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Released:

Generate profile report for pandas DataFrame

Project description

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 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 v2.9.0 released

The release candidate for v2.9.0 was already out for a while, now v2.9.0 is finally released. See the changelog below to know what has changed.

Spark backend in progress

We can happily announce that we're working on a 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!It's extra exciting that GitHub matches your contribution for the first year.

Find more information here:

September 2, 2020 💘

Contents:Examples |Installation | Documentation |Large datasets | Command line usage |Advanced usage | Support |Types | How to contribute |Editor Integration | Dependencies

Examples

The following examples can give you an impression of what the package can do:

• Census Income (US Adult Census data relating income)
• NASA Meteorites (comprehensive set of meteorite landings)
• Titanic (the 'Wonderwall' of datasets)
• NZA (open data from the Dutch Healthcare Authority)
• Stata Auto (1978 Automobile data)
• Vektis (Vektis Dutch Healthcare data)
• Colors (a simple colors dataset)

Specific features:

• Russian Vocabulary (demonstrates text analysis)
• Cats and Dogs (demonstrates image analysis from the file system)
• Celebrity Faces (demonstrates image analysis with EXIF information)
• Website Inaccessibility (demonstrates URL analysis)
• Orange prices and Coal prices (showcases report themes)

Tutorials:

• Tutorial: report structure using Kaggle data (advanced) (modify the report's structure)

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Installation

Using pip

You can install using the pip package manager by running

Alternatively, you could install the latest version directly from Github:

Using conda

You can install using the conda package manager by running

From source

Download the source code by cloning the repository or by pressing 'Download ZIP' on this page.Install by navigating to the proper directory and running

Documentation

The documentation for pandas_profiling can be found here. Previous documentation is still available here.

Getting started

Start by loading in your pandas DataFrame, e.g. by using

To generate the report, run:

Explore deeper

You can configure the profile report in any way you like. The example code below loads the explorative configuration file, that includes many features for text (length distribution, unicode information), files (file size, creation time) and images (dimensions, exif information). If you are interested what exact settings were used, you can compare with the default configuration file.

Learn more about configuring pandas-profiling on the Advanced usage page.

Jupyter Notebook

We recommend generating reports interactively by using the Jupyter notebook.There are two interfaces (see animations below): through widgets and through a HTML report.

This is achieved by simply displaying the report. In the Jupyter Notebook, run:

The HTML report can be included in a Jupyter notebook:

Run the following code:

Saving the report

If you want to generate a HTML report file, save the ProfileReport to an object and use the to_file() function:

Alternatively, you can obtain the data as json:

Large datasets

Www honestech com video conversion for mac. Version 2.4 introduces minimal mode.This is a default configuration that disables expensive computations (such as correlations and dynamic binning).Use the following syntax:

Command line usage

For standard formatted CSV files that can be read immediately by pandas, you can use the pandas_profiling executable. Run

for information about options and arguments.

Advanced usage

A set of options is available in order to adapt the report generated.

• title (str): Title for the report ('Pandas Profiling Report' by default).
• pool_size (int): Number of workers in thread pool. When set to zero, it is set to the number of CPUs available (0 by default).
• progress_bar (bool): If True, pandas-profiling will display a progress bar.

More settings can be found in the default configuration file, minimal configuration file and dark themed configuration file.

Example

Maintaining and developing the open-source code for pandas-profiling, with millions of downloads and thousands of users, would not be possible with support of our gracious sponsors.

We would like to thank our generous Github Sponsors supporters who make pandas-profiling possible:

More info if you would like to appear here: Github Sponsor page

Types

Types are a powerful abstraction for effective data analysis, that goes beyond the logical data types (integer, float etc.).pandas-profiling currently recognizes the following types: Boolean, Numerical, Date, Categorical, URL, Path, File and Image.

We have developed a type system for Python, tailored for data analysis: visions.Selecting the right typeset drastically reduces the complexity the code of your analysis.Future versions of pandas-profiling will have extended type support through visions! Iclipboard 5 0.

Contributing

Read on getting involved in the Contribution Guide.

Editor integration

PyCharm integration

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1. Install pandas-profiling via the instructions above

2. Locate your pandas-profiling executable.

   On macOS / Linux / BSD:

   On Windows:

3. In Pycharm, go to Settings (or Preferences on macOS) > Tools > External tools

4. Click the + icon to add a new external tool

5. Insert the following values

   • Name: Pandas Profiling
   • Program: The location obtained in step 2
   • Arguments: '$FilePath$' '$FileDir$/$FileNameWithoutAllExtensions$_report.html'
   • Working Directory: $ProjectFileDir$

To use the PyCharm Integration, right click on any dataset file:External Tools > Pandas Profiling.

Other integrations

Other editor integrations may be contributed via pull requests.

Dependencies

The profile report is written in HTML and CSS, which means pandas-profiling requires a modern browser.

You need Python 3 to run this package. Other dependencies can be found in the requirements files:

Release historyRelease notifications | RSS feed

2.9.0

2.9.0rc1 pre-release

2.8.0

2.7.1

2.7.0

2.6.0

2.5.0 Recording audio files on pc.

2.4.0

2.3.0

2.2.0

2.1.2

2.1.0

2.0.3

1.4.3

1.4.2

1.4.1

1.4.0

1.3.0

1.2.0

1.1.0

1.0.0a2 pre-release

1.0.0a1 pre-release

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Hashes for pandas_profiling-2.9.0-py2.py3-none-any.whl

Hashes for pandas-profiling-2.9.0.tar.gz

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Released:

Generate profile report for pandas DataFrame

Project description

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 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 v2.9.0 released

The release candidate for v2.9.0 was already out for a while, now v2.9.0 is finally released. See the changelog below to know what has changed.

