plt.fill_between( x, y, color="skyblue", alpha=0.2)
plt.plot(x, y, color="Slateblue", alpha=0.6)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x=range(1,15)
y=[1,4,6,8,4,5,3,2,4,1,5,6,8,7]
plt.fill_between( x, y, color="skyblue", alpha=0.3)
plt.plot(x, y, color="skyblue")
plt.title("An area chart", loc="left")
plt.xlabel("Value of X")
plt.ylabel("Value of Y")
plt.show()
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
# 创建数据
my_count=["France","Australia","Japan","USA","Germany","Congo","China","England","Spain","Greece","Marocco","South Africa","Indonesia","Peru","Chili","Brazil"]
df = pd.DataFrame({
"country":np.repeat(my_count, 10),
"years":list(range(2000, 2010)) * 16,
"value":np.random.rand(160)
# 创建一个网格并初始化
g = sns.FacetGrid(df, col='country', hue='country', col_wrap=4, )
# 添加线
g = g.map(plt.plot, 'years', 'value')
# 填充区域
g = g.map(plt.fill_between, 'years', 'value', alpha=0.2).set_titles("{col_name} country")
# 控制标题
g = g.set_titles("{col_name}")
# 添加标题
plt.subplots_adjust(top=0.92)
g = g.fig.suptitle('Evolution of the value of stuff in 16 countries')
plt.show()
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("whitegrid")
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
blue, = sns.color_palette("muted", 1)
x = np.arange(23)
y = np.random.randint(8, 20, 23)
fig, ax = plt.subplots()
ax.plot(x, y, color=blue, lw=3)
ax.fill_between(x, 0, y, alpha=.3)
ax.set(xlim=(0, len(x) - 1), ylim=(0, None), xticks=x)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x=range(1,6)
y=[ [1,4,6,8,9], [2,2,7,10,12], [2,8,5,10,6] ]
plt.stackplot(x,y, labels=['A','B','C'])
plt.legend(loc='upper left')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x=range(1,6)
y1=[1,4,6,8,9]
y2=[2,2,7,10,12]
y3=[2,8,5,10,6]
plt.stackplot(x,y1, y2, y3, labels=['A','B','C'])
plt.legend(loc='upper left')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
X = np.arange(0, 10, 1)
Y = X + 5 * np.random.random((5, X.size))
baseline = ["zero", "sym", "wiggle", "weighted_wiggle"]
# 画四个图
for n, v in enumerate(baseline):
if n<3 :
plt.tick_params(labelbottom='off')
plt.subplot(2 ,2, n + 1)
plt.stackplot(X, *Y, baseline=v)
plt.title(v)
plt.axis('tight', size=0.2)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x=range(1,6)
y=[ [10,4,6,5,3], [12,2,7,10,1], [8,18,5,7,6] ]
pal = sns.color_palette("Set1")
plt.stackplot(x,y, labels=['A','B','C'], colors=pal, alpha=0.4 )
plt.legend(loc='upper right')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x=range(1,6)
y=[ [10,4,6,5,3], [12,2,7,10,1], [8,18,5,7,6] ]
pal = ["#9b59b6", "#e74c3c", "#34495e", "#2ecc71"]
plt.stackplot(x,y, labels=['A','B','C'], colors=pal, alpha=0.4 )
plt.legend(loc='upper right')
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.plot.area();
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
data = pd.DataFrame({
'group_A':[1,4,6,8,9],
'group_B':[2,24,7,10,12],
'group_C':[2,8,5,10,6],
}, index=range(1,6))
# 将原始数据转换成百分比
data_perc = data.divide(data.sum(axis=1), axis=0)
plt.stackplot(range(1,6), data_perc["group_A"], data_perc["group_B"], data_perc["group_C"], labels=['A','B','C'])
plt.legend(loc='upper left')
plt.margins(0,0)
plt.title('100 % stacked area chart')
plt.show()
本博主新开公众号, 希望大家能扫码关注一下,十分感谢大家。
本文来自:
https://github.com/holtzy/The-Python-Graph-Gallery/blob/master/PGG_notebook.py
# original为True代表优先下载原图
net, num, urls = Crawler
.
get_images_url('二次元', 20, original=True)
Crawle
.
.
.
python
(matplotlib)划分子区域
在使用
python
绘制图形的时候,我们可能会在同一个区域绘制多个不同的图形,这个时候就需要使用多区域绘制图形,具体使用案例如下所示:
1
.
利用matplotlib库文件,
画
出如下子图的图形。
import matplotlib
.
pyplot as
plt
#包含对应的库
ax1 =
plt
.
subplot2grid((3,3), (0,0), colspan=3)
ax2 =
plt
.
subplot2grid((3,3), (1,0), colspan=2)
在使用matplotlib
画
图时,少不了对性能图形做出一些说明和补充。一般情况下,loc属性设置为’best’就足够应付了
plt
.
legend
(handles = [l1, l2,], labels = [‘a’, ‘b’], loc = ‘best’)
或直接loc = 0
plt
.
legend
(handles = [l1, l2,], labels = [‘a’, ‘b’], loc = 0)
除’best’,另外loc属性有:
‘upper right’, ‘upper left’, ‘lower left’, ‘lower right’, ‘right’, ‘center
原文链接https://blog
.
csdn
.
net/helunqu2017/article/details/78641290,感谢作者辛勤付出,仅作笔记使用,侵删
1
.
图例
legend
基础语法及用法
legend
语法参数如下: matplotlib
.
pyplot
.
legend
(*args, **kwargs)
Keyword
Description
Lo
.
.
.
sns
.
set(font_scale=1
.
5)
#修改默认设置
mpl
.
rcParams["font
.
family"] = 'Times New Roman' #默认字体类型
mpl
.
rcParams["mathtext
.
fontset"] = 'cm' #数学文字字体
mpl
.
rcParams["font
.
s
在
Python
中,可以使用matplotlib库中的subplot
()
函数来创建多子图地图,这通常用于在一个大的网格区域内绘制多个相关的图表。例如,如果你想要在同一张图上显示地理区域的数据分布,你可以这样做:
```
python
import matplotlib
.
pyplot as
plt
import numpy as np
# 假设你有数据,比如各个城市的气温
city_temps = {
'北京': [10, 15, 20, 25],
'上海': [8, 12, 16, 20],
'广州': [22, 27, 32, 35]
# 创建一个新的图形窗口
fig, axs =
plt
.
subplots(nrows=2, ncols=2)
# 绘制每个子图
for i, (city, temps) in enumerate(city_temps
.
items
()
):
row, col = divmod(i, 2)
ax = axs[row][col]
ax
.
set_title(city)
ax
.
plot(temps)
# 调整子图之间的间距
plt
.
tight_layout
()
# 显示地图
plt
.
show
()
在这个例子中,`subplots
()
`函数创建了一个2x2的网格布局,然后遍历城市及其对应的气温数据,在每个子图上绘制折线图。每个子图都有相应的标题,并通过索引来关联到正确的子图。
plant_genome:
【学习记录】Yolox检测踩坑记录
老师靠不住:
【学习记录】Yolox检测踩坑记录
舒肤佳好难啊: