pandas 将趋势线添加到 matplotlib 线图 python
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Adding a trend line to a matplotlib line plot python
提问by prmlmu
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:
抱歉，如果已经有人问过这个问题，但我在任何地方都找不到答案。我想在 plt 图中添加一条整体趋势线。样本数据：
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?
如何添加趋势线？
回答by Sheldore
If you are looking for a simple linear regression fit, you can use directly either lmplotor regplotfrom 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.
如果您正在寻找简单的线性回归拟合，您可以直接使用lmplot或regplotfrom seaborn。它执行线性回归并绘制具有 95% 置信区间（阴影，默认值）的拟合（线）。您还可以使用 NumPy 来执行拟合。如果你想使用 NumPy，请在下面评论，我会更新。
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
来自文档
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
FacetGridwhich allows you to plot conditional relationships amongst your data on different subplots in the grid.
regplot() 和 lmplot() 函数密切相关，但前者是轴级函数，而后者是结合 regplot() 的图形级函数，
FacetGrid它允许您在不同子图上绘制数据之间的条件关系在网格中。
