컬럼의 상관관계를 확인
# tip 이 올라갈때 total_bill 이 올라가면
# 수치가 1과 가깝게 나옴
tips.corr(numeric_only=True)

# 각 컬럼의 상관관계를 시각화 ( heatmap 사용 )
# annot = True : 수치를 숫자로 나타내준다
# 수치가 0.7 이상이면 서로 깊은 연관관계가 있다
sns.heatmap(tips.corr(numeric_only=True), annot=True)

<Axes: >

!pip list

Package                           Version
--------------------------------- ------------
aiobotocore                       2.7.0
aiohttp                           3.9.3
aioitertools                      0.7.1
aiosignal                         1.2.0
alabaster                         0.7.12
altair                            5.0.1
anaconda-anon-usage               0.4.3
anaconda-catalogs                 0.2.0
anaconda-client                   1.12.3
anaconda-cloud-auth               0.1.4
anaconda-navigator                2.5.2
anaconda-project                  0.11.1
anyio                             4.2.0
appdirs                           1.4.4
archspec                          0.2.1
argon2-cffi                       21.3.0
argon2-cffi-bindings              21.2.0
arrow                             1.2.3
astroid                           2.14.2
astropy                           5.3.4
asttokens                         2.0.5
async-lru                         2.0.4
atomicwrites                      1.4.0
attrs                             23.1.0
Automat                           20.2.0
autopep8                          1.6.0
Babel                             2.11.0
backports.functools-lru-cache     1.6.4
backports.tempfile                1.0
backports.weakref                 1.0.post1
bcrypt                            3.2.0
beautifulsoup4                    4.12.2
binaryornot                       0.4.4
black                             23.11.0
bleach                            4.1.0
blinker                           1.6.2
bokeh                             3.3.4
boltons                           23.0.0
botocore                          1.31.64
Bottleneck                        1.3.7
Brotli                            1.0.9
cachetools                        4.2.2
certifi                           2024.2.2
cffi                              1.16.0
chardet                           4.0.0
charset-normalizer                2.0.4
click                             8.1.7
cloudpickle                       2.2.1
clyent                            1.2.2
colorama                          0.4.6
colorcet                          3.0.1
comm                              0.1.2
conda                             24.1.2
conda-build                       24.1.2
conda-content-trust               0.2.0
conda_index                       0.4.0
conda-libmamba-solver             24.1.0
conda-pack                        0.6.0
conda-package-handling            2.2.0
conda_package_streaming           0.9.0
conda-repo-cli                    1.0.75
conda-token                       0.4.0
conda-verify                      3.4.2
constantly                        23.10.4
contourpy                         1.2.0
cookiecutter                      2.5.0
cryptography                      42.0.2
cssselect                         1.2.0
cycler                            0.11.0
cytoolz                           0.12.2
dask                              2023.11.0
datashader                        0.16.0
debugpy                           1.6.7
decorator                         5.1.1
defusedxml                        0.7.1
diff-match-patch                  20200713
dill                              0.3.7
distributed                       2023.11.0
distro                            1.8.0
docstring-to-markdown             0.11
docutils                          0.18.1
entrypoints                       0.4
et-xmlfile                        1.1.0
executing                         0.8.3
fastjsonschema                    2.16.2
filelock                          3.13.1
flake8                            6.0.0
Flask                             2.2.5
fonttools                         4.25.0
frozenlist                        1.4.0
fsspec                            2023.10.0
future                            0.18.3
gensim                            4.3.0
gitdb                             4.0.7
GitPython                         3.1.37
gmpy2                             2.1.2
greenlet                          3.0.1
h5py                              3.9.0
HeapDict                          1.0.1
holoviews                         1.18.3
hvplot                            0.9.2
hyperlink                         21.0.0
idna                              3.4
imagecodecs                       2023.1.23
imageio                           2.33.1
imagesize                         1.4.1
imbalanced-learn                  0.11.0
importlib-metadata                7.0.1
incremental                       22.10.0
inflection                        0.5.1
iniconfig                         1.1.1
intake                            0.6.8
intervaltree                      3.1.0
ipykernel                         6.28.0
ipython                           8.20.0
ipython-genutils                  0.2.0
ipywidgets                        7.6.5
