python fit distributions

fitter包是一个用于标识数据样本分布的Python库。它使用了80个分布模型，并允许您绘制结果以检查最可能的分布和最佳参数。您可以通过执行`pip install fitter`来安装它。一旦安装完成，您可以使用`fit`方法来拟合数据，然后使用`fitted_param`属性来获取最佳参数。另外，您还可以使用`fitted_pdf`属性来获取拟合分布的概率密度函数。

相关问题

python distfit

distfit is a Python package for fitting and comparing probability distributions. It provides a simple interface for fitting a variety of probability distributions to data, including continuous and discrete distributions. The package also includes tools for comparing the goodness of fit of different distributions and for generating random samples from fitted distributions.

distfit is useful for a variety of applications in data analysis and modeling, including modeling the distribution of a dataset, simulating data from a fitted distribution, and testing the goodness of fit of different distribution models. The package is easy to use and provides a convenient way to work with probability distributions in Python.

Some of the key features of distfit include:

- A simple interface for fitting a variety of probability distributions to data
- Tools for comparing the goodness of fit of different distributions
- Support for both continuous and discrete distributions
- Tools for generating random samples from fitted distributions
- Integration with other Python packages for data analysis and modeling, such as pandas and numpy

Overall, distfit is a powerful tool for working with probability distributions in Python, and it can be a valuable addition to any data analysis or modeling workflow.

分布拟合检验python

在Python中，可以使用fitter库来进行分布拟合检验。首先，你需要安装fitter库，可以使用pip install fitter命令进行安装。接下来，你可以使用scipy库中的stats模块生成一段模拟数据，例如使用stats.norm.rvs函数生成一个服从正态分布的数据样本。然后，你可以使用fitter库中的Fitter类来拟合数据样本的分布，可以通过指定distributions参数来选择要尝试的分布类型。最后，可以使用Fitter类的summary方法来查看拟合结果的摘要信息。

例如，下面的代码演示了如何使用fitter库进行分布拟合检验：

# 导入所需库
from scipy import stats
import numpy as np
from fitter import Fitter

# 生成模拟数据
data1 = list(stats.norm.rvs(loc=0, scale=2, size=70000))
data2 = list(stats.norm.rvs(loc=0, scale=20, size=30000))
data = np.array(data1 + data2)

# 使用fitter拟合数据样本的分布
f = Fitter(data, distributions=\['norm', 't', 'laplace'\])
f.fit()
f.summary()

在上述代码中，我们生成了一个模拟数据样本，其中包含了两个不同的正态分布。然后，我们使用Fitter类来拟合数据样本的分布，指定了要尝试的分布类型为正态分布（norm）、t分布（t）和拉普拉斯分布（laplace）。最后，我们使用summary方法来查看拟合结果的摘要信息。

希望这个例子能够帮助你理解如何使用fitter库进行分布拟合检验。
#### 引用[.reference_title]
- *1* [Python拟合数据样本的分布](https://blog.csdn.net/sinat_23971513/article/details/111874082)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [概率统计Python计算：假设检验应用——分布拟合检验](https://blog.csdn.net/u012958850/article/details/118366266)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [利用python通过拟合优度检验判别数据是否为正态分布](https://blog.csdn.net/qq_45019507/article/details/105842372)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]