yassineAlouini

1. Update the version in the setup.py file.

2. Run: python3 setup.py sdist bdist_wheel.

3. Run the following: python setup.py sdist upload -r pypi.

Notice that this last command is deprecated. Use twine instead.

yassineAlouini

import pandas as pd
import matplotlib.pylab as plt
import matplotlib.dates as mdates
hours = mdates.HourLocator(interval = 1)
h_d_fmt = mdates.DateFormatter('%d-%m %H:%M:%S')

DATA_PATH = "/path/to/your/data"
TMS_COL = "timestamp_column"
COL_TO_PLOT = "column_to_plot"

yassineAlouini

import pandas as pd

INPUT_PATH = "your/input/path.csv"
OUPUT_PATH = "your/output/path_{}.csv"


df = pd.read_csv(INPUT_PATH, parse_dates=['tms_gmt'])
df['year'] = df.tms_gmt.dt.year

for year in df['year'].unique():

yassineAlouini

import geopandas as gpd
import requests
def get_data_from_url(url):
    data = requets.get(url).json()
    return gpd.GeoDataFrame.from_features(data)

yassineAlouini

import pandas as pd
import matplotlib.pylab as plt
import seaborn as sns


# In the clipboard
# piece,price
# gpu,869
# ssd,140
# power,113

yassineAlouini

from hyperopt import tpe, fmin, Trials
from hyperopt.hp import normal
from hyperopt.plotting import main_plot_history, main_plot_histogram
import pandas as pd
import matplotlib.pylab as plt


def rosenbrock(suggestion):
    """ 
    A test function to minimize using hyperopt. The

yassineAlouini

# Extracted from this blog post: https://tech.marksblogg.com/install-and-configure-apache-airflow.html. 

import airflow
from airflow import models, settings
from airflow.contrib.auth.backends.password_auth import PasswordUser


user = PasswordUser(models.User())
user.username = 'username'
user.email    = '[email protected]'

yassineAlouini

# To get the segmentation_models library, run: 
# pip install segmentation-models


from segmentation_models import Unet


def build_pretrained_unet_model():
    """Build a pre-trained Unet model. """
    return Unet(backbone_name='resnet34', encoder_weights='imagenet')

yassineAlouini

# This function could be made generic to almost any loaded CSV file with
# pandas. Can you see how to do it?

import pandas as pd

# Some constants
PARQUET_ENGINE = "pyarrow"
DATE_COL = "purchase_date"
CATEGORICAL_COLS = ["card_id", "category_3", "merchant_id", "month_lag", 
                    "installments", "state_id", "subsector_id", 

yassineAlouini

SELECT t1.datname                                   AS db_name, 
       Pg_size_pretty(Pg_database_size(t1.datname)) AS db_size 
FROM   pg_database t1 
ORDER  BY Pg_database_size(t1.datname) DESC