%global _empty_manifest_terminate_build 0
Name: python-verticapy
Version: 0.12.0
Release: 1
Summary: VerticaPy simplifies data exploration, data cleaning, and machine learning in Vertica.
License: Apache Software License
URL: https://github.com/vertica/VerticaPy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/85/8c/bc3f23f356017e0d3a8e4a142c8559741ea9fc25d80bf3fafd245fc56afa/verticapy-0.12.0.tar.gz
BuildArch: noarch
Requires: python3-matplotlib
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-highcharts
Requires: python3-scipy
Requires: python3-tqdm
Requires: python3-vertica-python
Requires: python3-descartes
Requires: python3-geopandas
Requires: python3-graphviz
Requires: python3-shapely
%description
:star: 2022-12-01: VerticaPy secures 100 stars.
:loudspeaker: 2020-06-27: Vertica-ML-Python has been renamed to VerticaPy.
:warning: VerticaPy 0.9.0 includes several significant changes and is therefore not backward compatible with older versions. For details, see the changelog.
# VerticaPy
[](https://badge.fury.io/py/verticapy)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/downloads/)
[](https://codecov.io/gh/vertica/VerticaPy)
VerticaPy is a Python library with scikit-like functionality used to conduct data science projects on data stored in Vertica, taking advantage Vertica’s speed and built-in analytics and machine learning features. VerticaPy offers robust support for the entire data science life cycle, uses a 'pipeline' mechanism to sequentialize data transformation operations, and offers beautiful graphical options.
Nowadays, 'Big Data' is one of the main topics in the data science world, and data scientists are often at the center of any organization. The benefits of becoming more data-driven are undeniable and are often needed to survive in the industry.
Vertica was the first real analytic columnar database and is still the fastest in the market. However, SQL alone isn't flexible enough to meet the needs of data scientists.
Python has quickly become the most popular tool in this domain, owing much of its flexibility to its high-level of abstraction and impressively large and ever-growing set of libraries. Its accessibility has led to the development of popular and perfomant APIs, like pandas and scikit-learn, and a dedicated community of data scientists. Unfortunately, Python only works in-memory as a single-node process. This problem has led to the rise of distributed programming languages, but they too, are limited as in-memory processes and, as such, will never be able to process all of your data in this era, and moving data for processing is prohobitively expensive. On top of all of this, data scientists must also find convenient ways to deploy their data and models. The whole process is time consuming.
**VerticaPy aims to solve all of these problems**. The idea is simple: instead of moving data around for processing, VerticaPy brings the logic to the data.
3 years in the making, we're proud to bring you VerticaPy.
Main Advantages:
- Easy Data Exploration.
- Fast Data Preparation.
- In-Database Machine Learning.
- Easy Model Evaluation.
- Easy Model Deployment.
## Installation
To install VerticaPy with pip:
```shell
# Latest release version
root@ubuntu:~$ pip3 install verticapy[all]
# Latest commit on master branch
root@ubuntu:~$ pip3 install git+https://github.com/vertica/verticapy.git@master
```
To install VerticaPy from source, run the following command from the root directory:
```shell
root@ubuntu:~$ python3 setup.py install
```
A detailed installation guide is available at:
https://www.vertica.com/python/installation.php
## Documentation
Documentation is available at:
https://www.vertica.com/python/documentation_last/
## Use-cases
Examples and case-studies:
https://www.vertica.com/python/examples/
## SQL Magic
You can use VerticaPy to execute SQL queries directly from a Jupyter notebook. For details, see SQL Magic:
### Example
Load the SQL extension.
```python
%load_ext verticapy.sql
```
Execute your SQL queries.
```python
%%sql
SELECT version();
# Output
# Vertica Analytic Database v11.0.1-0
```
## Charts
A gallery of VerticaPy-generated charts is available at:
https://www.vertica.com/python/gallery/
## Contributing
For a short guide on contribution standards, see CONTRIBUTING.md
## Connecting to the Database
VerticaPy is compatible with several clients. For details, see the connection page.
## Quickstart
The following example follows the VerticaPy quickstart guide.
Install the library using with pip.
