ditat-etl 0.3.3

Multiple tools and utilities for ETL pipelines and others.

Ditat ETL

Multiple tools and utilities for ETL pipelines and others.

Utils

time_it

Decorator to time function and class method. Additional text can be added.

from ditat_etl.utils import time_it

@time_it()
def f():
  '''Do something'''
f()

f time: 0.1

Url

Extension of module requests/urllib3 for Proxy usage and Bulk usage.

Url

High-level usage

from ditat_etl import url

response = url.get('https://google.com')
# You can pass the same parameters as the library requests and other special parameters.

# Check low level usage for more details.

Low-level usage

from ditat_etl.url import Url

u = Url()

We use the logging module and it is set by default with 'DEBUG'. You can change this parameter to any allowed level

u = Url(debug_level='WARNING') # Just an example

Manage your proxies

u.add_proxies(n=3) # Added 3 new proxies (not necessarily valid) to self.proxies

u.clean_proxies() # Multithreaded to validate and keep only valid proxies.

print(u.proxies)
# You can also u.proxies = [], set them manually but this is not recommended.

Main functionality

def request(
    queue: str or list,
    expected_status_code: int=200,
    n_times: int=1,
    max_retries: int=None,
    use_proxy=False,
    _raise=True,
    ***kwargs
    ):

Examples

result = u.request('https://google.com')

result = u.request(queue=['https://google.com', 'htttps://facebook.com'], use_proxy=True)

# You can also pass optional parameter valid por a requests "Request"
import json
result = u.request(queue='https://example.com', method='post', data=json.dumps({'hello': 'world'}))

Databases

Useful wrappers for databases and methods to execute queries.

Postgres

It is compatible with pandas.DataFrame interaction, either reading as dataframes and pushing to the db.

from ditat_etl.databases import Postgres

config = {
    "database": "xxxx",
    "user": "xxxx",
    "password": "xxxx",
    "host": "xxxxx",
    "port": "xxxx"
}
p = Postgres(config)

The main base function is query.

p.query(
    query_statement: list or str,
    df: bool=False,
    as_dict: bool=False,
    commit: bool=True,
    returning: bool=True,
    mogrify: bool=False,
    mogrify_tuple: tuple or list=None,
    verbose=False
)

This function is a workaround of pandas.to_sql() which drops the table before inserting. It really works like an upsert and it gives you the option to do nothing or update on the column(s) constraint.

p.insert_df_to_sql(
    df: pd.DataFrame,
    tablename: str,
    commit=True,
    conflict_on: list=None,
    do_update_columns: bool or list=False,
    verbose=False
):

This one is similar, it lets you "upsert" without necessarily having a primary key or constraint. Ideally use the previous method.

p.update_df_to_sql(
    df: pd.DataFrame,
    tablename: str,
    on_columns: str or list,
    insert_new=True,
    commit=True,
    verbose=False
):