(Data Viz) Line Plot 실습

이 색깔은 주석이라 무시하셔도 됩니다.

1. 기본 Line Plot

!pip install matplotlib==3.3.2

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

```
'3.3.2'
```

1-1. plot() 기본

• plot() : line()이 아님
• plot은 $x_1, x_2, \cdots$, $y_1, y_2, \cdots$ 데이터를 사용해서 그린다.
• line plot은 왼쪽에서 오른쪽으로 그리는 게 일반적이지만 문법 자체는 이전 점 $(x_1, y_1)$에서 $(x_2, y_2)$로 잇고, $(x_2, y_2)$에서 $(x_3, y_3)$로 잇는 순차적인 선으로 구성된 그래프이다.

fig, axes = plt.subplots(1, 2, figsize=(12, 7))

x1 = [1, 2, 3, 4, 5]
x2 = [1, 3, 2, 4, 5]
y = [1, 3, 2, 1, 5]

axes[0].plot(x1, y)
axes[1].plot(x2, y)

plt.show()

• 원 그리기

fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111, aspect=1)

n = 1000
x = np.sin(np.linspace(0, 2*np.pi, n))
y = np.cos(np.linspace(0, 2*np.pi, n))

ax.plot(x, y)

plt.show()

1-2 plot 변형하기

• 랜덤한 데이터로 그래프 그리기

fig, ax = plt.subplots(1, 1, figsize=(5, 5))

np.random.seed(97)
x = np.arange(7)
y = np.random.rand(7)

ax.plot(x, y)

plt.show()

• 3가지 요소 변경하기
  • 색(color)
  • 마커(marker) : 마커의 종류
  • 선의 종류(linestyle) : solid, dashed, dashdot, dotted, None, ...

fig, ax = plt.subplots(1, 1, figsize=(5, 5))

np.random.seed(97)
x = np.arange(7)
y = np.random.rand(7)

ax.plot(x, y,
        color='black',
        marker='*', # 'o', '.', '^'
        linestyle='solid', # '--', 'dashed', '-.', 'dashdot', ':', 'dotted'
       )

plt.show()

1-3. Line plot을 위한 전처리

• 데이터 : https://www.kaggle.com/dgawlik/nyse (미국 주식 데이터셋)

stock = pd.read_csv('./prices.csv')
stock
stock['date'] = pd.to_datetime(stock['date'], format='%Y-%m-%d', errors='raise')
stock.set_index("date", inplace = True)
stock

# FAANG
apple = stock[stock['symbol']=='AAPL']
google = stock[stock['symbol']=='GOOGL']
google.head()

fig, ax = plt.subplots(1, 1, figsize=(15, 7), dpi=300)

ax.plot(google.index, google['close'])
ax.plot(apple.index, apple['close'])

plt.show()

• 이동평균 그리기

google_rolling = google.rolling(window=20).mean()
google_rolling.head(7)

fig, axes = plt.subplots(2, 1, figsize=(12, 7), dpi=300, sharex=True)

axes[0].plot(google.index,google['close'])
axes[1].plot(google_rolling.index,google_rolling['close'])

plt.show()

2. 정확한 Line Plot

2-1. 추세에 집중

from matplotlib.ticker import MultipleLocator

fig = plt.figure(figsize=(12, 5))


np.random.seed(970725)

x = np.arange(20)
y = np.random.rand(20)


# Ax1
ax1 = fig.add_subplot(121)
ax1.plot(x, y,
         marker='o',
         linewidth=2)

ax1.xaxis.set_major_locator(MultipleLocator(1))
ax1.yaxis.set_major_locator(MultipleLocator(0.05))    
ax1.grid(linewidth=0.3)    


# Ax2
ax2 = fig.add_subplot(122)
ax2.plot(x, y,
       linewidth=2,)

ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)




ax1.set_title(f"Line Plot (information)", loc='left', fontsize=12, va= 'bottom', fontweight='semibold')
ax2.set_title(f"Line Plot (clean)", loc='left', fontsize=12, va= 'bottom', fontweight='semibold')


plt.show()

2-2. 간격

x = [2, 3, 5, 7, 9]
y = [2, 3, 4, 7, 10]

fig, ax = plt.subplots(1, 3, figsize=(13, 4))
ax[0].plot([str(i) for i in x], y)
ax[1].plot(x, y)
ax[2].plot(x, y, marker='o')

plt.show()

