https://github.com/kiwi0fruit/featherhelper

Concise interface to cache numpy arrays and pandas dataframes
https://github.com/kiwi0fruit/featherhelper

cache numpy pandas pyarrow python

Last synced: about 1 month ago
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Concise interface to cache numpy arrays and pandas dataframes

• Host: GitHub
• URL: https://github.com/kiwi0fruit/featherhelper
• Owner: kiwi0fruit
• License: mit
• Created: 2018-12-12T08:49:42.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-01-22T08:50:13.000Z (almost 6 years ago)
• Last Synced: 2024-10-19T02:00:00.439Z (2 months ago)
• Topics: cache, numpy, pandas, pyarrow, python
• Language: Python
• Homepage:
• Size: 47.9 KB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Feather Helper

Feather Helper is a concise interface to cache and load numpy arrays and pandas dataframes. I use it with  [Pandoctools/Knitty](https://github.com/kiwi0fruit/pandoctools).

# Contents

* [Feather Helper](#feather-helper)

* [Contents](#contents)

* [Install](#install)

* [Usage example](#usage-example)

# Install

Via conda:

```

conda install -c defaults -c conda-forge featherhelper

```

Via pip:

```

pip install featherhelper

```

## Usage example

```py

import pandas as pd

import numpy as np

import featherhelper as fh

fh.setdir("~/feather/mydoc")  # (optional)

# fh.exc(1, 2)  # force raise exceptions for names (optional)

# %%

fh.name(1)  # can also be fh.name('id1'), default is 'default', 1 is the same as '1'

try:

    # raise fh.Err  # (optional)

    df, A, B = fh.pull()  # control length can be set: fh.pull(N)

except fh.Err:

    # calculate:

    print('push')  

    df = pd.DataFrame(np.random.random(16).reshape(4, 4))

    A = df.values

    B = np.random.random(16 * 3).reshape(4, 2, 2, 3)

    #

    fh.push(df, A, B)

print(df, '\n', A, '\n', B)

```

A shorter example:

```py

import numpy as np

import featherhelper as fh

# fh.exc()

# %%

try:

    A = fh.pull()

except fh.Err:

    A = np.random.random(16).reshape(4, 4)

    fh.push(A)

```