SKT Package
This is highly site dependent package. Resources are abstracted into package structure.
Usage
Hive metastore
from skt.ye import get_hms
c = get_hms()
c.get_partition_names("db", "table")
c.close()
Hash and unhash
from skt.lake import hash_s
from skt.lake import unhash_s
unhashed_list = ['0000000000']
hashed_list = hash_s(unhashed_list)
unhash_s(hashed_list)
Get pandas dataframe from parquet file in hdfs
from skt.ye import parquet_to_pandas
pandas_df = parquet_to_pandas(hdfs_path)
Save pandas dataframe as parquet in hdfs
from skt.ye import get_spark
from skt.ye import pandas_to_parquet
spark = get_spark()
pandas_to_parquet(pandas_df, hdfs_path, spark) # we need spark for this operation
spark.stop()
Work with spark
from skt.ye import get_spark
spark = get_spark()
# do with spark session
spark.stop()
Work with spark-bigquery-connector
# SELECT
from skt.gcp import bq_table_to_pandas
pandas_df = bq_table_to_pandas("dataset", "table_name", ["col_1", "col_2"], "2020-01-01", "svc_mgmt_num is not null")
# INSERT
from skt.gcp import pandas_to_bq_table
pandas_to_bq_table(pandas_df, "dataset", "table_name", "2020-03-01")
Send slack message
from skt.ye import slack_send
text = 'Hello'
username = 'airflow'
channel = '#leavemealone'
slack_send(text=text, username=username, channel=channel)
# Send dataframe as text
df = pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]})
slack_send(text=df, username=username, channel=channel, dataframe=True)
Get bigquery client
from skt.gcp import get_bigquery_client
bq = get_bigquery_client()
bq.query(query)
IPython BigQuery Magic
from skt.gcp import import_bigquery_ipython_magic
import_bigquery_ipython_magic()
query_params = {
"p_1": "v_1",
"dataset": "mnoai",
}
%% bq --params $query_params
SELECT c_1
FROM {dataset}.user_logs
WHERE c_1 = @p_1
Use NES CLI
nes input_notebook_url -p k1 v1 -p k2 v2 -p k3 v3
Installation
$ pip install skt --upgrade
If you would like to install submodules for AIR
$ pip install skt[air] --upgrade
Develop
Create an issue first, then open a merge request on GitLab https://gitlab.tde.sktelecom.com/AIDP/skt
AIPS EDA tools
OVERVIEW
1) EDA
1. Numeric variable EDA
- def numeric_eda_plot
Numeric feature에 대한 EDA Plot function
Args. :
- df : Pandas DataFrame 형태의 EDA대상 데이터
- feature_list : EDA 대상 feature list (df의 columns)
- label_col : Label(or Hue) column
- cols : Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
- n_samples : Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
- plot_type : density or box (default = 'density')
- stat_yn : 기초 통계량 출력여부 (mean / min / max / 1q / 3q) (default : False)
- figsize : (default : (7,4))
Returns :
matplotlib.pyplot object
Example :
fig = numeric_eda_plot(df, ['age'], 'answer', cols = 1, n_samples = 10000, plot_type='density', stat_yn=True, figsize = (7,4))
fig
if want to Save the EDA images,
fig.savefig('filename')
2. Categorical variable EDA
- def categorical_eda_plot
Categorical feature에 대한 EDA Plot function
Args. :
- df : Pandas DataFrame 형태의 EDA대상 데이터
- feature_list : EDA 대상 feature list (df의 columns)
- label_col : Label(or Hue) column
- cols : Multi-plot 시 grid column 개수 (row 개수는 feature_list에 따라 자동으로 결정 됨)
- n_samples : Label 별 sampling 할 개수 (default = -1(전수 데이터로 EDA할 경우))
- figsize : (default : (7,4))
Returns :
matplotlib.pyplot object
Example :
Example :
fig = categorical_eda_plot(df, ['sex_cd'], 'answer', cols = 1, n_samples = 10000, figsize = (7,4))
fig
if want to Save the EDA images,
fig.savefig('filename')
Release files for skt 1.0.22
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| skt-1.0.22.tar.gz | 28.0 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| skt-1.0.22-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 56.9 kB
Release files / skt-1.0.22.tar.gz
| Download URL | skt-1.0.22.tar.gz |
|---|---|
| Size | 28.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.5
|
Release files / skt-1.0.22-py3-none-any.whl
| Download URL | skt-1.0.22-py3-none-any.whl |
|---|---|
| Size | 29.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1398dd0bbcd70b4c448b7fb4fc62c6fd4216ce009ab58862650ba09e77dc1b6a
|
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.5
|
Security for skt 1.0.22
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