```shell
root@ubuntu:~$ pip3 install verticapy[all]
```
Create a new Vertica connection:
```python
import verticapy as vp
vp.new_connection({"host": "10.211.55.14",
"port": "5433",
"database": "testdb",
"password": "XxX",
"user": "dbadmin"},
name = "Vertica_New_Connection")
```
Use the newly created connection:
```python
vp.connect("Vertica_New_Connection")
```
Create a VerticaPy schema for native VerticaPy models (that is, models available in VerticaPy, but not Vertica itself):
```python
vp.create_verticapy_schema()
```
Create a vDataFrame of your relation:
```python
from verticapy import vDataFrame
vdf = vDataFrame("my_relation")
```
Load a sample dataset:
```python
from verticapy.datasets import load_titanic
vdf = load_titanic()
```
Examine your data:
```python
vdf.describe()
# Output
count mean std min
"pclass" 1234 2.28444084278768 0.842485636190292 1.0
"survived" 1234 0.364667747163696 0.481532018641288 0.0
"age" 997 30.1524573721163 14.4353046299159 0.33
"sibsp" 1234 0.504051863857374 1.04111727241629 0.0
"parch" 1234 0.378444084278768 0.868604707790393 0.0
"fare" 1233 33.963793673966 52.6460729831293 0.0
"body" 118 164.14406779661 96.5760207557808 1.0
approx_25% approx_50% approx_75% max
"pclass" 1.0 3.0 3.0 3.0
"survived" 0.0 0.0 1.0 1.0
"age" 21.0 28.0 39.0 80.0
"sibsp" 0.0 0.0 1.0 8.0
"parch" 0.0 0.0 0.0 9.0
"fare" 7.8958 14.4542 31.3875 512.3292
"body" 79.25 160.5 257.5 328.0
Rows: 1-7 | Columns: 9
```
Print the SQL query with set_option:
```python
set_option("sql_on", True)
vdf.describe()
# Output
## Compute the descriptive statistics of all the numerical columns ##
SELECT
SUMMARIZE_NUMCOL("pclass", "survived", "age", "sibsp", "parch", "fare", "body") OVER ()
FROM public.titanic
```
With VerticaPy, it is now possible to solve a ML problem with few lines of code.
```python
from verticapy.learn.model_selection import cross_validate
from verticapy.learn.ensemble import RandomForestClassifier
# Data Preparation
vdf["sex"].label_encode()["boat"].fillna(method = "0ifnull")["name"].str_extract(' ([A-Za-z]+)\.').eval("family_size", expr = "parch + sibsp + 1").drop(columns = ["cabin", "body", "ticket", "home.dest"])["fare"].fill_outliers().fillna()
# Model Evaluation
cross_validate(RandomForestClassifier("rf_titanic", cur, max_leaf_nodes = 100, n_estimators = 30),
vdf,
["age", "family_size", "sex", "pclass", "fare", "boat"],
"survived",
cutoff = 0.35)
# Output
auc prc_auc
1-fold 0.9877114427860691 0.9530465915039339
2-fold 0.9965555014605642 0.7676485351425721
3-fold 0.9927239216549301 0.6419135521132449
avg 0.992330288634 0.787536226253
std 0.00362128464093 0.12779562393
accuracy log_loss
1-fold 0.971291866028708 0.0502052541223871
2-fold 0.983253588516746 0.0298167751798457
3-fold 0.964824120603015 0.0392745694400433
avg 0.973123191716 0.0397655329141
std 0.0076344236729 0.00833079837099
precision recall
1-fold 0.96 0.96
2-fold 0.9556962025316456 1.0
3-fold 0.9647887323943662 0.9383561643835616
avg 0.960161644975 0.966118721461
std 0.00371376912311 0.025535200301
f1-score mcc
1-fold 0.9687259282082884 0.9376119402985075
2-fold 0.9867172675521821 0.9646971010878469
3-fold 0.9588020287309097 0.9240569687684576
avg 0.97141507483 0.942122003385
std 0.0115538960753 0.0168949813163
informedness markedness
1-fold 0.9376119402985075 0.9376119402985075
2-fold 0.9737827715355807 0.9556962025316456
3-fold 0.9185148945422918 0.9296324823943662
avg 0.943303202125 0.940980208408
std 0.0229190954261 0.0109037699717
csi
1-fold 0.9230769230769231
2-fold 0.9556962025316456
3-fold 0.9072847682119205
avg 0.928685964607
std 0.0201579224026
```
Enjoy!
