معرفی شرکت ها
bpd-0.1.2a1
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
bpd
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bpd-0.1.2a1 |
| نام | bpd |
| نسخه کتابخانه | 0.1.2a1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | CADIC Jean-Maximilien |
| ایمیل نویسنده | git@zakuro.ai |
| آدرس صفحه اصلی | https://github.com/zakuro-ai/bpd |
| آدرس اینترنتی | https://pypi.org/project/bpd/ |
| مجوز | MIT |
<h1 align="center">
<br>
<img src="https://drive.google.com/uc?id=1CV1tY4jcZDO4g_CLhGQK5VUN2q9SNsll" width="200">
<br>
bpd
<br>
</h1>
<p align="center">
<a href="#modules">Modules</a> •
<a href="#code-structure">Code structure</a> •
<a href="#installing-the-application">Installing the application</a> •
<a href="#makefile-commands">Makefile commands</a> •
<a href="#environments">Environments</a> •
<a href="#running-the-application">Running the application</a>•
<a href="#notebook">Notebook</a>•
<a href="#pipeline">Pipeline</a>•
<a href="#ressources">Ressources</a>
</p>
# Code structure
```python
from setuptools import setup
from bpd import __version__
setup(
name="bpd",
version=__version__,
short_description="bpd",
packages=[
"bpd",
"bpd.dask",
"bpd.dask.types",
"bpd.pandas",
"bpd.pyspark",
"bpd.pyspark.udf",
"bpd.tests",
],
long_description="".join(open("README.md", "r").readlines()),
long_description_content_type="text/markdown",
include_package_data=True,
package_data={"": ["*.yml"]},
url="https://github.com/zakuro-ai/bpd",
license="MIT",
author="CADIC Jean-Maximilien",
python_requires=">=3.6",
install_requires=[r.rsplit()[0] for r in open("requirements.txt")],
author_email="git@zakuro.ai",
description="bpd",
platforms="linux_debian_10_x86_64",
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
],
)
```
# Installing the application
To clone and run this application, you'll need the following installed on your computer:
- [Git](https://git-scm.com)
- Docker Desktop
- [Install Docker Desktop on Mac](https://docs.docker.com/docker-for-mac/install/)
- [Install Docker Desktop on Windows](https://docs.docker.com/desktop/install/windows-install/)
- [Install Docker Desktop on Linux](https://docs.docker.com/desktop/install/linux-install/)
- [Python](https://www.python.org/downloads/)
Install bpd:
```bash
# Clone this repository and install the code
git clone https://github.com/JeanMaximilienCadic/bpd
# Go into the repository
cd bpd
```
# Makefile commands
Exhaustive list of make commands:
```
install_wheels
sandbox_cpu
sandbox_gpu
build_sandbox
push_environment
push_container_sandbox
push_container_vanilla
pull_container_vanilla
pull_container_sandbox
build_vanilla
build_wheels
auto_branch
```
# Environments
## Docker
> **Note**
>
> Running this application by using Docker is recommended.
To build and run the docker image
```
make build
make sandbox
```
## PythonEnv
> **Warning**
>
> Running this application by using PythonEnv is possible but *not* recommended.
```
make install_wheels
```
# Running the application
```console
make tests
```
```
=1= TEST PASSED : bpd
=1= TEST PASSED : bpd.dask
=1= TEST PASSED : bpd.dask.types
=1= TEST PASSED : bpd.pandas
=1= TEST PASSED : bpd.pyspark
=1= TEST PASSED : bpd.pyspark.udf
=1= TEST PASSED : bpd.tests
+-----------+-------+-------------+-------------+-------+----+------------------------+---+-------+
|Pregnancies|Glucose|BloodPressure|SkinThickness|Insulin| BMI|DiabetesPedigreeFunction|Age|Outcome|
+-----------+-------+-------------+-------------+-------+----+------------------------+---+-------+
| 6| 148| 72| 35| 0|33.6| 0.627| 50| 1|
| 1| 85| 66| 29| 0|26.6| 0.351| 31| 0|
| 8| 183| 64| 0| 0|23.3| 0.672| 32| 1|
| 1| 89| 66| 23| 94|28.1| 0.167| 21| 0|
| 0| 137| 40| 35| 168|43.1| 2.288| 33| 1|
| 5| 116| 74| 0| 0|25.6| 0.201| 30| 0|
| 3| 78| 50| 32| 88| 31| 0.248| 26| 1|
| 10| 115| 0| 0| 0|35.3| 0.134| 29| 0|
| 2| 197| 70| 45| 543|30.5| 0.158| 53| 1|
| 8| 125| 96| 0| 0| 0| 0.232| 54| 1|
| 4| 110| 92| 0| 0|37.6| 0.191| 30| 0|
| 10| 168| 74| 0| 0| 38| 0.537| 34| 1|
| 10| 139| 80| 0| 0|27.1| 1.441| 57| 0|
| 1| 189| 60| 23| 846|30.1| 0.398| 59| 1|
| 5| 166| 72| 19| 175|25.8| 0.587| 51| 1|
| 7| 100| 0| 0| 0| 30| 0.484| 32| 1|
| 0| 118| 84| 47| 230|45.8| 0.551| 31| 1|
| 7| 107| 74| 0| 0|29.6| 0.254| 31| 1|
| 1| 103| 30| 38| 83|43.3| 0.183| 33| 0|
| 1| 115| 70| 30| 96|34.6| 0.529| 32| 1|
+-----------+-------+-------------+-------------+-------+----+------------------------+---+-------+
only showing top 20 rows
.
