• df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"]) # 计算每对变量之间的相关性 corr_matrix=df.corr() # 半个矩阵 mask = np.zeros_like(corr_matrix) mask[np.triu_indices_from...
import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
from scipy.stats import kde

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x = np.random.normal(size=500)
y = x * 3 + np.random.normal(size=500)

nbins=300
k = kde.gaussian_kde([x,y])
xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
plt.pcolormesh(xi, yi, zi.reshape(xi.shape))
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
from scipy.stats import kde

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x = np.random.normal(size=500)
y = x * 3 + np.random.normal(size=500)

nbins=300
k = kde.gaussian_kde([x,y])
xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
plt.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap=plt.cm.Greens_r)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
from scipy.stats import kde

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
x = np.random.normal(size=500)
y = x * 3 + np.random.normal(size=500)

nbins=300
k = kde.gaussian_kde([x,y])
xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
plt.pcolormesh(xi, yi, zi.reshape(xi.shape), cmap=plt.cm.Greens_r)
plt.colorbar()
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((5,5)), columns=["a","b","c","d","e"])
p1 = sns.heatmap(df)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])
# 计算每对变量之间的相关性
corr_matrix=df.corr()
p1 = sns.heatmap(corr_matrix, cmap='PuOr')
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.random((100,5)), columns=["a","b","c","d","e"])
# 计算每对变量之间的相关性
corr_matrix=df.corr()
# 画半个矩阵
with sns.axes_style("white"):
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

people=np.repeat(("A","B","C","D","E"),5)
feature=list(range(1,6))*5
value=np.random.random(25)
df=pd.DataFrame({'feature': feature, 'people': people, 'value': value })
df_wide=df.pivot_table( index='people', columns='feature', values='value' )
p2=sns.heatmap( df_wide )
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p1 = sns.heatmap(df, linewidths=2, linecolor='yellow')
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p2 = sns.heatmap(df, annot=True, annot_kws={"size": 7})
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p3 = sns.heatmap(df, cbar=False)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p4 = sns.heatmap(df, yticklabels=False)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p5 = sns.heatmap(df, xticklabels=4)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p1 = sns.heatmap(df, cmap="YlGnBu")
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p1 = sns.heatmap(df, cmap="Blues")
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p1 = sns.heatmap(df, vmin=0, vmax=0.5)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
p1 = sns.heatmap(df, vmin=0.5, vmax=0.7)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.randn(6, 6))

# 离散分布
df_q = pd.DataFrame()
for col in df:
df_q[col] = pd.to_numeric( pd.qcut(df[col], 3, labels=list(range(3))) )

p1 = sns.heatmap(df_q)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = np.random.randn(30, 30)
p1 = sns.heatmap(df, cmap="PiYG")
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = np.random.randn(30, 30)
p1 = sns.heatmap(df, center=1)
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
df[1]=df[1]+40
p1 = sns.heatmap(df, cmap='viridis')
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
df[1]=df[1]+40

df_norm_col=(df-df.mean())/df.std()
p2 = sns.heatmap(df_norm_col, cmap='viridis')
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
df.iloc[2]=df.iloc[2]+40

p3 = sns.heatmap(df, cmap='viridis')
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
np.random.seed(0)

df = pd.DataFrame(np.random.randn(10,10) * 4 + 3)
df.iloc[2]=df.iloc[2]+40
# 减去均值
df_norm_row=df.sub(df.mean(axis=1), axis=0)

df_norm_row=df_norm_row.div( df.std(axis=1), axis=0 )

p4 = sns.heatmap(df_norm_row, cmap='viridis')
plt.show()

import matplotlib.pylab as plt
import seaborn as sns
import numpy as np
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D

my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)

# 获得网页数据

sns.heatmap(data, cmap="viridis")
# 坐标轴和标题
plt.tick_params(labelbottom='off', labelleft='off')
plt.xlabel('Latitude')
plt.ylabel('Longitude')
plt.title('Altitude on the volcano area', loc='left' )
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  
展开全文
• im = plt.imshow(data, cmap=plt.cm.jet) plt.colorbar(im) plt.show() 最后热图的排序和data排序一致
im = plt.imshow(data, cmap=plt.cm.jet)
plt.colorbar(im)
plt.show()

最后热图的排序和data排序一致
展开全文
• 本文实例为大家分享了python绘制热力图的具体代码，供大家参考，具体内容如下 python的热力图是用皮尔逊相关系数来查看两者之间的关联性。 #encoding:utf-8 import numpy as np import pandas as pd from ...
• ## Python-Seaborn热图绘制

