[핀테커스] 231004 matplot 실습

import matplotlib.pyplot as plt
import numpy as np
figsize = (7, 7)

In [ ]:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=figsize)
ax.scatter(0, 0,
           s=10000,
           facecolor='red',
           edgecolor='b',
           linewidth=5)

Out[ ]:

<matplotlib.collections.PathCollection at 0x16be1ea00>

In [ ]:

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

n_data = 100
x_data = np.random.normal(0, 1, (n_data,))
y_data = np.random.normal(0, 1, (n_data,))

ax.scatter(x_data, y_data,
           s=100,
           facecolor='white', # 중첩 확인 불가
           edgecolor='tab:blue',
           linewidth=1)

Out[ ]:

<matplotlib.collections.PathCollection at 0x16bd01f10>

In [ ]:

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

n_data = 100
x_data = np.random.normal(0, 1, (n_data,))
y_data = np.random.normal(0, 1, (n_data,))

ax.scatter(x_data, y_data,
           s=100,
           facecolor='None',# 중첩 확인 가능
           edgecolor='tab:blue',
           linewidth=1)

Out[ ]:

<matplotlib.collections.PathCollection at 0x16bca3190>

In [ ]:

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

n_data = 100
x_data = np.random.normal(0, 1, (n_data,))
y_data = np.random.normal(0, 1, (n_data,))

ax.scatter(x_data, y_data,
           s=100,
           facecolor='None',
           edgecolor='tab:blue',
           linewidth=2,
           alpha=0.5) # 투명도
plt.show()

In [ ]:

n_data = 100
s_idx = 30
x_data = np.arange(s_idx, s_idx + n_data)
y_data = np.random.normal(0, 1, (n_data,))

fig, ax = plt.subplots(figsize=figsize)
ax.plot(x_data, y_data)

fig.tight_layout(pad=1) #pad : Padding between the figure edge and the edges of subplots. 여백
x_ticks = \
    np.arange(s_idx, s_idx + n_data + 1, step=29)
ax.set_xticks(x_ticks)

ax.tick_params(labelsize=15)
ax.grid()
plt.show()

In [ ]:

np.random.seed(404)

x_data = np.array([10, 25, 31, 40, 55, 80, 100])
y_data = np.random.normal(0, 1, (7, ))

fig, ax = plt.subplots(figsize=figsize)
ax.plot(x_data, y_data)

fig.subplots_adjust(left=0.2)
ax.tick_params(labelsize=10)

In [ ]:

ax.set_xticks(x_data)
ylim = ax.get_ylim() # The current y-axis limits in data coordinates.
# ylim에 y 데이터 좌표 limits 추출. 다시 num=8(개) 로 yticks 생성. set_yticks 로 재설정
yticks = np.linspace(ylim[0], ylim[1], 8)
ax.set_yticks(yticks)
ax.grid()
plt.show()

In [ ]:

PI = np.pi
t = np.linspace(-4*PI, 4*PI, 300)
sin = np.sin(t)
linear = 0.1*t

fig, ax = plt.subplots(figsize=figsize)
ax.plot(t, sin)
ax.plot(t, linear)

ax.set_ylim(-1.5, 1.5)

x_ticks = np.arange(-4*PI, 4*PI+0.1, PI)
x_ticklabels = [str(i) + r'$\pi$' for i in range(-4, 5)]

ax.set_xticks(x_ticks)
ax.set_xticklabels(x_ticklabels)

ax.tick_params(labelsize=10)
ax.grid()

In [ ]:

PI = np.pi
t = np.linspace(-4*PI, 4*PI, 1000)
sin = np.sin(t)
cos = np.cos(t)
tan = np.tan(t)

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

axes[0].plot(t, sin)
axes[1].plot(t, cos)
# axes[2].plot(t, tan)
# axes[2].set_ylim(-5, 5)

# tan 발산하는 그래프이다.
# 트릭을 사용하여 정상적으로 그리자
# y[:-1][np.diff(y) < 0] = np.nan # np.nan means NA. 값을 없애버린다.
tan[:-1][np.diff(tan) < 0] = np.nan
axes[2].plot(t, tan)
axes[2].set_ylim(-5, 5)

# grid()
for ax in axes:
    ax.grid()


fig.tight_layout()

 

In [ ]:

PI = np.pi
t = np.linspace(-4*PI, 4*PI, 1000).reshape(1, -1)
# 이번 예습은 reshape 안해도 결과가 나온다. 그래도 연습한다.
# reshape 의 -1은 컴터가 알아서 찾아준다.
# reshape(1, -1) : (1000, ) -> (1, 1000)
# reshape(-1, 1) : (1000, ) -> (1000, 1)
sin = np.sin(t) # shape -> (1, 1000)
cos = np.cos(t) # shape -> (1, 1000)
tan = np.tan(t) # shape -> (1, 1000)
# tan[:-1][np.diff(tan) < 0] = np.nan
data = np.vstack((sin, cos, tan)) # shape -> (3, 1000)

title_list = [r'$sin(t)$', r'$cos(t)$', r'$tan(t)$']
x_ticks = np.arange(-4*PI, 4*PI+PI, PI)
x_ticklabels = [str(i) + r'$\pi$' for i in range(-4, 5)]

fig, axes = plt.subplots(3, 1, 
                         figsize=figsize,
                         sharex=True)