%package -n python3-verticapy
Summary: VerticaPy simplifies data exploration, data cleaning, and machine learning in Vertica.
Provides: python-verticapy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-verticapy
:star: 2022-12-01: VerticaPy secures 100 stars.
:loudspeaker: 2020-06-27: Vertica-ML-Python has been renamed to VerticaPy.
:warning: VerticaPy 0.9.0 includes several significant changes and is therefore not backward compatible with older versions. For details, see the changelog.
# VerticaPy
[](https://badge.fury.io/py/verticapy)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/downloads/)
[](https://codecov.io/gh/vertica/VerticaPy)
VerticaPy is a Python library with scikit-like functionality used to conduct data science projects on data stored in Vertica, taking advantage Vertica’s speed and built-in analytics and machine learning features. VerticaPy offers robust support for the entire data science life cycle, uses a 'pipeline' mechanism to sequentialize data transformation operations, and offers beautiful graphical options.
Nowadays, 'Big Data' is one of the main topics in the data science world, and data scientists are often at the center of any organization. The benefits of becoming more data-driven are undeniable and are often needed to survive in the industry.
Vertica was the first real analytic columnar database and is still the fastest in the market. However, SQL alone isn't flexible enough to meet the needs of data scientists.
Python has quickly become the most popular tool in this domain, owing much of its flexibility to its high-level of abstraction and impressively large and ever-growing set of libraries. Its accessibility has led to the development of popular and perfomant APIs, like pandas and scikit-learn, and a dedicated community of data scientists. Unfortunately, Python only works in-memory as a single-node process. This problem has led to the rise of distributed programming languages, but they too, are limited as in-memory processes and, as such, will never be able to process all of your data in this era, and moving data for processing is prohobitively expensive. On top of all of this, data scientists must also find convenient ways to deploy their data and models. The whole process is time consuming.
**VerticaPy aims to solve all of these problems**. The idea is simple: instead of moving data around for processing, VerticaPy brings the logic to the data.
3 years in the making, we're proud to bring you VerticaPy.
Main Advantages:
- Easy Data Exploration.
- Fast Data Preparation.
- In-Database Machine Learning.
- Easy Model Evaluation.
- Easy Model Deployment.
## Installation
To install VerticaPy with pip:
```shell
# Latest release version
root@ubuntu:~$ pip3 install verticapy[all]
# Latest commit on master branch
root@ubuntu:~$ pip3 install git+https://github.com/vertica/verticapy.git@master
```
To install VerticaPy from source, run the following command from the root directory:
```shell
root@ubuntu:~$ python3 setup.py install
```
A detailed installation guide is available at:
https://www.vertica.com/python/installation.php
## Documentation
Documentation is available at:
https://www.vertica.com/python/documentation_last/
## Use-cases
Examples and case-studies:
https://www.vertica.com/python/examples/
## SQL Magic
You can use VerticaPy to execute SQL queries directly from a Jupyter notebook. For details, see SQL Magic:
### Example
Load the SQL extension.
```python
%load_ext verticapy.sql
```
Execute your SQL queries.
```python
%%sql
SELECT version();
# Output
# Vertica Analytic Database v11.0.1-0
```
## Charts
A gallery of VerticaPy-generated charts is available at:
https://www.vertica.com/python/gallery/
## Contributing
For a short guide on contribution standards, see CONTRIBUTING.md
## Connecting to the Database
VerticaPy is compatible with several clients. For details, see the connection page.
## Quickstart
The following example follows the VerticaPy quickstart guide.
Install the library using with pip.
