https://github.com/eurobios-mews-labs/crep

This simple module aims at providing some function to tackle tabular data that have a continuous axis. In situations, this index can represent time, but this tool was originally developed to tackle rail way description.
https://github.com/eurobios-mews-labs/crep

data pandas pandas-dataframe python python3 rails-application time-series

Last synced: 16 days ago
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This simple module aims at providing some function to tackle tabular data that have a continuous axis. In situations, this index can represent time, but this tool was originally developed to tackle rail way description.

• Host: GitHub
• URL: https://github.com/eurobios-mews-labs/crep
• Owner: eurobios-mews-labs
• License: other
• Created: 2023-07-04T17:04:02.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-29T09:00:44.000Z (2 months ago)
• Last Synced: 2024-10-29T09:54:35.961Z (2 months ago)
• Topics: data, pandas, pandas-dataframe, python, python3, rails-application, time-series
• Language: Python
• Homepage:
• Size: 2.57 MB
• Stars: 3
• Watchers: 2
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Tabular data processing for Continuous REPresentation

[![pytest](https://github.com/eurobios-scb/crep/actions/workflows/pytest.yml/badge.svg?event=push)](https://docs.pytest.org)

![code coverage](https://raw.githubusercontent.com/eurobios-mews-labs/crep/coverage-badge/coverage.svg?raw=true)

```python

pip install crep

``` 

This simple module aims at providing some function to tackle tabular 

data that have a continuous axis. In situations, this index can represent 

time, but this tool was originally developed to tackle rail way description.

This simple tools helps providing tools to represent a linear structure (cable, rail, beam, pipe) 

whose characteristics are piece-wize constant (of strongly heterogeneous length)

## Basic usage

* **Merge function** merge together two dataframe

```python

import pandas as pd

from crep import merge

df_left = pd.DataFrame(

    dict(id=[2, 2, 2],

         t1=[0, 100, 120],

         t2=[100, 120, 130],

         data1=[0.2, 0.1, 0.5])

)

df_right = pd.DataFrame(

    dict(id=[2, 2, 2],

         t1=[0, 80, 100],

         t2=[70, 100, 140],

         data2=[0.1, 0.3, 0.2])

)

ret = merge(data_left=df_left,

            data_right=df_right,

            id_continuous=["t1", "t2"],

            id_discrete=["id"],

            how="outer")

```

Yield the following result



## Tools

Test if your data is admissible to the merge function, you can use the tools module

```python

import pandas as pd

from crep import tools

df_admissible = pd.DataFrame(

    dict(id=[2, 2, 2],

         t1=[0, 100, 120],

         t2=[100, 120, 130],

         data1=[0.2, 0.1, 0.5])

)

df_not_admissible = pd.DataFrame(

    dict(id=[2, 2, 2],

         t1=[0, 90, 120],

         t2=[100, 120, 130],

         data1=[0.2, 0.1, 0.5])

)

# the second table is not admissible because for t in [90,100] two values are possible

assert tools.admissible_dataframe(

    df_admissible, id_continuous=["t1", "t2"],

    id_discrete=["id"])

assert not tools.admissible_dataframe(

    df_not_admissible, id_continuous=["t1", "t2"],

    id_discrete=["id"])

print(tools.sample_non_admissible_data(

    df_not_admissible, id_continuous=["t1", "t2"],

    id_discrete=["id"]

))

# id  t1   t2  data1

# 1   2  90  120    0.1

```

 

## Acknowledgement

This implementation come from an SNCF DTIPG project and is

developed and maintained by Mews Labs and SNCF DTIPG.