ckcd

matplotlib

- 단점이 많이 존재, 전설적인(?) plot

- 더 보기 편리한 시각화 해보기

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

- 기본

plt.plot([1,2,3,4],[2,3,4,3],'o--')

[<matplotlib.lines.Line2D at 0x7f762f8ab580>]

- 다른 테마

plt.style.available

['Solarize_Light2',
 '_classic_test_patch',
 'bmh',
 'classic',
 'dark_background',
 'fast',
 'fivethirtyeight',
 'ggplot',
 'grayscale',
 'seaborn',
 'seaborn-bright',
 'seaborn-colorblind',
 'seaborn-dark',
 'seaborn-dark-palette',
 'seaborn-darkgrid',
 'seaborn-deep',
 'seaborn-muted',
 'seaborn-notebook',
 'seaborn-paper',
 'seaborn-pastel',
 'seaborn-poster',
 'seaborn-talk',
 'seaborn-ticks',
 'seaborn-white',
 'seaborn-whitegrid',
 'tableau-colorblind10']

다양한 스타일 존재

with plt.style.context('Solarize_Light2'):
    plt.plot([1,2,3,4],[2,3,4,3],'o--')
plt.show()

with 쓰면 테마 여기서는 바뀌지만 기본은 바뀌지 않는 것 같음

lst = plt.style.available

for l in lst:
    with plt.style.context(l):
        plt.plot([1,2,3,4],[2,3,4,3],'o--')
    plt.title(l)
    plt.show()

다양한 테마 보기

seaborn-dark, xkcd로 12주차 강의노트 그리기

Pandas Backend

import

import numpy as np
import pandas as pd
import warnings
from IPython.display import HTML

from pandas_datareader import data as pdr

def show(fig): 
    return HTML(fig.to_html(include_plotlyjs='cdn',include_mathjax=False, config=dict({'scrollZoom':False})))

line

matploblib

symbols = ['AMZN','AAPL','GOOG','MSFT','NFLX','NVDA','TSLA']
start = '2020-01-01'
end = '2020-11-28'
df = pdr.get_data_yahoo(symbols,start,end)['Adj Close']

- 1개의 y를 그리기

matplotlib.pyplot.xkcd(scale=1,length=200)
- scale 조정에 따라 축의 ~이 달라지는 듯..!
- 0이면 거의 직선이 돼

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.reset_index().plot.line(x='Date',y='AMZN')

- 2개의 y를 겹쳐그리기

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.reset_index().plot.line(x='Date',y=['AMZN','TSLA'])

- 모든 y겹처그리기, 및 그림 크기 조정

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.reset_index().plot.line(x='Date',figsize=(8,8))

- 서브플랏, 레이아웃 조정, 폰트조정, 투명도 조정, 레전드 삭제

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.reset_index().plot.line(x='Date',figsize=(10,8),subplots=True,layout=(4,2),fontsize=6,alpha=0.3, legend=False)

bar, barh

matplotlib

df = pd.read_csv('https://raw.githubusercontent.com/kalilurrahman/datasets/main/mobilephonemktshare2020.csv')

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.plot.bar(x='Date',y=['Samsung','Huawei'],figsize=(10,5))

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.plot.bar(x='Date',y=['Samsung','Apple'],figsize=(10,5),width=0.8)

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.plot.barh(x='Date',y=['Huawei','Apple'],figsize=(5,10))

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=200)
    df.plot.bar(x='Date',figsize=(15,10),subplots=True,layout=(4,4),legend=False)

- 비율을 평균내는 것은 이상하지만 시각화예제를 위해서 제조사별로 평균점유율을 sorting 한 후 시각화하여보자.

with plt.style.context('seaborn-dark'):
    matplotlib.pyplot.xkcd(scale=0,length=100)
    df.melt(id_vars='Date').groupby('variable').agg(np.mean).sort_values('value',ascending=False).\
plot.bar(legend=False)

plotly

wide form 이 아닌 long form, 즉, tidy data일때 사용

fig= df.melt(id_vars='Date').groupby('variable').agg(np.mean).sort_values('value',ascending=False).\
plot.bar(backend='plotly')
show(fig)

fig=df.melt(id_vars='Date').\
plot.barh(y='Date',x='value',color='variable',backend='plotly',width=800,height=500)
show(fig)

fig=df.melt(id_vars='Date').query("variable=='Samsung' or variable=='Apple' or variable=='Huawei'" ).\
plot.bar(x='Date',y='value',color='variable',backend='plotly',barmode='group',text='value')
show(fig)

최근 trend

- mplcyberpunk 소개 및 사용법

symbols = ['AMZN','AAPL','GOOG','MSFT','NFLX','NVDA','TSLA']
start = '2020-01-01'
end = '2020-11-28'
df = pdr.get_data_yahoo(symbols,start,end)['Adj Close']

import mplcyberpunk

with plt.style.context('cyberpunk'):
    #matplotlib.pyplot.xkcd(scale=0,length=200)
    df.reset_index().plot.line(x='Date',y=['AMZN','GOOG'])
    mplcyberpunk.add_glow_effects() # 선 밝게
    mplcyberpunk.add_underglow() # 선 아래 번짐없이 뚜렷하게

- subplot에는 mplcyberpunk.add_glow_effects() 적용이 안 된다..

with plt.style.context('cyberpunk'):
    df.reset_index().plot.line(x='Date',figsize=(10,8),subplots=True,layout=(4,2),fontsize=6,alpha=0.3, legend=False)
    mplcyberpunk.add_glow_effects()
    mplcyberpunk.add_underglow()