```shell
root@ubuntu:~$ pip3 install verticapy[all]
```
Create a new Vertica connection:
```python
import verticapy as vp
vp.new_connection({"host": "10.211.55.14",
"port": "5433",
"database": "testdb",
"password": "XxX",
"user": "dbadmin"},
name = "Vertica_New_Connection")
```
Use the newly created connection:
```python
vp.connect("Vertica_New_Connection")
```
Create a VerticaPy schema for native VerticaPy models (that is, models available in VerticaPy, but not Vertica itself):
```python
vp.create_verticapy_schema()
```
Create a vDataFrame of your relation:
```python
from verticapy import vDataFrame
vdf = vDataFrame("my_relation")
```
Load a sample dataset:
```python
from verticapy.datasets import load_titanic
vdf = load_titanic()
```
Examine your data:
```python
vdf.describe()
# Output
count mean std min
"pclass" 1234 2.28444084278768 0.842485636190292 1.0
"survived" 1234 0.364667747163696 0.481532018641288 0.0
"age" 997 30.1524573721163 14.4353046299159 0.33
"sibsp" 1234 0.504051863857374 1.04111727241629 0.0
"parch" 1234 0.378444084278768 0.868604707790393 0.0
"fare" 1233 33.963793673966 52.6460729831293 0.0
"body" 118 164.14406779661 96.5760207557808 1.0
approx_25% approx_50% approx_75% max
"pclass" 1.0 3.0 3.0 3.0
"survived" 0.0 0.0 1.0 1.0
"age" 21.0 28.0 39.0 80.0
"sibsp" 0.0 0.0 1.0 8.0
"parch" 0.0 0.0 0.0 9.0
"fare" 7.8958 14.4542 31.3875 512.3292
"body" 79.25 160.5 257.5 328.0
Rows: 1-7 | Columns: 9
```
Print the SQL query with set_option:
```python
set_option("sql_on", True)
vdf.describe()
# Output
## Compute the descriptive statistics of all the numerical columns ##
SELECT
SUMMARIZE_NUMCOL("pclass", "survived", "age", "sibsp", "parch", "fare", "body") OVER ()
FROM public.titanic
```
With VerticaPy, it is now possible to solve a ML problem with few lines of code.
```python
from verticapy.learn.model_selection import cross_validate
from verticapy.learn.ensemble import RandomForestClassifier
# Data Preparation
vdf["sex"].label_encode()["boat"].fillna(method = "0ifnull")["name"].str_extract(' ([A-Za-z]+)\.').eval("family_size", expr = "parch + sibsp + 1").drop(columns = ["cabin", "body", "ticket", "home.dest"])["fare"].fill_outliers().fillna()
# Model Evaluation
cross_validate(RandomForestClassifier("rf_titanic", cur, max_leaf_nodes = 100, n_estimators = 30),
vdf,
["age", "family_size", "sex", "pclass", "fare", "boat"],
"survived",
cutoff = 0.35)
# Output
auc prc_auc
1-fold 0.9877114427860691 0.9530465915039339
2-fold 0.9965555014605642 0.7676485351425721
3-fold 0.9927239216549301 0.6419135521132449
avg 0.992330288634 0.787536226253
std 0.00362128464093 0.12779562393
accuracy log_loss
1-fold 0.971291866028708 0.0502052541223871
2-fold 0.983253588516746 0.0298167751798457
3-fold 0.964824120603015 0.0392745694400433
avg 0.973123191716 0.0397655329141
std 0.0076344236729 0.00833079837099
precision recall
1-fold 0.96 0.96
2-fold 0.9556962025316456 1.0
3-fold 0.9647887323943662 0.9383561643835616
avg 0.960161644975 0.966118721461
std 0.00371376912311 0.025535200301
f1-score mcc
1-fold 0.9687259282082884 0.9376119402985075
2-fold 0.9867172675521821 0.9646971010878469
3-fold 0.9588020287309097 0.9240569687684576
avg 0.97141507483 0.942122003385
std 0.0115538960753 0.0168949813163
informedness markedness
1-fold 0.9376119402985075 0.9376119402985075
2-fold 0.9737827715355807 0.9556962025316456
3-fold 0.9185148945422918 0.9296324823943662
avg 0.943303202125 0.940980208408
std 0.0229190954261 0.0109037699717
csi
1-fold 0.9230769230769231
2-fold 0.9556962025316456
3-fold 0.9072847682119205
avg 0.928685964607
std 0.0201579224026
```
Enjoy!
