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If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript. Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Nov 18, Web Client for Visualizing Pandas Objects. Navigation Project description Release history Download files. Project links Homepage. Maintainers ahldev aschonfeld.
Duplicate data check To help guard against users loading the same data to D-Tale multiple times and thus eating up precious memory, we have a loose check for duplicate input data. Jupyter Notebook Within any jupyter ipython notebook executing a cell like this will display a small instance of D-Tale in the output cell.
Here are some examples: dtale. Docker Container If you have D-Tale installed within your docker container please add the following parameters to your docker run command. Here is an example: import pandas as pd import dtale import dtale. Binder I have built a repo which shows an example of how to run D-Tale within Binder here.
Command-line Base CLI options run dtale --help to see all options available Prop Description --host the name of the host you would like to use most likely not needed since socket.
This global variable is required for building any customized CLI loader. Timestamp pd. Timestamp 'now'. DataFrame np. This function is required for building any customized CLI loader.
Authentication You can choose to use optional authentication by adding the following to your D-Tale. When opening your D-Tale session you will be presented with a screen like this: From there you can enter the credentials you either set in your. Instance Settings Users can set front-end properties on their instances programmatically in the dtale. Anything else will be hidden. Predefined Filters Users can build their own custom filters which can be used from the front-end using the following code snippet: import pandas as pd import dtale import dtale.
Using Swifter Swifter is a package which will increase performance on any apply function on a pandas series or dataframe. Header The header gives users an idea of what operations have taken place on your data sorts, filters, hidden columns.
Clicking this will remove those operations. Here are some examples: Sorts Filters Hidden Columns. Resize Columns Currently there are two ways which you can resize columns. Editing Cells You may edit any cells in your grid with the exception of the row indexes or headers, the ladder can be edited using the Rename column menu function. It is as simple as selecting one or many columns as an index and then your dataframe will be converted to a dataset df. XArray Dimensions : If you are currently viewing data associated with an xarray.
You can select values for all, some or none all data - no filter of your coordinates and the data displayed in your grid will match your selections. Under the hood the code being executed is as follows: ds. Summarize Data This is very powerful functionality which allows users to create a new data from currently loaded data. The folowing screen shots are for a dataframe with the following data: Function Description Preview Remove Duplicate Columns Remove any columns that contain the same data as another and you can either keep the first, last or none of these columns that match this criteria.
Remove Duplicate Column Names Remove any columns with the same name name comparison is case-insensitive and you can either keep the first, last or none of these columns that match this criteria. Remove Duplicate Rows Remove any rows from your dataframe where the values of a subset of columns are considered duplicates. You can choose to keep the first, last or none of the rows considered duplicated. Show Duplicates Break any duplicate rows based on a subset of columns out into another dataframe viewable in your D-Tale session.
You can choose to view all duplicates or select specific groups based on the duplicated value. Missing Analysis Display charts analyzing the presence of missing NaN data in your dataset using the missingno pacakage.
Exporting Charts You can now export your dash charts with the exception of Wordclouds to static HTML files which can be emailed to others or saved down to be viewed at a later time. There are two courses of action in this situation: Restart your jupyter notebook kernel or python console Open a new D-Tale session on a different port than the current session.
You can do that with the following command: dtale. They are still available from the link in the upper-right corner, but on for a limited time… Here is the documentation for those: Legacy Charts Your Feedback is Valuable This is a very powerful feature with many more features that could be offered linked subplots, different statistical aggregations, etc… so please submit issues : Network Viewer This tool gives users the ability to visualize directed graphs.
Data Correlations. Predictive Power Score Predictive Power Score using the package ppscore is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two columns. Heat Map This will hide any non-float or non-int columns with the exception of the index on the right and apply a color to the background of each cell.
Each float is renormalized to be a value between 0 and 1. Highlight Dtypes This is a quick way to check and see if your data has been categorized correctly. Highlight Missing Any cells which contain nan values will be highlighted in yellow. Any string column cells which are empty strings or strings consisting only of spaces will be highlighted in orange. Highlight Outliers Highlight any cells for numeric columns which surpass the upper or lower bounds of a custom outlier computation.
Instances This will give you information about other D-Tale instances are running under your current Python process. For example, if you ran the following script: import pandas as pd import dtale dtale. Clicking that button while in the first instance invoked above will give you this popup: The grid above contains the following information: - Process: timestamp when the process was started along with the name if specified in dtale.
Refresh Widths Mostly a fail-safe in the event that your columns are no longer lining up. Light Dark. Reload Data Force a reload of the data from the server for the current rows being viewing in the grid by clicking this button. Shutdown Pretty self-explanatory, kills your D-Tale session there is also an auto-kill process that will kill your D-Tale after an hour of inactivity.
Delete As simple as it sounds, click this button to delete this column from your dataframe. Rename Update the name of any column in your dataframe to a name that is not currently in use by your dataframe.
Replacements This feature allows users to replace content on their column directly or for safer purposes in a brand new column. Formats Apply simple formats to numeric values in your grid Type Editing Result Numeric Date String For all data types you have the ability to change what string is ued for display.
Describe Column Analysis Based on the data type of a column different charts will be shown. Documentation Have a look at the detailed documentation. Acknowledgements D-Tale has been under active development at Man Numeric since Changelog 1. Project details Project links Homepage. Download files Download the file for your platform. Files for dtale, version 1.
Close Hashes for dtale If you have less than or equal to unique values they will be displayed at the bottom of your popup. Remove any columns that contain the same data as another and you can either keep the first, last or none of these columns that match this criteria.
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