Spark backend in progress

We can happily announce that we're working on a 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!It's extra exciting that GitHub matches your contribution for the first year.

Find more information here:

September 2, 2020 💘

Contents:Examples |Installation | Documentation |Large datasets | Command line usage |Advanced usage | Support |Types | How to contribute |Editor Integration | Dependencies

Examples

The following examples can give you an impression of what the package can do:

• Census Income (US Adult Census data relating income)
• NASA Meteorites (comprehensive set of meteorite landings)
• Titanic (the 'Wonderwall' of datasets)
• NZA (open data from the Dutch Healthcare Authority)
• Stata Auto (1978 Automobile data)
• Vektis (Vektis Dutch Healthcare data)
• Colors (a simple colors dataset)

Specific features:

• Russian Vocabulary (demonstrates text analysis)
• Cats and Dogs (demonstrates image analysis from the file system)
• Celebrity Faces (demonstrates image analysis with EXIF information)
• Website Inaccessibility (demonstrates URL analysis)
• Orange prices and Coal prices (showcases report themes)

Tutorials:

• Tutorial: report structure using Kaggle data (advanced) (modify the report's structure)

Master Of Typing In Chinese 3 2 2 07

Installation

Using pip

You can install using the pip package manager by running

Alternatively, you could install the latest version directly from Github:

Using conda

You can install using the conda package manager by running

From source

Download the source code by cloning the repository or by pressing 'Download ZIP' on this page.Install by navigating to the proper directory and running

Documentation

The documentation for pandas_profiling can be found here. Previous documentation is still available here.

Getting started

Start by loading in your pandas DataFrame, e.g. by using

To generate the report, run:

Explore deeper

You can configure the profile report in any way you like. The example code below loads the explorative configuration file, that includes many features for text (length distribution, unicode information), files (file size, creation time) and images (dimensions, exif information). If you are interested what exact settings were used, you can compare with the default configuration file.

Learn more about configuring pandas-profiling on the Advanced usage page.

Jupyter Notebook

We recommend generating reports interactively by using the Jupyter notebook.There are two interfaces (see animations below): through widgets and through a HTML report.

This is achieved by simply displaying the report. In the Jupyter Notebook, run:

The HTML report can be included in a Jupyter notebook:

Run the following code:

Saving the report

If you want to generate a HTML report file, save the ProfileReport to an object and use the to_file() function:

Alternatively, you can obtain the data as json:

Large datasets

Www honestech com video conversion for mac. Version 2.4 introduces minimal mode.This is a default configuration that disables expensive computations (such as correlations and dynamic binning).Use the following syntax:

Command line usage

For standard formatted CSV files that can be read immediately by pandas, you can use the pandas_profiling executable. Run

for information about options and arguments.

Advanced usage

A set of options is available in order to adapt the report generated.

• title (str): Title for the report ('Pandas Profiling Report' by default).
• pool_size (int): Number of workers in thread pool. When set to zero, it is set to the number of CPUs available (0 by default).
• progress_bar (bool): If True, pandas-profiling will display a progress bar.

More settings can be found in the default configuration file, minimal configuration file and dark themed configuration file.

Example

Maintaining and developing the open-source code for pandas-profiling, with millions of downloads and thousands of users, would not be possible with support of our gracious sponsors.

We would like to thank our generous Github Sponsors supporters who make pandas-profiling possible:

More info if you would like to appear here: Github Sponsor page

Types

Types are a powerful abstraction for effective data analysis, that goes beyond the logical data types (integer, float etc.).pandas-profiling currently recognizes the following types: Boolean, Numerical, Date, Categorical, URL, Path, File and Image.

We have developed a type system for Python, tailored for data analysis: visions.Selecting the right typeset drastically reduces the complexity the code of your analysis.Future versions of pandas-profiling will have extended type support through visions! Iclipboard 5 0.

Contributing

Read on getting involved in the Contribution Guide.

Editor integration

PyCharm integration

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1. Install pandas-profiling via the instructions above

2. Locate your pandas-profiling executable.

   On macOS / Linux / BSD:

   On Windows:

3. In Pycharm, go to Settings (or Preferences on macOS) > Tools > External tools

4. Click the + icon to add a new external tool

5. Insert the following values

   • Name: Pandas Profiling
   • Program: The location obtained in step 2
   • Arguments: '$FilePath$' '$FileDir$/$FileNameWithoutAllExtensions$_report.html'
   • Working Directory: $ProjectFileDir$

To use the PyCharm Integration, right click on any dataset file:External Tools > Pandas Profiling.

Other integrations

Other editor integrations may be contributed via pull requests.

Dependencies

The profile report is written in HTML and CSS, which means pandas-profiling requires a modern browser.

You need Python 3 to run this package. Other dependencies can be found in the requirements files:

Release historyRelease notifications | RSS feed

2.9.0

2.9.0rc1 pre-release

2.8.0

2.7.1

2.7.0

2.6.0

2.5.0 Recording audio files on pc.

2.4.0

2.3.0

2.2.0

2.1.2

2.1.0

2.0.3

1.4.3

1.4.2

1.4.1

1.4.0

1.3.0

1.2.0

1.1.0

1.0.0a2 pre-release

1.0.0a1 pre-release

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Hashes for pandas_profiling-2.9.0-py2.py3-none-any.whl

Hashes for pandas-profiling-2.9.0.tar.gz

The #1 Premier Online Typing Program!

Looking for a quality online program to learn to type or to improve your keyboarding skills? TypingInstructorWeb is a high quality program that is both educational and entertaining. Choose from over 20 Typing Courses for typists of all ages and skill levels, based on Typing Instructor, an award-winning typing program for 28 years, and recipient of the Top Ten Reviews Gold Award for its Kids and Platinum Typing Software.

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