%package help
Summary: Development documents and examples for verticapy
Provides: python3-verticapy-doc
%description help
:star: 2022-12-01: VerticaPy secures 100 stars.
:loudspeaker: 2020-06-27: Vertica-ML-Python has been renamed to VerticaPy.
:warning: VerticaPy 0.9.0 includes several significant changes and is therefore not backward compatible with older versions. For details, see the changelog.
# VerticaPy
[](https://badge.fury.io/py/verticapy)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/downloads/)
[](https://codecov.io/gh/vertica/VerticaPy)
VerticaPy is a Python library with scikit-like functionality used to conduct data science projects on data stored in Vertica, taking advantage Vertica’s speed and built-in analytics and machine learning features. VerticaPy offers robust support for the entire data science life cycle, uses a 'pipeline' mechanism to sequentialize data transformation operations, and offers beautiful graphical options.
Nowadays, 'Big Data' is one of the main topics in the data science world, and data scientists are often at the center of any organization. The benefits of becoming more data-driven are undeniable and are often needed to survive in the industry.
Vertica was the first real analytic columnar database and is still the fastest in the market. However, SQL alone isn't flexible enough to meet the needs of data scientists.
Python has quickly become the most popular tool in this domain, owing much of its flexibility to its high-level of abstraction and impressively large and ever-growing set of libraries. Its accessibility has led to the development of popular and perfomant APIs, like pandas and scikit-learn, and a dedicated community of data scientists. Unfortunately, Python only works in-memory as a single-node process. This problem has led to the rise of distributed programming languages, but they too, are limited as in-memory processes and, as such, will never be able to process all of your data in this era, and moving data for processing is prohobitively expensive. On top of all of this, data scientists must also find convenient ways to deploy their data and models. The whole process is time consuming.
**VerticaPy aims to solve all of these problems**. The idea is simple: instead of moving data around for processing, VerticaPy brings the logic to the data.
3 years in the making, we're proud to bring you VerticaPy.
Main Advantages:
- Easy Data Exploration.
- Fast Data Preparation.
- In-Database Machine Learning.
- Easy Model Evaluation.
- Easy Model Deployment.
## Installation
To install VerticaPy with pip:
```shell
# Latest release version
root@ubuntu:~$ pip3 install verticapy[all]
# Latest commit on master branch
root@ubuntu:~$ pip3 install git+https://github.com/vertica/verticapy.git@master
```
To install VerticaPy from source, run the following command from the root directory:
```shell
root@ubuntu:~$ python3 setup.py install
```
A detailed installation guide is available at:
https://www.vertica.com/python/installation.php
## Documentation
Documentation is available at:
https://www.vertica.com/python/documentation_last/
## Use-cases
Examples and case-studies:
https://www.vertica.com/python/examples/
## SQL Magic
You can use VerticaPy to execute SQL queries directly from a Jupyter notebook. For details, see SQL Magic:
### Example
Load the SQL extension.
```python
%load_ext verticapy.sql
```
Execute your SQL queries.
```python
%%sql
SELECT version();
# Output
# Vertica Analytic Database v11.0.1-0
```
## Charts
A gallery of VerticaPy-generated charts is available at:
https://www.vertica.com/python/gallery/
## Contributing
For a short guide on contribution standards, see CONTRIBUTING.md
## Connecting to the Database
VerticaPy is compatible with several clients. For details, see the connection page.
## Quickstart
The following example follows the VerticaPy quickstart guide.
Install the library using with pip.