万次阅读 多人点赞 2017-11-19 11:08:20
python-3.6 Seaborn-0.8热图import numpy as np import seaborn as sns import matplotlib.pyplot as plt sns.set() np.random.seed(0) uniform_data = np.random.rand(10, 12) ax = sns.heatmap(unif
制图环境：  pycharm  python-3.6  Seaborn-0.8
热图
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set()
np.random.seed(0)
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data)
plt.show()


# 改变颜色映射的值范围
ax = sns.heatmap(uniform_data, vmin=0, vmax=1)
plt.show()

uniform_data = np.random.randn(10, 12)
#为以0为中心的数据绘制一张热图
ax = sns.heatmap(uniform_data, center=0)
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
#用行和列标签绘制
flights = flights_long.pivot("month", "year", "passengers")
# 绘制x-y-z的热力图，比如 年-月-销量 的热力图
f, ax = plt.subplots(figsize=(9, 6))
sns.heatmap(flights, ax=ax)
#设置坐标字体方向
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360, horizontalalignment='right')
label_x = ax.get_xticklabels()
plt.setp(label_x, rotation=45, horizontalalignment='right')
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
flights = flights_long.pivot("month", "year", "passengers")
# 绘制x-y-z的热力图，比如 年-月-销量 的热力图
f, ax = plt.subplots(figsize=(9, 6))
#使用不同的颜色
sns.heatmap(flights, fmt="d",cmap='YlGnBu', ax=ax)
#设置坐标字体方向
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360, horizontalalignment='right')
label_x = ax.get_xticklabels()
plt.setp(label_x, rotation=45, horizontalalignment='right')
plt.show()


注释热图
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
flights = flights_long.pivot("month", "year", "passengers")
# 绘制x-y-z的热力图，比如 年-月-销量 的热力图
f, ax = plt.subplots(figsize=(9, 6))
#绘制热力图，还要将数值写到热力图上
sns.heatmap(flights, annot=True, fmt="d", ax=ax)
#设置坐标字体方向
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360, horizontalalignment='right')
label_x = ax.get_xticklabels()
plt.setp(label_x, rotation=45, horizontalalignment='right')
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
flights = flights_long.pivot("month", "year", "passengers")
# 绘制x-y-z的热力图，比如 年-月-销量 的热力图
f, ax = plt.subplots(figsize=(9, 6))
#绘制热力图，还要将数值写到热力图上
#每个网格上用线隔开
sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)
#设置坐标字体方向
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360, horizontalalignment='right')
label_x = ax.get_xticklabels()
plt.setp(label_x, rotation=45, horizontalalignment='right')
plt.show()


聚类热图
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
flights = flights_long.pivot("month", "year", "passengers")
# 绘制x-y-z的热力图，比如 年-月-销量 的聚类热图
g= sns.clustermap(flights, fmt="d",cmap='YlGnBu')
ax = g.ax_heatmap
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360, horizontalalignment='left')
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
species = iris.pop("species")
#设置图片大小
g= sns.clustermap(iris, fmt="d",cmap='YlGnBu',figsize=(6,9))
ax = g.ax_heatmap
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=360, horizontalalignment='left')
#设置图片名称，分辨率，并保存
plt.savefig('cluster.tif',dpi = 300)
plt.show()


注：更多参数的用法请查阅官方文档
展开全文
• 最近要处理的数据需要更直观的显示出来，也就是需要数据的可视化操作，需要用到pyheatmap，具体安装，直接pip install pyheatmap 即可 ...Python-Seaborn热图绘制 Python可视化：Seaborn库热力图使用进阶...
最近要处理的数据需要更直观的显示出来，也就是需要数据的可视化操作，需要用到pyheatmap，具体安装，直接pip install pyheatmap 即可
由于我要绘制的数据是二维的，行是样本，列是代谢物，或者pathway 也就是不同的特征，绘制heatmap时 参考了下面的资料，觉得很有用呀，嘻嘻  Python-Seaborn热图绘制
Python可视化：Seaborn库热力图使用进阶
相关系数矩阵与热力图heatmap(Python高级可视化库seaborn)
上手也很快，主要是简单，有时间再来详细介绍功能
展开全文
• 有时候图像需要用热图也就是heatmap来进行可视化下面是我的代码 # coding=utf-8 import numpy as np from PIL import Image import matplotlib.pyplot as plt import urllib from pyheatmap.heatmap import HeatMap...
• 这是一个生成热图的小程序，基于 Python 和 PIL 开发。 程序截图： 点击图 热图 安装： 通过 pip 安装： pip install pyheatmap 通过 easy_install 安装： easy_install pyheatmap 通过源码安装： ...
• from matplotlib import pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable import numpy as np x1 = np.arange(-1, 0, 0.01) x2 = np.arange(-1, 1, 0.01) x3 = np.arange(0, 1, 0.01) ...
• ## 用python画美国地图