----------------------------------------------------------------------
Ran 1 test in 2.701s
OK
```
# Notebook
## Pipeline
```python
from gnutools import fs
from gnutools.remote import gdrivezip
from bpd import cfg
from bpd.dask import DataFrame, udf
from bpd.dask import functions as F
from bpd.dask.pipelines import *
```
```python
# Import a sample dataset
df = DataFrame({"filename": fs.listfiles(gdrivezip(cfg.gdrive.google_mini)[0], [".wav"])})
df.compute()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
</tr>
<tr>
<th>1</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
</tr>
<tr>
<th>2</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
</tr>
<tr>
<th>3</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/beb...</td>
</tr>
<tr>
<th>4</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/d37...</td>
</tr>
</tbody>
</table>
</div>
```python
# Register a user-defined function
@udf
def word(f):
return fs.name(fs.parent(f))
@udf
def initial(classe):
return classe[0]
@udf
def lists(classes):
return list(set(classes))
df.run_pipelines(
[
{
select_cols: ("filename",),
pipeline: (
("classe", word(F.col("filename"))),
("name", udf(fs.name)(F.col("filename"))),
),
},
{
group_on: "classe",
select_cols: ("name", ),
pipeline: (
("initial", initial(F.col("classe"))),
),
},
{
group_on: "initial",
select_cols: ("classe", ),
pipeline: (
("_initial", lists(F.col("classe"))),
),
},
]
)\
.withColumnRenamed("_initial", "initial")\
.compute()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
<th>initial</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
<td>[wow]</td>
</tr>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
<td>[wow]</td>
</tr>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
<td>[wow]</td>
</tr>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/beb...</td>
<td>[wow]</td>
</tr>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/d37...</td>
<td>[wow]</td>
</tr>
</tbody>
</table>
</div>
## Sequential calls
```python
from gnutools import fs
from bpd.dask import DataFrame, udf
from bpd.dask import functions as F
from gnutools.remote import gdrivezip
```
```python
# Import a sample dataset
gdrivezip("gdrive://1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE")
df = DataFrame({"filename": fs.listfiles("/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE", [".wav"])})
df.compute()
```
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
</tr>
<tr>
<th>1</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
</tr>
<tr>
<th>2</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
</tr>
<tr>
<th>3</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/beb...</td>
</tr>
<tr>
<th>4</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/d37...</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
</tr>
<tr>
<th>145</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/6a...</td>
</tr>
<tr>
<th>146</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e3...</td>
</tr>
<tr>
<th>147</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/68...</td>
</tr>
<tr>
<th>148</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e7...</td>
</tr>
<tr>
<th>149</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/65...</td>
</tr>
</tbody>
</table>
<p>150 rows × 1 columns</p>
</div>
```python
# Register a user-defined function
@udf
def word(f):
return fs.name(fs.parent(f))
# Apply a udf function
df\
.withColumn("classe", word(F.col("filename")))\
.compute()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
<th>classe</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
<td>wow</td>
</tr>
<tr>
<th>1</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
<td>wow</td>
</tr>
<tr>
<th>2</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
<td>wow</td>
</tr>
<tr>
<th>3</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/beb...</td>
<td>wow</td>
</tr>
<tr>
<th>4</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/d37...</td>
<td>wow</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>145</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/6a...</td>
<td>left</td>
</tr>
<tr>
<th>146</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e3...</td>
<td>left</td>
</tr>
<tr>
<th>147</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/68...</td>
<td>left</td>
</tr>
<tr>
<th>148</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e7...</td>
<td>left</td>
