# Pandas Profiling
![Pandas Profiling Logo Header](https://pandas-profiling.github.io/pandas-profiling/docs/assets/logo_header.png)
[![Build Status](https://travis-ci.com/pandas-profiling/pandas-profiling.svg?branch=master)](https://travis-ci.com/pandas-profiling/pandas-profiling)
[![Code Coverage](https://codecov.io/gh/pandas-profiling/pandas-profiling/branch/master/graph/badge.svg?token=gMptB4YUnF)](https://codecov.io/gh/pandas-profiling/pandas-profiling)
[![Release Version](https://img.shields.io/github/release/pandas-profiling/pandas-profiling.svg)](https://github.com/pandas-profiling/pandas-profiling/releases)
[![Python Version](https://img.shields.io/pypi/pyversions/pandas-profiling)](https://pypi.org/project/pandas-profiling/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)
<p align="center">
<a href="https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/">Documentation</a>
|
<a href="https://join.slack.com/t/pandas-profiling/shared_invite/zt-l2iqwb92-9JpTEdFBijR2G798j2MpQw">Slack</a>
|
<a href="https://stackoverflow.com/questions/tagged/pandas-profiling">Stack Overflow</a>
</p>
Generates profile reports from a pandas `DataFrame`.
The pandas `df.describe()` function is great but a little basic for serious exploratory data analysis.
`pandas_profiling` extends the pandas DataFrame with `df.profile_report()` for quick data analysis.
For each column the following statistics - if relevant for the column type - are presented in an interactive HTML report:
* **Type inference**: detect the [types](#types) of columns in a dataframe.
* **Essentials**: type, unique values, missing values
* **Quantile statistics** like minimum value, Q1, median, Q3, maximum, range, interquartile range
* **Descriptive statistics** like mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness
* **Most frequent values**
* **Histogram**
* **Correlations** highlighting of highly correlated variables, Spearman, Pearson and Kendall matrices
* **Missing values** matrix, count, heatmap and dendrogram of missing values
* **Text analysis** learn about categories (Uppercase, Space), scripts (Latin, Cyrillic) and blocks (ASCII) of text data.
* **File and Image analysis** extract file sizes, creation dates and dimensions and scan for truncated images or those containing EXIF information.
## Announcements
### Version v2.10.0rc1 released
v2.10.0rc1 includes a major overhaul of the type system, now fully reliant on visions.
See the changelog below to know what has changed.
### Spark backend in progress
We can happily announce that we're nearing v1 for the Spark backend for generating profile reports.
Stay tuned.
### Support `pandas-profiling`
The development of `pandas-profiling` relies completely on contributions.
If you find value in the package, we welcome you to support the project through [GitHub Sponsors](https://github.com/sponsors/sbrugman)!
It's extra exciting that GitHub **matches your contribution** for the first year.
Find more information here:
- [Changelog v2.10.0rc1](https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/changelog.html#changelog-v2-10-0rc1)
- [Sponsor the project on GitHub](https://github.com/sponsors/sbrugman)
_January 5, 2021 ����_
---
_Contents:_ **[Examples](#examples)** |
**[Installation](#installation)** | **[Documentation](#documentation)** |
**[Large datasets](#large-datasets)** | **[Command line usage](#command-line-usage)** |
**[Advanced usage](#advanced-usage)** | **[Support](#supporting-open-source)** |
**[Types](#types)** | **[How to contribute](#contributing)** |
**[Editor Integration](#editor-integration)** | **[Dependencies](#dependencies)**
---