isort                             5.9.3
itemadapter                       0.3.0
itemloaders                       1.1.0
itsdangerous                      2.0.1
jaraco.classes                    3.2.1
jedi                              0.18.1
jellyfish                         1.0.1
Jinja2                            3.1.3
jmespath                          1.0.1
joblib                            1.2.0
json5                             0.9.6
jsonpatch                         1.32
jsonpointer                       2.1
jsonschema                        4.19.2
jsonschema-specifications         2023.7.1
jupyter                           1.0.0
jupyter_client                    8.6.0
jupyter-console                   6.6.3
jupyter_core                      5.5.0
jupyter-events                    0.8.0
jupyter-lsp                       2.2.0
jupyter_server                    2.10.0
jupyter_server_terminals          0.4.4
jupyterlab                        4.0.11
jupyterlab-pygments               0.1.2
jupyterlab_server                 2.25.1
jupyterlab-widgets                3.0.9
keyring                           23.13.1
kiwisolver                        1.4.4
lazy_loader                       0.3
lazy-object-proxy                 1.6.0
lckr_jupyterlab_variableinspector 3.1.0
libarchive-c                      2.9
libmambapy                        1.5.6
linkify-it-py                     2.0.0
llvmlite                          0.42.0
lmdb                              1.4.1
locket                            1.0.0
lxml                              4.9.3
lz4                               4.3.2
Markdown                          3.4.1
markdown-it-py                    2.2.0
MarkupSafe                        2.1.3
matplotlib                        3.8.0
matplotlib-inline                 0.1.6
mccabe                            0.7.0
mdit-py-plugins                   0.3.0
mdurl                             0.1.0
menuinst                          2.0.2
mistune                           2.0.4
mkl-fft                           1.3.8
mkl-random                        1.2.4
mkl-service                       2.4.0
more-itertools                    10.1.0
mpmath                            1.3.0
msgpack                           1.0.3
multidict                         6.0.4
multipledispatch                  0.6.0
munkres                           1.1.4
mypy                              1.8.0
mypy-extensions                   1.0.0
navigator-updater                 0.4.0
nbclient                          0.8.0
nbconvert                         7.10.0
nbformat                          5.9.2
nest-asyncio                      1.6.0
networkx                          3.1
nltk                              3.8.1
notebook                          7.0.8
notebook_shim                     0.2.3
numba                             0.59.0
numexpr                           2.8.7
numpy                             1.26.4
numpydoc                          1.5.0
openpyxl                          3.0.10
overrides                         7.4.0
packaging                         23.1
pandas                            2.1.4
pandocfilters                     1.5.0
panel                             1.3.8
param                             2.0.2
paramiko                          2.8.1
parsel                            1.8.1
parso                             0.8.3
partd                             1.4.1
pathlib                           1.0.1
pathspec                          0.10.3
patsy                             0.5.3
pexpect                           4.8.0
pickleshare                       0.7.5
pillow                            10.2.0
pip                               23.3.1
pkce                              1.0.3
pkginfo                           1.9.6
platformdirs                      3.10.0
plotly                            5.9.0
pluggy                            1.0.0
ply                               3.11
prometheus-client                 0.14.1
prompt-toolkit                    3.0.43
Protego                           0.1.16
protobuf                          3.20.3
psutil                            5.9.0
ptyprocess                        0.7.0
pure-eval                         0.2.2
py-cpuinfo                        9.0.0
pyarrow                           14.0.2
pyasn1                            0.4.8
pyasn1-modules                    0.2.8
pycodestyle                       2.10.0
pycosat                           0.6.6
pycparser                         2.21
pyct                              0.5.0
pycurl                            7.45.2
pydantic                          1.10.12
pydeck                            0.8.0
PyDispatcher                      2.0.5
pydocstyle                        6.3.0
pyerfa                            2.0.0
pyflakes                          3.0.1
Pygments                          2.15.1
PyJWT                             2.4.0
pylint                            2.16.2
pylint-venv                       2.3.0
pyls-spyder                       0.4.0
PyNaCl                            1.5.0
