seaborn | 易学教程

How to scale the x and y axis equally by log in Seaborn?

阅读更多关于 How to scale the x and y axis equally by log in Seaborn?

问题 I want to create a regplot with a linear regression in Seaborn and scale both axes equally by log, such that the regression stays a straight line. An example: import matplotlib.pyplot as plt import seaborn as sns some_x=[0,1,2,3,4,5,6,7] some_y=[3,5,4,7,7,9,9,10] ax = sns.regplot(x=some_x, y=some_y, order=1) plt.ylim(0, 12) plt.xlim(0, 12) plt.show() What I get: If I scale the x and y axis by log, I would expect the regression to stay a straight line. What I tried: import matplotlib.pyplot as

Python package to plot two heatmaps in one (split each square into two triangles)

阅读更多关于 Python package to plot two heatmaps in one (split each square into two triangles)

问题 I've been searching around but couldn't find an easy solution to plot two heatmaps in one graphic by having each square in the heatmap split into two triangles (similar to the attached graphic I saw in a paper). Does anybody know a Python package that is able to do this? I tried seaborn but I don't think it has an easy way to achieve this. Thank you for your time! -Peter 回答1: plt.tripcolor colors a mesh of triangles similar to how plt.pcolormesh colors a rectangular mesh. Also similar to

Python package to plot two heatmaps in one (split each square into two triangles)

阅读更多关于 Python package to plot two heatmaps in one (split each square into two triangles)

How to Fix: “ImportError: DLL load failed The specified procedure could not be found.” when the DLLs are there

阅读更多关于 How to Fix: “ImportError: DLL load failed The specified procedure could not be found.” when the DLLs are there

问题 Updated to new Anaconda 2018, opened a jupyter notebook that worked prior to the update. Having problems with loading seaborn into the script. Tried following several threads on this but nothing worked question. I've tried: -Setting Enviromental Variables -Reinstalling Anaconda2018 -Uninstalled and reinstalled seaborn using conda -Reinstalled SciPy Running out of options as I want to use Seaborn 0.9.0 and python-3.7 but I cannot seem to get 3.7 to play with 0.9.0. Help? import seaborn as sns

李宏毅线性回归预测PM2.5

阅读更多关于李宏毅线性回归预测PM2.5

作业说明　　给定训练集train.csv，要求根据前9个小时的空气监测情况预测第10个小时的PM2.5含量。训练集介绍：　　(1)：CSV文件，包含台湾丰原地区240天的气象观测资料(取每个月前20天的数据做训练集，12月X20天=240天，每月后10天数据用于测试，对学生不可见); 　　(2)：每天的监测时间点为0时，1时......到23时，共24个时间节点; 　　(3)：每天的检测指标包括CO、NO、PM2.5、PM10等气体浓度，是否降雨、刮风等气象信息，共计18项； (4):数据集 https://github.com/datawhalechina/leeml-notes/blob/master/docs/Homework/HW_1/Dataset 数据处理【下文中提到的“数据帧”并非指pandas库中的数据结构DataFrame,而是指一个二维的数据包】根据作业要求可知，需要用到连续9个时间点的气象观测数据，来预测第10个时间点的PM2.5含量。针对每一天来说，其包含的信息维度为(18,24)(18项指标，24个时间节点)。可以将0到8时的数据截取出来，形成一个维度为(18,9)的数据帧，作为训练数据，将9时的PM2.5含量取出来，作为该训练数据对应的label；同理可取1到9时的数据作为训练用的数据帧，10时的PM2.5含量作为label......以此

捋一捋少有人知的 Python "重试机制"

阅读更多关于捋一捋少有人知的 Python "重试机制"

点击上方“ Python爬虫与数据挖掘 ”，进行关注回复“ 书籍 ”即可获赠Python从入门到进阶共10本电子书今日鸡汤弃我去者，昨日之日不可留。周末愉快，欢迎小伙伴积极学习，文末有《 Python数据分析》5本书籍的送书活动，记得参与噢~ 为了避免由于一些网络或等其他不可控因素，而引起的功能性问题。比如在发送请求时，会因为网络不稳定，往往会有请求超时的问题。这种情况下，我们通常会在代码中加入重试的代码。重试的代码本身不难实现，但如何写得优雅、易用，是我们要考虑的问题。这里要给大家介绍的是一个第三方库 - Tenacity （标题中的重试机制并并不准确，它不是 Python 的内置模块，因此并不能称之为机制），它实现了几乎我们可以使用到的所有重试场景，比如：在什么情况下才进行重试？重试几次呢? 重试多久后结束？每次重试的间隔多长呢？重试失败后的回调？在使用它之前，先要安装它 $ pip install tenacity 1. 最基本的重试无条件重试，重试之间无间隔 from tenacity import retry @retry def test_retry () : print( "等待重试，重试无间隔执行..." ) raise Exception test_retry() 无条件重试，但是在重试之前要等待 2 秒 from

How to Spread Plot's Date Axis According To Years When Plotting With Seaborn?

阅读更多关于 How to Spread Plot's Date Axis According To Years When Plotting With Seaborn?

问题 I'm trying to train a Linear Regression Model with Python via using Google Stock Prices that can be found here: https://www.kaggle.com/medharawat/google-stock-price And trying to predict future stocks by given features. After that I'm planning to plot it with the values in current dataset. First, I read dataframes with date values with date parser and concatted these 2 dataframes into one in order to split it myself: parser = lambda date: pd.datetime.strptime(date, '%m/%d/%Y') df_test=pd.read

How to plot a barchart by pandas groupby and then loop for all unique values

阅读更多关于 How to plot a barchart by pandas groupby and then loop for all unique values

问题 I have the following data which has a persons name, score and what attempt number it was: # Import pandas library import pandas as pd import numpy as np # Data data = [['tom', 10,1], ['nick', 15,1], ['dom', 14,1], ['tom', 15,2], ['nick', 18,2], ['dom', 15,2], ['tom', 17,3] , ['nick', 14,3], ['tom',16 ,4], ['dom', 22,3]] # Create the pandas DataFrame df = pd.DataFrame(data, columns = ['Name', 'Score','Attempt']) # print dataframe. df Name Score Attempt 0 tom 10 1 1 nick 15 1 2 dom 14 1 3 tom

Nesting or combining matplotlib figures and plots?

阅读更多关于 Nesting or combining matplotlib figures and plots?

问题 I have a function that takes an arbitrary length 3D data set of dates, prices(float), and some resulting value(float) and makes a set of seaborn heatmaps split by year. The pseudocode is as follows (note the number of years varies by dataset so I need it to arbitrarily scale): def makePlots(data): split data by year fig,axs=plt.subplots(1, numYears) x=0 for year in years sns.heatmap(data[year], ax = axs[x++]) return axs this outputs a single matplotlib figure with a heatmap for each year next

Absolute Values and Percentage Values Side by Side in Bar Chart Matplotlib

阅读更多关于 Absolute Values and Percentage Values Side by Side in Bar Chart Matplotlib

问题 I would like to count on your help for the following problem that I have been facing. I've been trying, but without success, to put absolute values and percentage values side by side. Where 53 and 47 are percentage values and would be outside the parentheses and 17 and 15 are absolute values and would be inside the parentheses. Both absolute values and percentage values are already known, therefore, there is no need for any calculation to obtain them. I leave my code here for you to see how

订阅 seaborn