Django Docker Setup

23-04-2021

Django Docker Setup

Docker let you to do it with the help of port forwarding. First run docker ps -a, find your container id, usually the very first column and then run the command like docker port 8dbfe2506534 where 8dbfe2506534 is my container id. Even if you just do a docker ps -a you can see how a port is being. The Docker extension provides a docker debug configuration provider that manages how VS Code will launch an application and/or attach a debugger to the application in a running Docker container. This provider is configured via entries within launch.json, with configuration being specific to each application platform supported by the provider.

Latest version

Released:

Tools for working with pydata.pandas in your Django projects

Project description

Tools for working with pandas in your Djangoprojects

Contributors

What’s New

Version 0.6.4 supports the latest release of Pandas (1.2.1)

Dependencies

django-pandas supports Django (>=1.4.5) or laterand requires django-model-utils (>= 1.4.0) and Pandas (>= 0.12.0).Note because of problems with the requires directive of setuptoolsyou probably need to install numpy in your virtualenv before you installthis package or if you want to run the test suite

Some pandas functionality requires parts of the Scipy stack.You may wish to consult http://www.scipy.org/install.htmlfor more information on installing the Scipy stack.

You need to install your preferred version of Django.as that Django 2 does not support Python 2.

Contributing

Please file bugs and send pull requests to the GitHub repository and issuetracker.

Installation

Start by creating a new virtualenv for your project

Next install numpy and pandas and optionally scipy

You may want to consult the scipy documentation for more informationon installing the Scipy stack.

Finally, install django-pandas using pip:

or install the development version from github

Usage

IO Module

The django-pandas.io module provides some convenience methods tofacilitate the creation of DataFrames from Django QuerySets.

read_frame

Parameters

qs: A Django QuerySet.
fieldnames: A list of model field names to use in creating the DataFrame.
You can span a relationship in the usual Django wayby using double underscores to specify a related fieldin another model
index_col: Use specify the field name to use for the DataFrame index.
If the indexfield is not in the field list it will be appended
coerce_float :Boolean, defaults to True
Attempt to convert values to non-string,non-numeric objects (like decimal.Decimal)to floating point.
verbose: If this is True then populate the DataFrame with the
human readable versions of any foreign key or choice fieldselse use the actual values set in the model.
column_names: If not None, use to override the column names in the
DateFrame

Examples

Assume that this is your model:

First create a query set:

To create a dataframe using all the fields in the underlying model

The df will contain human readable column values for foreign key and choicefields. The DataFrame will include all the fields in the underlyingmodel including the primary key.To create a DataFrame using specified field names:

To set full_name as the DataFrame index

You can use filters and excludes

DataFrameManager

django-pandas provides a custom manager to use with models thatyou want to render as Pandas Dataframes. The DataFrameManagermanager provides the to_dataframe method that returnsyour models queryset as a Pandas DataFrame. To use the DataFrameManager, firstoverride the default manager (objects) in your model’s definitionas shown in the example below

This will give you access to the following QuerySet methods:

to_dataframe
to_timeseries
to_pivot_table

to_dataframe

Returns a DataFrame from the QuerySet

Parameters

fieldnames: The model field names to utilise in creating the frame.
to span a relationship, use the field name of relatedfields across models, separated by double underscores,
index: specify the field to use for the index. If the index
field is not in the field list it will be appended
coerce_float: Attempt to convert the numeric non-string data
like object, decimal etc. to float if possible
verbose: If this is True then populate the DataFrame with the
human readable versions of any foreign key or choice fieldselse use the actual value set in the model.

Examples

Create a dataframe using all the fields in your model as follows

This will include your primary key. To create a DataFrame using specifiedfield names:

To set full_name as the index

You can use filters and excludes

to_timeseries

A convenience method for creating a time series i.e theDataFrame index is instance of a DateTime or PeriodIndex

Parameters

fieldnames: The model field names to utilise in creating the frame.
to span a relationship, just use the field name of relatedfields across models, separated by double underscores,
index: specify the field to use for the index. If the index
field is not in the field list it will be appended. Thisis mandatory.
storage: Specify if the queryset uses the wide or long format
for data.
pivot_columns: Required once the you specify long format
storage. This could either be a list or string identifyingthe field name or combination of field. If the pivot_columnis a single column then the unique values in this column becomea new columns in the DataFrameIf the pivot column is a list the values in these columns areconcatenated (using the ‘-‘ as a separator)and these values are used for the new timeseries columns
values: Also required if you utilize the long storage the
values column name is use for populating new frame values
freq: the offset string or object representing a target conversion
rs_kwargs: Arguments based on pandas.DataFrame.resample
verbose: If this is True then populate the DataFrame with the
human readable versions of any foreign key or choice fieldselse use the actual value set in the model.

Examples

Using a long storage format