</tr>
<tr>
<th>149</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/65...</td>
<td>left</td>
</tr>
</tbody>
</table>
<p>150 rows × 2 columns</p>
</div>
```python
# You can use inline udf functions
df\
.withColumn("name", udf(fs.name)(F.col("filename")))\
.display()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
<th>name</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
<td>919d3c0e_nohash_2</td>
</tr>
<tr>
<th>1</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
<td>6a27a9bf_nohash_0</td>
</tr>
<tr>
<th>2</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
<td>6823565f_nohash_2</td>
</tr>
<tr>
<th>3</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/beb...</td>
<td>beb49c22_nohash_1</td>
</tr>
<tr>
<th>4</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/d37...</td>
<td>d37e4bf1_nohash_0</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>145</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/6a...</td>
<td>6a27a9bf_nohash_0</td>
</tr>
<tr>
<th>146</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e3...</td>
<td>e32ff49d_nohash_0</td>
</tr>
<tr>
<th>147</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/68...</td>
<td>6823565f_nohash_2</td>
</tr>
<tr>
<th>148</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e7...</td>
<td>e77d88fc_nohash_0</td>
</tr>
<tr>
<th>149</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/65...</td>
<td>659b7fae_nohash_2</td>
</tr>
</tbody>
</table>
<p>150 rows × 2 columns</p>
</div>
```python
# Retrieve the first 3 filename per classe
df\
.withColumn("classe", word(F.col("filename")))\
.aggregate("classe")\
.withColumn("filename", F.top_k(F.col("filename"), 3))\
.explode("filename")\
.compute()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
</tr>
<tr>
<th>classe</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>wow</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
</tr>
<tr>
<th>wow</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
</tr>
<tr>
<th>wow</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
</tr>
<tr>
<th>nine</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/nine/0f...</td>
</tr>
<tr>
<th>nine</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/nine/6a...</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
</tr>
<tr>
<th>yes</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/yes/0a9...</td>
</tr>
<tr>
<th>yes</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/yes/0a7...</td>
</tr>
<tr>
<th>left</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/6a...</td>
</tr>
<tr>
<th>left</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e3...</td>
</tr>
<tr>
<th>left</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/68...</td>
</tr>
</tbody>
</table>
<p>90 rows × 1 columns</p>
</div>
```python
# Add the classe column to the original dataframe
df = df\
.withColumn("classe", word(F.col("filename")))
# Display the modified dataframe
df.display()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
<th>classe</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
<td>wow</td>
</tr>
<tr>
<th>1</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
<td>wow</td>
</tr>
<tr>
<th>2</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
<td>wow</td>
</tr>
<tr>
<th>3</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/beb...</td>
<td>wow</td>
</tr>
<tr>
<th>4</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/d37...</td>
<td>wow</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>145</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/6a...</td>
<td>left</td>
</tr>
<tr>
<th>146</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e3...</td>
<td>left</td>
</tr>
<tr>
<th>147</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/68...</td>
<td>left</td>
</tr>
<tr>
<th>148</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e7...</td>
<td>left</td>
</tr>
<tr>
<th>149</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/65...</td>
<td>left</td>
</tr>
</tbody>
</table>
<p>150 rows × 2 columns</p>
</div>
```python
# Display the dataframe
# Retrieve the first 3 filename per classe
@udf
def initial(classe):
return classe[0]
_df = df\
.aggregate("classe")\
.reset_index(hard=False)\
.withColumn("initial", initial(F.col("classe")))\
.select(["classe", "initial"])\
.set_index("classe")
# Display the dataframe grouped by classe
_df.compute()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>initial</th>
