ahmedshahriar

# data - pandas dataframe
def missing_value_describe(data):
    # check missing values in the data
    total = data.isna().sum().sort_values(ascending=False)
    missing_value_pct_stats = (data.isnull().sum() / len(data)*100)
    missing_value_col_count = sum(missing_value_pct_stats > 0)

    # missing_value_stats = missing_value_pct_stats.sort_values(ascending=False)[:missing_value_col_count]
    missing_data = pd.concat([total, missing_value_pct_stats], axis=1, keys=['Total', 'Percent'])

ahmedshahriar

Icons

Avater pack
1. https://www.flaticon.com/packs/avatars-93

ahmedshahriar

"""
MongoDecimalField to work with mongdb decimal field using djongo
"""
from bson.decimal128 import Decimal128
from djongo.models import DecimalField


# https://github.com/nesdis/djongo/issues/82
# https://github.com/nesdis/djongo/issues/378

ahmedshahriar

[postgresql]
host=localhost
database=my_db
user=postgres
password=postgres

ahmedshahriar

import concurrent.futures


def test(li):
    # print('hello world')
    if li:
        print('hello world', li)


def run_all(li):

ahmedshahriar

See also:

• Free backend hosting
• https://free-for.dev/#/?id=dbaas

Service	Type	Limitations
Amazon DynamoDB	Proprietary NoSQL	25 GB of storage
Amazon RDS	Proprietary RDBMS	⚠️ Only free for 1 year
Azure SQL Database	MS SQL Server	⚠️ Only free for 1 year
Clever Cloud	PostgreSQL, MySQL, MongoDB, Redis	256 MB of storage (PostgreSQL)Max 5 connections (PostgreSQL)

ahmedshahriar

import pandas as pd
import glob

path = r'C:\DRO\DCL_rawdata_files' # use your path
all_files = glob.glob(path + "/*.csv")

li = []

for filename in all_files:
    df = pd.read_csv(filename, index_col=None, header=0) # to keep a single header

ahmedshahriar

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, StratifiedKFold
from sklearn.metrics         import accuracy_score

# configure the cross-validation procedure
kf = StratifiedKFold(n_splits = 10 , shuffle = True , random_state = 42)

def tune_hyperparameter(search_method, estimator, search_space):
    
    # enumerate splits
    outer_results = list()

ahmedshahriar

// google-scholar.svg
<svg height="2500" width="2500" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512">
  <g fill="none" fill-rule="evenodd"><path d="M256 411.12L0 202.667 256 0z" fill="#4285f4"/>
    <path d="M256 411.12l256-208.453L256 0z" fill="#356ac3"/>
    <circle cx="256" cy="362.667" fill="#a0c3ff" r="149.333"/>
    <path d="M121.037 298.667c23.968-50.453 75.392-85.334 134.963-85.334s110.995 34.881 134.963 85.334H121.037z" fill="#76a7fa"/>
  </g>
</svg>

// style.css

ahmedshahriar

#!/usr/bin/env python

# import csv
# reader = csv.reader(open('4956984.csv', 'rb'))

def gen_chunks(reader, chunksize=100):
    """ 
    Chunk generator. Take a CSV `reader` and yield
    `chunksize` sized slices. 
    """