千次阅读 2020-04-24 09:47:29
• ## 一、python可视化——热力图

万次阅读 多人点赞 2018-04-04 10:41:57
:设置作图的坐标轴，一般多个子图时需要修改不同的子图的该值 **kwargs :All other keyword arguments are passed to ax.pcolormesh   热力图矩阵块颜色参数 #cmap(颜色) import matplotlib.pyplot ...
• 我们在做诸如人群密集度等可视化的时候，可能会考虑使用热力图，在Python中能很方便地绘制热力图。 下面以识别图片中的行人，并绘制热力图为例进行讲解。 步骤1：首先识别图像中的人，得到bounding box的中心坐标。...
• ## Python画散点图

千次阅读 2020-07-19 18:12:42
import matplotlib.pyplot as plt import numpy as np import pandas as pd my_dpi=96 plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi) df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100...
• ## python画热力图（相关系数矩阵图）

万次阅读 多人点赞 2018-05-08 16:40:00
使用热力图的形式展示包括相关系数矩阵...如果想试一下，可以参考https://zhuanlan.zhihu.com/p/26100511所以研究了一下第二种方法，就是用seaborn包。参考了https://blog.csdn.net/a19990412/article/details/793...
• 主要介绍了Python绘制热力图,结合实例形式分析了Python使用pyheatmap及matplotlib模块进行数值计算与图形绘制相关操作技巧,需要的朋友可以参考下
• 使用plotly三维立体高逼格图，代码传送门： import plotly.graph_objects as go import numpy as np np.random.seed(1) N = 70 fig = go.Figure(data=[go.Mesh3d(x=(70*np.random.randn(N)), y=(55*np.random....
• 混淆矩阵热力图如下所示： 代码如下： import seaborn as sn #画图模块 from sklearn.metrics import confusion_matrix def plot_matrix(y_true, y_pred,title_name): cm = confusion_matrix(y_true, y_pred)#...
• # 定义热图的横纵坐标 xLabel = ['A', 'B', 'C', 'D', 'E'] yLabel = ['1', '2', '3', '4', '5'] # 准备数据阶段，利用random生成二维数据（5*5） data = [] for i in range(5): temp = [] for j in range(5...
• Python代码： import plotly.graph_objects as go import numpy as np import pandas as pd def get_data(size): R = np.linspace(0, 2 * np.pi, size) x = np.linspace(start=0, stop=size, num=size, dtype...
• 主要介绍了详解python 利用echarts地图(热力图)(世界地图，省市地图，区县地图),文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
• pyHeatMap一个用于绘制热力图的Python库。 依赖于Pillow，Python 2/3兼容。
• 原大神博客：...import numpy as np import pandas as pd import folium import webbrowser from folium.plugins import HeatMap posi=pd.read_excel("2015Cities-CHI...
• Python生物信息学⑥绘制热图及火山图 Python生物信息学③提取差异基因 通过上Python生物信息学③提取差异基因得到了该数据集的差异分析的两个关键参数，1.差异倍数（foldchange）以及2.差异的P值。本篇目的是得到...
• pyHeatMap是一个使用Python生成热图的库，基本代码是我一年多之前写的，最近把它从项目中抠出来做成一个独立的库并开源。(https://github.com/oldj/pyheatmap) 可以直接下载源码安装最新的版本，也可以通过pip或...
• 'weight' : 'normal', 'size' : 20, } #横纵轴的名称 plt.xlabel('round',font1) plt.ylabel('value',font1) #热力图名称 ax.set_title('DX model score',font1) #图的输出 #将文件保存至文件中并且出图 ...
• 点上方蓝字人工智能算法与Python大数据获取更多干货在右上方···设为星标★，第一时间获取资源仅做学术分享，如有侵权，联系删除转载于 ：作者丨Drazen Zaric来源 |专...
• 此时返回的值corrmat为相关矩阵 f, ax = plt.subplots(figsize=(12, 9)) sns.heatmap(corrmat, vmax=.8, square=True) # 将这个相关矩阵以热图的形式出来 plt.savefig('C:/Users/Mloong/Desktop/f_image/two ...
• xticklabels(ax.get_xticklabels(), rotation=20) cmap的参数如下，参考【Python】绘制热力图seaborn.heatmap，cmap设置颜色的参数： Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r,...
• 今天遇到了这样一个问题，使用matplotlib绘制热图数组中横纵坐标自然是图片的像素排列顺序， 但是这样带来的问题就是出来的x，y轴中坐标点的数据任然是x，y在数组中的下标， 实际中我们可能期望坐标点是其他的一个...

...

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