%global _empty_manifest_terminate_build 0
Name:		python-pandas-aws
Version:	0.1.6
Release:	1
Summary:	please add a summary manually as the author left a blank one
License:	MIT
URL:		https://github.com/FlorentPajot/pandas-aws
Source0:	https://mirrors.aliyun.com/pypi/web/packages/bb/99/352369f0265066eeb3d222f312beb555df5b6676fa0a30d93e6edceeabe6/pandas-aws-0.1.6.tar.gz
BuildArch:	noarch

Requires:	python3-boto3
Requires:	python3-pandas
Requires:	python3-fastparquet
Requires:	python3-pyarrow
Requires:	python3-xlsxwriter
Requires:	python3-xlrd
Requires:	python3-psycopg2

%description
[![Build Status](https://travis-ci.com/FlorentPajot/pandas-aws.svg?branch=master)](https://travis-ci.com/FlorentPajot/pandas-aws) [![codecov](https://codecov.io/gh/FlorentPajot/pandas-aws/branch/master/graph/badge.svg)](https://codecov.io/gh/FlorentPajot/pandas-aws)

# Pandas AWS - AWS made easy for data scientists

Pandas AWS makes it super easy to use a pandas.DataFrame along with AWS services.

## Working with S3

First create an S3 client to be used later and define a bucket
```
from pandas_aws import get_client

s3 = get_client('s3')
MY_BUCKET= 'pandas-aws-bucket'
```
Example 1: get a DataFrame from a parquet file stored in S3
```
from pandas_aws.s3 import get_df

df_from_parquet_file = get_df(s3, MY_BUCKET, 'my_parquet_file_path', format='parquet')
```
Example 2: get a DataFrame from multiple CSV files (with same schema) stored in S3
```
from pandas_aws.s3 import get_df_from_keys

df_from_list = get_df_from_keys(s3, MY_BUCKET, prefix='my-folder', suffix='.csv')
```
Example 3: put a DataFrame into S3 using an xlsx (Excel) file format
```
from pandas_aws.s3 import put_df

put_df(s3, my_dataframe, MY_BUCKET, 'target_file_path', format='xlsx')
```
Example 4: put a DataFrame into S3 using multi parts upload
```
from pandas_aws.s3 import put_df

put_df(s3, my_dataframe, MY_BUCKET, 'target_file_path', format='csv', compression='gzip', parts=8)
```

# Installing pandas-aws

## Pip installation

You can use pip to download the package

`pip install pandas-aws`

# Contributing to pandas-aws

## Git clone

We use the `develop` brand as the release branch, thus `git clone` the repository and `git checkout develop` in order to get the latest version in development.

```
git clone git@github.com:FlorentPajot/pandas-aws.git
```

## Preparing your environment

Pandas AWS uses `poetry` to manage dependencies. Thus, `poetry` is required:

`curl -SSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python`

Create a separate Python environment, for example using `pyenv` along with `pyenv-virtualenv` and Python 3.7.7:

```
pyenv install 3.7.7
pyenv virtualenv 3.7.7 pandas-aws
pyenv activate pandas-aws
```

Check your environment using:
```
which python
// should show something like .pyenv/shims/python
python -V
// should show python 3.7.7 (or any other version you selected)
pip list
// should show barely nothing except pip and setuptools
```

In cas your encounter a problem, check `Pyenv` documentation.

Then install dependencies with poetry after your `git clone` from the project repository:

`poetry install`

## Guidelines

Todo

## Requires
The project needs the following dependencies:
- libpq-dev (psycopg2 dependency)


%package -n python3-pandas-aws
Summary:	please add a summary manually as the author left a blank one
Provides:	python-pandas-aws
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-pandas-aws
[![Build Status](https://travis-ci.com/FlorentPajot/pandas-aws.svg?branch=master)](https://travis-ci.com/FlorentPajot/pandas-aws) [![codecov](https://codecov.io/gh/FlorentPajot/pandas-aws/branch/master/graph/badge.svg)](https://codecov.io/gh/FlorentPajot/pandas-aws)

# Pandas AWS - AWS made easy for data scientists

Pandas AWS makes it super easy to use a pandas.DataFrame along with AWS services.