pyodbc                            5.0.1
pyOpenSSL                         24.0.0
pyparsing                         3.0.9
PyQt5                             5.15.10
PyQt5-sip                         12.13.0
PyQtWebEngine                     5.15.6
PySocks                           1.7.1
pytest                            7.4.0
python-dateutil                   2.8.2
python-dotenv                     0.21.0
python-json-logger                2.0.7
python-lsp-black                  1.2.1
python-lsp-jsonrpc                1.0.0
python-lsp-server                 1.7.2
python-slugify                    5.0.2
python-snappy                     0.6.1
pytoolconfig                      1.2.6
pytz                              2023.3.post1
pyviz_comms                       3.0.0
pywavelets                        1.5.0
pywin32                           305.1
pywin32-ctypes                    0.2.0
pywinpty                          2.0.10
PyYAML                            6.0.1
pyzmq                             25.1.2
QDarkStyle                        3.0.2
qstylizer                         0.2.2
QtAwesome                         1.2.2
qtconsole                         5.4.2
QtPy                              2.4.1
queuelib                          1.6.2
referencing                       0.30.2
regex                             2023.10.3
requests                          2.31.0
requests-file                     1.5.1
requests-toolbelt                 1.0.0
rfc3339-validator                 0.1.4
rfc3986-validator                 0.1.1
rich                              13.3.5
rope                              1.7.0
rpds-py                           0.10.6
Rtree                             1.0.1
ruamel.yaml                       0.17.21
ruamel-yaml-conda                 0.17.21
s3fs                              2023.10.0
scikit-image                      0.22.0
scikit-learn                      1.2.2
scipy                             1.11.4
Scrapy                            2.8.0
seaborn                           0.12.2
semver                            2.13.0
Send2Trash                        1.8.2
service-identity                  18.1.0
setuptools                        68.2.2
sip                               6.7.12
six                               1.16.0
smart-open                        5.2.1
smmap                             4.0.0
sniffio                           1.3.0
snowballstemmer                   2.2.0
sortedcontainers                  2.4.0
soupsieve                         2.5
Sphinx                            5.0.2
sphinxcontrib-applehelp           1.0.2
sphinxcontrib-devhelp             1.0.2
sphinxcontrib-htmlhelp            2.0.0
sphinxcontrib-jsmath              1.0.1
sphinxcontrib-qthelp              1.0.3
sphinxcontrib-serializinghtml     1.1.5
spyder                            5.4.3
spyder-kernels                    2.4.4
SQLAlchemy                        2.0.25
stack-data                        0.2.0
statsmodels                       0.14.0
streamlit                         1.30.0
sympy                             1.12
tables                            3.9.2
tabulate                          0.9.0
tblib                             1.7.0
tenacity                          8.2.2
terminado                         0.17.1
text-unidecode                    1.3
textdistance                      4.2.1
threadpoolctl                     3.1.0
three-merge                       0.1.1
tifffile                          2023.4.12
tinycss2                          1.2.1
tldextract                        3.2.0
toml                              0.10.2
tomlkit                           0.11.1
toolz                             0.12.0
tornado                           6.3.3
tqdm                              4.65.0
traitlets                         5.7.1
truststore                        0.8.0
Twisted                           23.10.0
twisted-iocpsupport               1.0.2
typing_extensions                 4.9.0
tzdata                            2023.3
tzlocal                           2.1
uc-micro-py                       1.0.1
ujson                             5.4.0
Unidecode                         1.2.0
urllib3                           2.0.7
validators                        0.18.2
w3lib                             2.1.2
watchdog                          2.1.6
wcwidth                           0.2.5
webencodings                      0.5.1
websocket-client                  0.58.0
Werkzeug                          2.2.3
whatthepatch                      1.0.2
wheel                             0.41.2
widgetsnbextension                3.5.2
win-inet-pton                     1.1.0
wrapt                             1.14.1
xarray                            2023.6.0
xlwings                           0.29.1
xyzservices                       2022.9.0
yapf                              0.31.0
yarl                              1.9.3
yellowbrick                       1.5
zict                              3.0.0
zipp                              3.17.0
zope.interface                    5.4.0
zstandard                         0.19.0