2-3. 보간

• https://www.delftstack.com/howto/matplotlib/matplotlib-plot-smooth-curve/

from scipy.interpolate import make_interp_spline, interp1d
import matplotlib.dates as dates

fig, ax = plt.subplots(1, 2, figsize=(20, 7), dpi=300)

date_np = google.index
value_np = google['close']

date_num = dates.date2num(date_np)

# smooth
date_num_smooth = np.linspace(date_num.min(), date_num.max(), 50) 
spl = make_interp_spline(date_num, value_np, k=3)
value_np_smooth = spl(date_num_smooth)

# print
ax[0].plot(date_np, value_np)
ax[1].plot(dates.num2date(date_num_smooth), value_np_smooth)

plt.show()

2-4. 이중 축 사용

• twinx() 사용

fig, ax1 = plt.subplots(figsize=(12, 7), dpi=150)

# First Plot
color = 'royalblue'

ax1.plot(google.index, google['close'], color=color)
ax1.set_xlabel('date')
ax1.set_ylabel('close price', color=color)  
ax1.tick_params(axis='y', labelcolor=color)

# # Second Plot
ax2 = ax1.twinx()  
color = 'tomato'

ax2.plot(google.index, google['volume'], color=color)
ax2.set_ylabel('volume', color=color)  
ax2.tick_params(axis='y', labelcolor=color)

ax1.set_title('Google Close Price & Volume', loc='left', fontsize=15)
plt.show()

• secondary-xaxis() : https://matplotlib.org/stable/gallery/subplots_axes_and_figures/secondary_axis.html

def deg2rad(x):
    return x * np.pi / 180

def rad2deg(x):
    return x * 180 / np.pi

fig, ax = plt.subplots()
x = np.arange(0, 360)
y = np.sin(2 * x * np.pi / 180)
ax.plot(x, y)
ax.set_xlabel('angle [degrees]')
ax.set_ylabel('signal')
ax.set_title('Sine wave')
secax = ax.secondary_xaxis('top', functions=(deg2rad, rad2deg))
secax.set_xlabel('angle [rad]')
plt.show()

2-5. ETC

fig = plt.figure(figsize=(12, 5))

x = np.linspace(0, 2*np.pi, 1000)
y1 = np.sin(x)
y2 = np.cos(x)


ax = fig.add_subplot(111, aspect=1)
ax.plot(x, y1,
       color='#1ABDE9',
       linewidth=2, label='sin')

ax.plot(x, y2,
       color='#F36E8E',
       linewidth=2, label='cos')

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

ax.legend(loc='upper center')

plt.show()

fig = plt.figure(figsize=(12, 5))

x = np.linspace(0, 2*np.pi, 1000)
y1 = np.sin(x)
y2 = np.cos(x)

# Ax2
ax = fig.add_subplot(111, aspect=1)
ax.plot(x, y1,
       color='#1ABDE9',
       linewidth=2,)

ax.plot(x, y2,
       color='#F36E8E',
       linewidth=2,)

ax.text(x[-1]+0.1, y1[-1], s='sin', fontweight='bold',
         va='center', ha='left', 
         bbox=dict(boxstyle='round,pad=0.3', fc='#1ABDE9', ec='black', alpha=0.3))

ax.text(x[-1]+0.1, y2[-1], s='cos', fontweight='bold',
         va='center', ha='left', 
         bbox=dict(boxstyle='round,pad=0.3', fc='#F36E8E', ec='black', alpha=0.3))


ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.show()

fig = plt.figure(figsize=(7, 7))

np.random.seed(97)

x = np.arange(20)
y = np.random.rand(20)

ax = fig.add_subplot(111)
ax.plot(x, y,
       color='lightgray',
       linewidth=2,)

ax.set_xlim(-1, 21)

# max
ax.plot([-1, x[np.argmax(y)]], [np.max(y)]*2,
        linestyle='--', color='tomato'
       )

ax.scatter(x[np.argmax(y)], np.max(y), 
            c='tomato',s=50, zorder=20)

# min
ax.plot([-1, x[np.argmin(y)]], [np.min(y)]*2,
        linestyle='--', color='royalblue'
       )
ax.scatter(x[np.argmin(y)], np.min(y), 
            c='royalblue',s=50, zorder=20)

plt.show()

출처: 부스트캠프 AI Tech 4기(NAVER Connect Foundation)