## Working with S3

First create an S3 client to be used later and define a bucket
```
from pandas_aws import get_client

s3 = get_client('s3')
MY_BUCKET= 'pandas-aws-bucket'
```
Example 1: get a DataFrame from a parquet file stored in S3
```
from pandas_aws.s3 import get_df

df_from_parquet_file = get_df(s3, MY_BUCKET, 'my_parquet_file_path', format='parquet')
```
Example 2: get a DataFrame from multiple CSV files (with same schema) stored in S3
```
from pandas_aws.s3 import get_df_from_keys

df_from_list = get_df_from_keys(s3, MY_BUCKET, prefix='my-folder', suffix='.csv')
```
Example 3: put a DataFrame into S3 using an xlsx (Excel) file format
```
from pandas_aws.s3 import put_df

put_df(s3, my_dataframe, MY_BUCKET, 'target_file_path', format='xlsx')
```
Example 4: put a DataFrame into S3 using multi parts upload
```
from pandas_aws.s3 import put_df

put_df(s3, my_dataframe, MY_BUCKET, 'target_file_path', format='csv', compression='gzip', parts=8)
```

# Installing pandas-aws

## Pip installation

You can use pip to download the package

`pip install pandas-aws`

# Contributing to pandas-aws

## Git clone

We use the `develop` brand as the release branch, thus `git clone` the repository and `git checkout develop` in order to get the latest version in development.

```
git clone git@github.com:FlorentPajot/pandas-aws.git
```

## Preparing your environment

Pandas AWS uses `poetry` to manage dependencies. Thus, `poetry` is required:

`curl -SSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python`

Create a separate Python environment, for example using `pyenv` along with `pyenv-virtualenv` and Python 3.7.7:

```
pyenv install 3.7.7
pyenv virtualenv 3.7.7 pandas-aws
pyenv activate pandas-aws
```

Check your environment using:
```
which python
// should show something like .pyenv/shims/python
python -V
// should show python 3.7.7 (or any other version you selected)
pip list
// should show barely nothing except pip and setuptools
```

In cas your encounter a problem, check `Pyenv` documentation.

Then install dependencies with poetry after your `git clone` from the project repository:

`poetry install`

## Guidelines

Todo

## Requires
The project needs the following dependencies:
- libpq-dev (psycopg2 dependency)


%package help
Summary:	Development documents and examples for pandas-aws
Provides:	python3-pandas-aws-doc
%description help
[![Build Status](https://travis-ci.com/FlorentPajot/pandas-aws.svg?branch=master)](https://travis-ci.com/FlorentPajot/pandas-aws) [![codecov](https://codecov.io/gh/FlorentPajot/pandas-aws/branch/master/graph/badge.svg)](https://codecov.io/gh/FlorentPajot/pandas-aws)

# Pandas AWS - AWS made easy for data scientists

Pandas AWS makes it super easy to use a pandas.DataFrame along with AWS services.

## Working with S3

First create an S3 client to be used later and define a bucket
```
from pandas_aws import get_client

s3 = get_client('s3')
MY_BUCKET= 'pandas-aws-bucket'
```
Example 1: get a DataFrame from a parquet file stored in S3
```
from pandas_aws.s3 import get_df

df_from_parquet_file = get_df(s3, MY_BUCKET, 'my_parquet_file_path', format='parquet')
```
Example 2: get a DataFrame from multiple CSV files (with same schema) stored in S3
```
from pandas_aws.s3 import get_df_from_keys

df_from_list = get_df_from_keys(s3, MY_BUCKET, prefix='my-folder', suffix='.csv')
```
Example 3: put a DataFrame into S3 using an xlsx (Excel) file format
```
from pandas_aws.s3 import put_df

put_df(s3, my_dataframe, MY_BUCKET, 'target_file_path', format='xlsx')
```
Example 4: put a DataFrame into S3 using multi parts upload
```
from pandas_aws.s3 import put_df

put_df(s3, my_dataframe, MY_BUCKET, 'target_file_path', format='csv', compression='gzip', parts=8)
```

# Installing pandas-aws

## Pip installation

You can use pip to download the package

`pip install pandas-aws`

# Contributing to pandas-aws

## Git clone

We use the `develop` brand as the release branch, thus `git clone` the repository and `git checkout develop` in order to get the latest version in development.

```
git clone git@github.com:FlorentPajot/pandas-aws.git
```

## Preparing your environment

Pandas AWS uses `poetry` to manage dependencies. Thus, `poetry` is required:

`curl -SSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python`

Create a separate Python environment, for example using `pyenv` along with `pyenv-virtualenv` and Python 3.7.7:

```
pyenv install 3.7.7
pyenv virtualenv 3.7.7 pandas-aws
pyenv activate pandas-aws
```

Check your environment using:
```
which python
// should show something like .pyenv/shims/python
python -V
// should show python 3.7.7 (or any other version you selected)
pip list
// should show barely nothing except pip and setuptools
```

In cas your encounter a problem, check `Pyenv` documentation.

Then install dependencies with poetry after your `git clone` from the project repository:

`poetry install`

## Guidelines

Todo

## Requires
The project needs the following dependencies:
- libpq-dev (psycopg2 dependency)


%prep
%autosetup -n pandas-aws-0.1.6

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-pandas-aws -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.6-1
- Package Spec generated