</tr>
<tr>
<th>classe</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>bed</th>
<td>b</td>
</tr>
<tr>
<th>bird</th>
<td>b</td>
</tr>
<tr>
<th>cat</th>
<td>c</td>
</tr>
<tr>
<th>dog</th>
<td>d</td>
</tr>
<tr>
<th>down</th>
<td>d</td>
</tr>
<tr>
<th>eight</th>
<td>e</td>
</tr>
<tr>
<th>five</th>
<td>f</td>
</tr>
<tr>
<th>four</th>
<td>f</td>
</tr>
<tr>
<th>go</th>
<td>g</td>
</tr>
<tr>
<th>happy</th>
<td>h</td>
</tr>
<tr>
<th>house</th>
<td>h</td>
</tr>
<tr>
<th>left</th>
<td>l</td>
</tr>
<tr>
<th>marvin</th>
<td>m</td>
</tr>
<tr>
<th>nine</th>
<td>n</td>
</tr>
<tr>
<th>no</th>
<td>n</td>
</tr>
<tr>
<th>off</th>
<td>o</td>
</tr>
<tr>
<th>on</th>
<td>o</td>
</tr>
<tr>
<th>one</th>
<td>o</td>
</tr>
<tr>
<th>right</th>
<td>r</td>
</tr>
<tr>
<th>seven</th>
<td>s</td>
</tr>
<tr>
<th>sheila</th>
<td>s</td>
</tr>
<tr>
<th>six</th>
<td>s</td>
</tr>
<tr>
<th>stop</th>
<td>s</td>
</tr>
<tr>
<th>three</th>
<td>t</td>
</tr>
<tr>
<th>tree</th>
<td>t</td>
</tr>
<tr>
<th>two</th>
<td>t</td>
</tr>
<tr>
<th>up</th>
<td>u</td>
</tr>
<tr>
<th>wow</th>
<td>w</td>
</tr>
<tr>
<th>yes</th>
<td>y</td>
</tr>
<tr>
<th>zero</th>
<td>z</td>
</tr>
</tbody>
</table>
</div>
```python
_df_initial = _df.reset_index(hard=False).aggregate("initial")
_df_initial.compute()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>classe</th>
</tr>
<tr>
<th>initial</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>b</th>
<td>[bed, bird]</td>
</tr>
<tr>
<th>c</th>
<td>[cat]</td>
</tr>
<tr>
<th>d</th>
<td>[dog, down]</td>
</tr>
<tr>
<th>e</th>
<td>[eight]</td>
</tr>
<tr>
<th>f</th>
<td>[five, four]</td>
</tr>
<tr>
<th>g</th>
<td>[go]</td>
</tr>
<tr>
<th>h</th>
<td>[happy, house]</td>
</tr>
<tr>
<th>l</th>
<td>[left]</td>
</tr>
<tr>
<th>m</th>
<td>[marvin]</td>
</tr>
<tr>
<th>n</th>
<td>[nine, no]</td>
</tr>
<tr>
<th>o</th>
<td>[off, on, one]</td>
</tr>
<tr>
<th>r</th>
<td>[right]</td>
</tr>
<tr>
<th>s</th>
<td>[seven, sheila, six, stop]</td>
</tr>
<tr>
<th>t</th>
<td>[three, tree, two]</td>
</tr>
<tr>
<th>u</th>
<td>[up]</td>
</tr>
<tr>
<th>w</th>
<td>[wow]</td>
</tr>
<tr>
<th>y</th>
<td>[yes]</td>
</tr>
<tr>
<th>z</th>
<td>[zero]</td>
</tr>
</tbody>
</table>
</div>
```python
# Join the dataframes
df\
.join(_df, on="classe").drop_column("classe")\
.join(_df_initial, on="initial")\
.display()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>filename</th>
<th>initial</th>
<th>classe</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/919...</td>
<td>w</td>
<td>[wow]</td>
</tr>
<tr>
<th>1</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/6a2...</td>
<td>w</td>
<td>[wow]</td>
</tr>
<tr>
<th>2</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/682...</td>
<td>w</td>
<td>[wow]</td>
</tr>
<tr>
<th>3</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/beb...</td>
<td>w</td>
<td>[wow]</td>
</tr>
<tr>
<th>4</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/wow/d37...</td>
<td>w</td>
<td>[wow]</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>13</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/6a...</td>
<td>l</td>
<td>[left]</td>
</tr>
<tr>
<th>14</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e3...</td>
<td>l</td>
<td>[left]</td>
</tr>
<tr>
<th>15</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/68...</td>
<td>l</td>
<td>[left]</td>
</tr>
<tr>
<th>16</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/e7...</td>
<td>l</td>
<td>[left]</td>
</tr>
<tr>
<th>17</th>
<td>/tmp/1y4gwaS7LjYUhwTex1-lNHJJ71nLEh3fE/left/65...</td>
<td>l</td>
<td>[left]</td>
</tr>
</tbody>
</table>
<p>150 rows × 3 columns</p>
</div>
## Ressources
* Vanilla: https://en.wikipedia.org/wiki/Vanilla_software
* Sandbox: https://en.wikipedia.org/wiki/Sandbox_(software_development)
* All you need is docker: https://www.theregister.com/2014/05/23/google_containerization_two_billion/
* Dev in containers : https://code.visualstudio.com/docs/remote/containers
* Delta lake partitions: https://k21academy.com/microsoft-azure/data-engineer/delta-lake/
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | pandas |
| - | gnutools-python |
| - | tqdm |
| - | pyspark |
| - | distributed |
| - | jupyter |
| - | jupyterlab |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl bpd-0.1.2a1:
pip install bpd-0.1.2a1.whl
نصب پکیج tar.gz bpd-0.1.2a1:
pip install bpd-0.1.2a1.tar.gz