## Examples
The following examples can give you an impression of what the package can do:
* [Census Income](https://pandas-profiling.github.io/pandas-profiling/examples/master/census/census_report.html) (US Adult Census data relating income)
* [NASA Meteorites](https://pandas-profiling.github.io/pandas-profiling/examples/master/meteorites/meteorites_report.html) (comprehensive set of meteorite landings) [![Open In Colab](https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/github/pandas-profiling/pandas-profiling/blob/master/examples/meteorites/meteorites.ipynb) [![Binder](https://camo.githubusercontent.com/483bae47a175c24dfbfc57390edd8b6982ac5fb3/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667)](https://mybinder.org/v2/gh/pandas-profiling/pandas-profiling/master?filepath=examples%2Fmeteorites%2Fmeteorites.ipynb)
* [Titanic](https://pandas-profiling.github.io/pandas-profiling/examples/master/titanic/titanic_report.html) (the "Wonderwall" of datasets) [![Open In Colab](https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/github/pandas-profiling/pandas-profiling/blob/master/examples/titanic/titanic.ipynb) [![Binder](https://camo.githubusercontent.com/483bae47a175c24dfbfc57390edd8b6982ac5fb3/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667)](https://mybinder.org/v2/gh/pandas-profiling/pandas-profiling/master?filepath=examples%2Ftitanic%2Ftitanic.ipynb)
* [NZA](https://pandas-profiling.github.io/pandas-profiling/examples/master/nza/nza_report.html) (open data from the Dutch Healthcare Authority)
* [Stata Auto](https://pandas-profiling.github.io/pandas-profiling/examples/master/stata_auto/stata_auto_report.html) (1978 Automobile data)
* [Vektis](https://pandas-profiling.github.io/pandas-profiling/examples/master/vektis/vektis_report.html) (Vektis Dutch Healthcare data)
* [Colors](https://pandas-profiling.github.io/pandas-profiling/examples/master/colors/colors_report.html) (a simple colors dataset)
* [UCI Bank Dataset](https://pandas-profiling.github.io/pandas-profiling/examples/master/cbank_marketing_data/uci_bank_marketing_report.html) (banking marketing dataset)
Specific features:
* [Russian Vocabulary](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/russian_vocabulary.html) (demonstrates text analysis)
* [Cats and Dogs](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/cats-and-dogs.html) (demonstrates image analysis from the file system)
* [Celebrity Faces](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/celebrity-faces.html) (demonstrates image analysis with EXIF information)
* [Website Inaccessibility](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/website_inaccessibility_report.html) (demonstrates URL analysis)
* [Orange prices](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/united_report.html) and [Coal prices](https://pandas-profiling.github.io/pandas-profiling/examples/master/features/flatly_report.html) (showcases report themes)
Tutorials:
* [Tutorial: report structure using Kaggle data (advanced)](https://pandas-profiling.github.io/pandas-profiling/examples/master/tutorials/modify_report_structure.ipynb) (modify the report's structure) [![Open In Colab](https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/github/pandas-profiling/pandas-profiling/blob/master/examples/tutorials/modify_report_structure.ipynb) [![Binder](https://camo.githubusercontent.com/483bae47a175c24dfbfc57390edd8b6982ac5fb3/68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667)](https://mybinder.org/v2/gh/pandas-profiling
