将趋势线添加到 matplotlib 线图 python [英] Adding a trend line to a matplotlib line plot python

问题描述

抱歉，如果有人问过这个问题，但我在任何地方都找不到答案.我想在plt图上添加一条总体趋势线.样本数据:

将pandas导入为pd数据= pd.DataFrame({'year':[2011，2012，2013，2014，2015，2016，2017，2018，2019],'值': [2, 5, 8, 4, 1, 6, 10, 14, 8]})导入matplotlib.pyplot作为pltplt.rcParams['figure.figsize'] = [28, 26]data.plot(x = "year", y = "value", fontsize = 30)plt.xlabel('时间', fontsize = 30)

如何添加趋势线?

解决方案

如果您正在寻找简单的线性回归拟合，则可以直接使用

来自文档

<块引用>

regplot() 和 lmplot() 函数密切相关，但前者是轴级函数，后者是结合了 regplot() 和 FacetGrid 允许您在不同子图中绘制数据之间的条件关系网格.

Apologies if this has already been asked but I can't find the answer anywhere. I want to add an overall trend line to a plt plot. Sample data:

import pandas as pd
data = pd.DataFrame({'year': [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 
                              2019],
                     'value': [2, 5, 8, 4, 1, 6, 10, 14, 8]})

import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [28, 26]
data.plot(x = "year", y = "value", fontsize = 30)
plt.xlabel('Time', fontsize = 30)

How can I add a trend line?

解决方案

If you are looking for a simple linear regression fit, you can use directly either lmplot or regplot from seaborn. It performs the linear regression and plots the fit (line) with a 95% confidence interval (shades, default value). You can also use NumPy to perform the fit. In case you want to use NumPy, comment below and I will update.

import seaborn as sns

# Your DataFrame here 

# sns.lmplot(x='year',y='value',data=data,fit_reg=True) 

sns.regplot(x='year',y='value',data=data, fit_reg=True) 

From the Docs

The regplot() and lmplot() functions are closely related, but the former is an axes-level function while the latter is a figure-level function that combines regplot() and FacetGrid which allows you to plot conditional relationships amongst your data on different subplots in the grid.

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