3D图形

线形图&散点图

import numpy as np
import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d.axes3d import Axes3D

x = np.linspace(0,60,300)
y = np.sin(x)
z = np.cos(x)

fig = plt.figure(figsize=(9,6))
a3 = Axes3D(fig) # 二维变成3D
a3.plot(x,y,z)

plt.figure(figsize=(9,6))
# projection='3d' 指定了这个子图的投影类型为3D
a3 = plt.subplot(111, projection = '3d')
a3.plot(x,y,z) # 普通线形图
a3.set_xlabel('X')
a3.set_ylabel('Y')
a3.set_zlabel('Z')

# 散点图
x = np.random.randint(0,60,size = 20)
y = np.random.randn(20)
z = np.random.randn(20)
a3.scatter(x,y,z,color= 'red')

# 调整视图的角度
# elev=20示观察者向上仰角 20 度看图
# azim=-30 观察者沿着 z 轴逆时针旋转 30 度来观察图形
a3.view_init(elev = 20,azim=-30)

3D条形图

month = np.arange(1,5)
# 每个月 4周 每周都会产生数据
# 三个维度：月、周、销量
fig = plt.figure(figsize=(9,6))
ax3 = Axes3D(fig)

for m in month:
    # 每个月都要绘制条形图
    ax3.bar(np.arange(1,5), # 理解成横坐标
            np.random.randint(1,10,size = 4), # 纵坐标
            zs = m ,
            zdir = 'x',# 在哪个方向上，一排排排列
            alpha = 0.7,# alpha 透明度
            width = 0.5)

ax3.set_xlabel('Month',fontsize = 18,color = 'red')
ax3.set_xticks(month)
ax3.set_ylabel('Week',fontsize = 18,color = 'red')
ax3.set_yticks([1,2,3,4])
ax3.set_zlabel('Sales',fontsize = 18,color = 'green')

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 创建一个网格，其中 x 方向和 y 方向都是 [0, 1, 2, 3, 4]
x, y = np.meshgrid(np.arange(5), np.arange(5))
x = x.flatten()
y = y.flatten()
z = np.zeros_like(x)
dx = dy = 0.3
dz = np.random.randint(1, 10, len(z))

# 绘制3D条形图
ax.bar3d(x, y, z, dx, dy, dz, shade=True)

# 设置坐标轴标签
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

plt.show()

seaborn 快速入门

import seaborn as sns

# style='ticks' 坐标轴显示刻度线
# context='paper': 指定绘图的上下文环境, 从而调整图形的大小和比例
sns.set(style = 'ticks',context = 'paper',font = 'STKaiti') # 设置样式

plt.figure(figsize=(9,6))

x = np.linspace(0,2*np.pi,20)
y = np.sin(x)

# lineplot方法，画一条线
sns.lineplot(x = x, y = y, color = 'green', ls = '--')
sns.lineplot(x = x, y = np.cos(x), color = 'red', ls = '-.')

线形图

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
sns.set(style = 'dark',context = 'notebook',font = 'SimHei') # 设置样式
plt.figure(figsize=(9,6))

# 加载数据
fmri = pd.read_csv('./fmri.csv') # fmri这一核磁共振数据

fmri['event'].unique() # array(['stim', 'cue'], dtype=object)
fmri.head()

	subject	timepoint	event	region	signal
0	s13	18	stim	parietal	-0.017552
1	s5	14	stim	parietal	-0.080883
2	s12	18	stim	parietal	-0.081033
3	s11	18	stim	parietal	-0.046134
4	s10	18	stim	parietal	-0.037970

ax = sns.lineplot(x = 'timepoint',y = 'signal',
                  hue = 'event',# 根据event属性分类，绘制
                  style = 'event' ,# 根据event属性分类，指定样式
                  data= fmri,
                  palette='ocean', # 画板，颜色
                  markers=True,
                  markersize = 10)

plt.xlabel('时间节点',fontsize = 30)

热力图

flights = pd.read_csv('./flights.csv') # 飞行数据
flights

	year	month	passengers
0	1949	January	112
1	1949	February	118
2	1949	March	132
3	1949	April	129
4	1949	May	121
...	...	...	...
139	1960	August	606
140	1960	September	508
141	1960	October	461
142	1960	November	390
143	1960	December	432

144 rows × 3 columns

# !!! pivot数据重塑，改变DataFrame形状
flights = flights.pivot("month", "year", "passengers") # 年，月份，飞机乘客数量
flights

year	1949	1950	1951	1952	1953	1954	1955	1956	1957	1958	1959	1960
month
April	129	135	163	181	235	227	269	313	348	348	396	461
August	148	170	199	242	272	293	347	405	467	505	559	606
December	118	140	166	194	201	229	278	306	336	337	405	432
February	118	126	150	180	196	188	233	277	301	318	342	391
January	112	115	145	171	196	204	242	284	315	340	360	417
July	148	170	199	230	264	302	364	413	465	491	548	622
June	135	149	178	218	243	264	315	374	422	435	472	535
March	132	141	178	193	236	235	267	317	356	362	406	419
May	121	125	172	183	229	234	270	318	355	363	420	472
November	104	114	146	172	180	203	237	271	305	310	362	390
October	119	133	162	191	211	229	274	306	347	359	407	461
September	136	158	184	209	237	259	312	355	404	404	463	508

plt.figure(figsize=(12,9))
sns.heatmap(flights, annot=True,fmt = 'd',cmap = 'RdBu_r',linewidths=5)

条形图

tips = pd.read_csv('./tips.csv') # 小费
tips.head(10)

	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
5	25.29	4.71	Male	No	Sun	Dinner	4
6	8.77	2.00	Male	No	Sun	Dinner	2
7	26.88	3.12	Male	No	Sun	Dinner	4
8	15.04	1.96	Male	No	Sun	Dinner	2
9	14.78	3.23	Male	No	Sun	Dinner	2

plt.figure(figsize = (9,6))
sns.set(style = 'whitegrid')

#  palette='viridis' 使用序列调色板
# orient='h 设置水平方向
ax = sns.barplot(x = "total_bill", y = "day", 
                 data = tips,
                 palette='viridis', orient='h')