# for ax_idx, ax in enumerate(axes.flat):
for ax_idx, ax in enumerate(axes):
    ax.plot(t.flatten(), data[ax_idx])
    ax.set_title(title_list[ax_idx], fontsize=10)
    ax.tick_params(labelsize=10)
    ax.grid()
    if ax_idx == 2:
        ax.set_ylim([-3, 3])

fig.subplots_adjust(left=0.1, right=0.95,
                    bottom=0.05, top=0.95)
axes[-1].set_xticks(x_ticks)
axes[-1].set_xticklabels(x_ticklabels)

Out[ ]:

[Text(-12.566370614359172, 0, '-4$\\pi$'),
 Text(-9.42477796076938, 0, '-3$\\pi$'),
 Text(-6.283185307179586, 0, '-2$\\pi$'),
 Text(-3.141592653589793, 0, '-1$\\pi$'),
 Text(0.0, 0, '0$\\pi$'),
 Text(3.141592653589793, 0, '1$\\pi$'),
 Text(6.283185307179586, 0, '2$\\pi$'),
 Text(9.42477796076938, 0, '3$\\pi$'),
 Text(12.566370614359172, 0, '4$\\pi$')]

In [ ]:

# 점근선
fig, ax = plt.subplots(figsize=figsize)

ax.set_xlim(-5, 5)
ax.set_ylim(-5, 5)

ax.axvline(x=1,
           color='black',
           linewidth=1)

Out[ ]:

<matplotlib.lines.Line2D at 0x16c6124f0>

In [ ]:

# 점근선
fig, ax = plt.subplots(figsize=figsize)

ax.set_xlim(-5, 5)
ax.set_ylim(-5, 5)

ax.axvline(x=1,
           ymax=0.8, ymin=0.2,
           color='black',
           linewidth=1)

Out[ ]:

<matplotlib.lines.Line2D at 0x16bd0a3d0>

In [ ]:

x = np.linspace(-4*np.pi, 4*np.pi, 200)
sin = np.sin(x)

fig, ax = plt.subplots(figsize=figsize)
ax.plot(x, sin)
ax.axhline(1, ls=':', lw=1, color='gray')
ax.axhline(-1, ls=':', lw=1, color='gray')

Out[ ]:

<matplotlib.lines.Line2D at 0x16c0b8dc0>

In [ ]:

np.random.seed(0)

n_data = 100
random_noise1 = np.random.normal(0, 1, (n_data, ))
random_noise2 = np.random.normal(1, 1, (n_data, ))
random_noise3 = np.random.normal(2, 1, (n_data, ))

fig, ax = plt.subplots(figsize=figsize)
ax.tick_params(labelsize=10)

ax.plot(random_noise1, label='random noise1')
ax.plot(random_noise2, label='random noise2')
ax.plot(random_noise3, label='random noise3')

# ax.legend(fontsize=5, loc='upper right')
ax.legend(fontsize=5, loc='center right')

Out[ ]:

<matplotlib.legend.Legend at 0x16c432400>

In [ ]:

# bbox_to_anchor
# If "loc", update the loc parameter of the legend upon finalizing. 
# If "bbox", update the bbox_to_anchor parameter. loc 보다 bbox 선적용 된다.
# bbox의 기준에서 loc가 적용된다.

np.random.seed(0)

n_data = 100
random_noise1 = np.random.normal(0, 1, (n_data, ))
random_noise2 = np.random.normal(1, 1, (n_data, ))
random_noise3 = np.random.normal(2, 1, (n_data, ))

fig, ax = plt.subplots(figsize=figsize)
ax.tick_params(labelsize=10)

ax.plot(random_noise1, label='random noise1')
ax.plot(random_noise2, label='random noise2')
ax.plot(random_noise3, label='random noise3')

ax.legend(fontsize=8, bbox_to_anchor=(1, 0.5) ,loc='center left')
fig.tight_layout()

In [ ]:

PI = np.pi
t = np.linspace(-4*PI, 4*PI, 300)
sin = np.sin(t)

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

for ax_idx in range(12):
    label_template = 'added by {}' # == f'added by {ax_idx}'
    ax.plot(t, sin+ax_idx,
            label=label_template.format(ax_idx))
    
ax.legend(fontsize=7,
          ncol=4,
          bbox_to_anchor=(0.5, -0.1),
          loc='upper center')
fig.tight_layout()

In [ ]:

np.random.seed(0)

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

data1 = np.random.normal(0, 1, 100)
data2 = np.random.normal(5, 2, 200)
data3 = np.random.normal(13, 3, 300)

xticks = np.arange(3)

violin = ax.violinplot([data1, data2, data3], 
              showmeans=True, # 평균. 바이올린의 중간에 표시
              quantiles=[[0.25, 0.75], [0.1, 0.9], [0.3, 0.7]], # 지점 표시
              positions=xticks)

ax.set_xticks(xticks)
ax.set_xticklabels(['setosa', 'versicolor', 'virginica'])
ax.set_xlabel('Species', fontsize=10)
ax.set_ylabel('Values', fontsize=10)

print(violin.keys()) # dict_keys(['bodies', 'cmeans', 'cmaxes', 'cmins', 'cbars', 'cquantiles'])

violin['bodies'][0].set_facecolor('blue')
violin['bodies'][1].set_facecolor('red')
violin['bodies'][2].set_facecolor('green')

violin['cbars'].set_edgecolor('gray')
violin['cmaxes'].set_edgecolor('gray')
violin['cmins'].set_edgecolor('gray')
violin['cmeans'].set_edgecolor('gray')

plt.show()

dict_keys(['bodies', 'cmeans', 'cmaxes', 'cmins', 'cbars', 'cquantiles'])
<matplotlib.collections.PolyCollection object at 0x16c1c8c40>