pip install seaborn --upgrade

Requirement already satisfied: seaborn in c:\users\user\anaconda3\lib\site-packages (0.12.2)
Collecting seaborn
  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)
Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\users\user\anaconda3\lib\site-packages (from seaborn) (1.26.4)
Requirement already satisfied: pandas>=1.2 in c:\users\user\anaconda3\lib\site-packages (from seaborn) (2.1.4)
Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\users\user\anaconda3\lib\site-packages (from seaborn) (3.8.0)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)
Requirement already satisfied: cycler>=0.10 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.25.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.4)
Requirement already satisfied: packaging>=20.0 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)
Requirement already satisfied: pillow>=6.2.0 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.2.0)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7 in c:\users\user\anaconda3\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\users\user\anaconda3\lib\site-packages (from pandas>=1.2->seaborn) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in c:\users\user\anaconda3\lib\site-packages (from pandas>=1.2->seaborn) (2023.3)
Requirement already satisfied: six>=1.5 in c:\users\user\anaconda3\lib\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)
Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)
   ---------------------------------------- 0.0/294.9 kB ? eta -:--:--
   - -------------------------------------- 10.2/294.9 kB ? eta -:--:--
   --------- ----------------------------- 71.7/294.9 kB 991.0 kB/s eta 0:00:01
   ---------------------------------------- 294.9/294.9 kB 3.1 MB/s eta 0:00:00
Installing collected packages: seaborn
  Attempting uninstall: seaborn
    Found existing installation: seaborn 0.12.2
    Uninstalling seaborn-0.12.2:
      Successfully uninstalled seaborn-0.12.2
Successfully installed seaborn-0.13.2
Note: you may need to restart the kernel to use updated packages.

# 각 컬럼의 상관관계를 시각화 ( heatmap 사용 )
# annot = True : 수치를 숫자로 나타내준다
# 수치가 0.7 이상이면 서로 깊은 연관관계가 있다
sns.heatmap(tips.corr(numeric_only=True), annot=True)

# seaborn 으로 heatmap 그리는데 annot=True 를 설정해도
# 맨 윗줄 한 줄만 숫자가 나타남
# seaborn 버전 0.12.2 에서 자주 발생하는 문제라 함
# 버전을 0.13.2 로 올린 뒤 우측 상단의 Python 3 (ipykernel) 부분
# 클릭해서 커널 다시 재실행 후 시작하니 정상 작동

<Axes: >

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

	total_bill	tip	sex	smoker	time	size
day
Thur	62	62	62	62	62	62
Fri	19	19	19	19	19	19
Sat	87	87	87	87	87	87
Sun	76	76	76	76	76	76

	total_bill								tip					size
	count	mean	std	min	25%	50%	75%	max	count	mean	...	75%	max	count	mean	std	min	25%	50%	75%	max
day
Thur	62.0	17.682742	7.886170	7.51	12.4425	16.20	20.1550	43.11	62.0	2.771452	...	3.3625	6.70	62.0	2.451613	1.066285	1.0	2.0	2.0	2.0	6.0
Fri	19.0	17.151579	8.302660	5.75	12.0950	15.38	21.7500	40.17	19.0	2.734737	...	3.3650	4.73	19.0	2.105263	0.567131	1.0	2.0	2.0	2.0	4.0
Sat	87.0	20.441379	9.480419	3.07	13.9050	18.24	24.7400	50.81	87.0	2.993103	...	3.3700	10.00	87.0	2.517241	0.819275	1.0	2.0	2.0	3.0	5.0
Sun	76.0	21.410000	8.832122	7.25	14.9875	19.63	25.5975	48.17	76.0	3.255132	...	4.0000	6.50	76.0	2.842105	1.007341	2.0	2.0	2.0	4.0	6.0

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

	survived	pclass	sex	age	sibsp	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

전영호의 개발 블로그

머신러닝 시각화

[머신러닝] 시각화 ( mataplotlib, seaborn )

시각화

EDA-탐색적 데이터 분석¶

시각화¶

시각화 차트 5가지¶

Matplotlib¶

figure¶

plt.subplots(nrows, ncols)¶

Seaborn ( https://seaborn.pydata.org/ )¶

Seaborn 그래프( https://seaborn.pydata.org/tutorial/function_overview.html )¶

'BE > 머신러닝(ML)' 카테고리의 다른 글

+ Recent posts

티스토리툴바

	total_bill	tip	size
total_bill	1.000000	0.675734	0.598315
tip	0.675734	1.000000	0.489299
size	0.598315	0.489299	1.000000

[머신러닝] 지도학습 ( 분류, 회귀 ), 평가지표 선택하는 방법 (0)	2024.05.24
[머신러닝] 탐색적 데이터분석 ( EDA, 표준화, 가중치 ) (0)	2024.05.24
[머신러닝] 실습 예제 및 풀이 (1)	2024.05.23
[머신러닝] 데이터 전처리 ( 그룹 ) (0)	2024.05.23
[머신러닝] 데이터 전처리 ( 시계열 ) (0)	2024.05.23