没有合适的资源?快使用搜索试试~ 我知道了~
pandas分析:从pandas DataFrame对象创建HTML分析报告
共319个文件
py:183个
html:38个
rst:33个
5星 · 超过95%的资源 需积分: 35 3 下载量 58 浏览量
2021-02-04
14:07:26
上传
评论 1
收藏 32.95MB ZIP 举报
温馨提示
熊猫分析 | 从pandas DataFrame生成配置文件报告。 pandas df.describe()函数虽然功能强大,但对于进行认真的探索性数据分析却有些基础。 pandas_profiling使用df.profile_report()扩展了pandas DataFrame,以进行快速数据分析。 对于每个列,以下统计信息(如果与列类型相关)将显示在交互式HTML报告中: 类型推断:检测数据帧中列的。 要点:类型,唯一值,缺失值 分位数统计信息,例如最小值,Q1,中位数,Q3,最大值,范围,四分位数范围 描述性统计数据,例如均值,众数,标准偏差,总和,中位数绝对偏差,变异系数
资源推荐
资源详情
资源评论
收起资源包目录
pandas分析:从pandas DataFrame对象创建HTML分析报告 (319个子文件)
make.bat 1KB
make.bat 884B
flatly.bootstrap.min.css 124KB
united.bootstrap.min.css 120KB
bootstrap.min.css 118KB
bootstrap-theme.min.css 23KB
style.css 5KB
custom.css 267B
config_variables.csv 1KB
config_correlations.csv 1KB
config_html.csv 1016B
config_missing.csv 704B
config_general.csv 346B
config_interactions.csv 308B
streamlit-integration.gif 8.31MB
widgets.gif 3.55MB
widgets.gif 3.55MB
iframe.gif 2.38MB
iframe.gif 2.38MB
.gitignore 982B
.gitkeep 0B
style.html 2KB
layout.html 1KB
select.html 1KB
frequency_table.html 1KB
navigation.html 1KB
frequency_table_small.html 1KB
tabs.html 1KB
toggle_button.html 944B
javascript.html 904B
report.html 885B
warnings.html 667B
sections.html 533B
table.html 498B
batch_grid.html 469B
warning_high_correlation.html 432B
collapse.html 371B
diagram.html 347B
grid.html 287B
variable_info.html 281B
sample.html 240B
variable.html 239B
named_list.html 213B
warning_truncated.html 207B
warning_infinite.html 205B
warning_missing.html 202B
footer.html 201B
warning_type_date.html 198B
warning_zeros.html 189B
warning_high_cardinality.html 174B
warning_skewed.html 170B
warning_unsupported.html 170B
list.html 165B
warning_constant.html 150B
warning_uniform.html 124B
warning_constant_length.html 119B
warning_unique.html 117B
warning_duplicates.html 116B
duplicate.html 115B
MANIFEST.in 664B
mypy.ini 457B
lazy_pipeline.ipynb 5.5MB
titanic.ipynb 5.5MB
titanic.ipynb 5.5MB
modify_report_structure.ipynb 10KB
meteorites.ipynb 5KB
meteorites.ipynb 5KB
jquery-1.12.4.min.js 95KB
bootstrap.min.js 36KB
script.js 491B
census_column_definition.json 477B
LICENSE 1KB
Makefile 1KB
Makefile 654B
CONTRIBUTING.md 7KB
bug_report.md 2KB
feature_request.md 709B
pull_request_template.md 268B
README.md 17KB
git-workflow.pdf 4.05MB
figure-git-workflow.pdf 2.75MB
twitter_wisdom.png 181KB
pycharm-integration.png 65KB
cli.png 63KB
cli.png 63KB
lambda-labs.png 54KB
logo_header.png 52KB
qt.png 32KB
qt.png 32KB
icon.png 12KB
icon.png 12KB
theme_united_demo.py 78KB
meteorites.py 36KB
test_typeset_default.py 26KB
test_describe.py 16KB
profile_report.py 15KB
render_categorical.py 14KB
summary_algorithms.py 13KB
summary_helpers.py 11KB
summary.py 10KB
共 319 条
- 1
- 2
- 3
- 4
资源评论
- weixin_588356812021-08-03用户下载后在一定时间内未进行评价,系统默认好评。
马福报
- 粉丝: 28
- 资源: 4567
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- springboot279基于javaweb的影院订票系统的设计与实现.zip
- springboot279基于javaweb的影院订票系统的设计与实现_0303174040.zip
- springboot280基于WEB的旅游推荐系统设计与实现.zip
- springboot280基于WEB的旅游推荐系统设计与实现_0303174040.zip
- 基于C语言罗斯方块游戏实现示例与解析
- springboot281旅游网站.zip
- springboot281旅游网站_0303174040.zip
- springboot282基于web的机动车号牌管理系统_0303173844.zip
- springboot282基于web的机动车号牌管理系统.zip
- springboot282基于web的机动车号牌管理系统_0303174040.zip
- springboot059课程答疑系统.zip
- springboot283图书商城管理系统.zip
- springboot059课程答疑系统_0303152757.zip
- springboot256基于springboot+vue的游戏交易系统_0303174040.zip
- springboot256基于springboot+vue的游戏交易系统.zip
- springboot061基于B2B平台的医疗病历交互系统.zip
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功