```shell
root@ubuntu:~$ pip3 install verticapy[all]
```
Create a new Vertica connection:
```python
import verticapy as vp
vp.new_connection({"host": "10.211.55.14",
"port": "5433",
"database": "testdb",
"password": "XxX",
"user": "dbadmin"},
name = "Vertica_New_Connection")
```
Use the newly created connection:
```python
vp.connect("Vertica_New_Connection")
```
Create a VerticaPy schema for native VerticaPy models (that is, models available in VerticaPy, but not Vertica itself):
```python
vp.create_verticapy_schema()
```
Create a vDataFrame of your relation:
```python
from verticapy import vDataFrame
vdf = vDataFrame("my_relation")
```
Load a sample dataset:
```python
from verticapy.datasets import load_titanic
vdf = load_titanic()
```
Examine your data:
```python
vdf.describe()
# Output
count mean std min
"pclass" 1234 2.28444084278768 0.842485636190292 1.0
"survived" 1234 0.364667747163696 0.481532018641288 0.0
"age" 997 30.1524573721163 14.4353046299159 0.33
"sibsp" 1234 0.504051863857374 1.04111727241629 0.0
"parch" 1234 0.378444084278768 0.868604707790393 0.0
"fare" 1233 33.963793673966 52.6460729831293 0.0
"body" 118 164.14406779661 96.5760207557808 1.0
approx_25% approx_50% approx_75% max
"pclass" 1.0 3.0 3.0 3.0
"survived" 0.0 0.0 1.0 1.0
"age" 21.0 28.0 39.0 80.0
"sibsp" 0.0 0.0 1.0 8.0
"parch" 0.0 0.0 0.0 9.0
"fare" 7.8958 14.4542 31.3875 512.3292
"body" 79.25 160.5 257.5 328.0
Rows: 1-7 | Columns: 9
```
Print the SQL query with set_option:
```python
set_option("sql_on", True)
vdf.describe()
# Output
## Compute the descriptive statistics of all the numerical columns ##
SELECT
SUMMARIZE_NUMCOL("pclass", "survived", "age", "sibsp", "parch", "fare", "body") OVER ()
FROM public.titanic
```
With VerticaPy, it is now possible to solve a ML problem with few lines of code.
```python
from verticapy.learn.model_selection import cross_validate
from verticapy.learn.ensemble import RandomForestClassifier
# Data Preparation
vdf["sex"].label_encode()["boat"].fillna(method = "0ifnull")["name"].str_extract(' ([A-Za-z]+)\.').eval("family_size", expr = "parch + sibsp + 1").drop(columns = ["cabin", "body", "ticket", "home.dest"])["fare"].fill_outliers().fillna()
# Model Evaluation
cross_validate(RandomForestClassifier("rf_titanic", cur, max_leaf_nodes = 100, n_estimators = 30),
vdf,
["age", "family_size", "sex", "pclass", "fare", "boat"],
"survived",
cutoff = 0.35)
# Output
auc prc_auc
1-fold 0.9877114427860691 0.9530465915039339
2-fold 0.9965555014605642 0.7676485351425721
3-fold 0.9927239216549301 0.6419135521132449
avg 0.992330288634 0.787536226253
std 0.00362128464093 0.12779562393
accuracy log_loss
1-fold 0.971291866028708 0.0502052541223871
2-fold 0.983253588516746 0.0298167751798457
3-fold 0.964824120603015 0.0392745694400433
avg 0.973123191716 0.0397655329141
std 0.0076344236729 0.00833079837099
precision recall
1-fold 0.96 0.96
2-fold 0.9556962025316456 1.0
3-fold 0.9647887323943662 0.9383561643835616
avg 0.960161644975 0.966118721461
std 0.00371376912311 0.025535200301
f1-score mcc
1-fold 0.9687259282082884 0.9376119402985075
2-fold 0.9867172675521821 0.9646971010878469
3-fold 0.9588020287309097 0.9240569687684576
avg 0.97141507483 0.942122003385
std 0.0115538960753 0.0168949813163
informedness markedness
1-fold 0.9376119402985075 0.9376119402985075
2-fold 0.9737827715355807 0.9556962025316456
3-fold 0.9185148945422918 0.9296324823943662
avg 0.943303202125 0.940980208408
std 0.0229190954261 0.0109037699717
csi
1-fold 0.9230769230769231
2-fold 0.9556962025316456
3-fold 0.9072847682119205
avg 0.928685964607
std 0.0201579224026
```
Enjoy!
%prep
%autosetup -n verticapy-0.12.0
%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-verticapy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot - 0.12.0-1
- Package Spec generated