Python之pandas-profiling:pandas-profiling库的简介、安装、使用方法之详细攻略

一个处女座的程序猿 2020-11-16 12:12:48
Python pandas pandas-profiling profiling


Python之pandas-profiling:pandas-profiling库的简介、安装、使用方法之详细攻略

 

 

 

 

目录

pandas-profiling库的简介

pandas-profiling库的安装

pandas-profiling库的使用方法

1、基础用法


 

 

 

pandas-profiling库的简介

        从pandas数据路由生成配置文件报告。pandas df.describe()函数很棒,但对于严肃的探索性数据分析来说有点基础。pandas_profiling通过php .profile_report()扩展了pandas DataFrame,用于快速数据分析。对于每一列,以下统计数据-如果与列类型相关-在一个交互式HTML报告中显示:

  • 类型推断:检测数据流中的列类型。
  • 基本要素:类型、唯一值、缺失值
  • 分位数统计如最小值,Q1,中位数,Q3,最大值,范围,四分位数范围
  • 描述统计,如平均值,众数,标准差,总和,中位数绝对偏差,变异系数,峰度,偏度
  • 最常见的价值观
  • 柱状图
  • 高度相关变量的相关性突出,Spearman, Pearson和Kendall矩阵
  • 缺失值矩阵,计数,热图和缺失值的树状图
  • 学习文本数据的分类(大写,空格),脚本(拉丁语,西里尔字母)和块(ASCII)。
  • 文件和图像分析提取文件大小,创建日期和尺寸和扫描截短的图像或那些包含EXIF信息。

 

 

 

pandas-profiling库的安装

pip install pandas-profiling

 

 

 

pandas-profiling库的使用方法

1、基础用法

import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
df = pd.DataFrame(
np.random.rand(100, 5),
columns=["a", "b", "c", "d", "e"]
)
profile = ProfileReport(df, title="Pandas Profiling Report")
profile.to_file("your_report.html")
profile = ProfileReport(large_dataset, minimal=True)
profile.to_file("output.html")
profile = df.profile_report(title='Pandas Profiling Report', plot={'histogram': {'bins': 8}})
profile.to_file("output.html")

 

 

 

 

 

版权声明
本文为[一个处女座的程序猿]所创,转载请带上原文链接,感谢
https://yunyaniu.blog.csdn.net/article/details/109710384

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