oliverangelil

import pandas as pd


def get_table_details(catalogs: list[str]):
  
    """
    Retrieve detailed information about tables across multiple catalogs and schemas, including row count and size statistics.

    This function uses Spark SQL to iterate over a list of catalogs, fetching details for each schema and table within 
    each catalog. For each table, it calculates the number of rows, the total size in bytes, and the average size per row 

oliverangelil

import os
import pandas as pd
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt

path_maize = '/home/siyabusa/terra/Forecast_ProcessedFiles/Outputs/Maize/'

files_maize = sorted([i for i in os.listdir(path_maize) if i.endswith('ec1.csv')])
names = []

oliverangelil

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**\input .tex
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oliverangelil

\documentclass{article}
\usepackage{fontawesome}

\begin{document}
\begin{center}
  \textbf{\faEnvelope John Smith}\\
\end{center}

\end{document}

oliverangelil

  mysql57 has the following notes:
    On activation if no /opt/local/etc/mysql57/my.cnf file exists one
    will be created which loads
    /opt/local/etc/mysql57/macports-default.cnf.
    
    If a /opt/local/etc/mysql57/my.cnf file exists MacPorts does not
    touch it and any changes you make to /opt/local/etc/mysql57/my.cnf
    will be preserved (e.g., during port upgrades, deactivations or
    activations). /opt/local/etc/mysql57/my.cnf is a good place to
    customize your mysql57 installation.

oliverangelil

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
import numpy as np
import matplotlib.pyplot as plt

# generate data
X, y, w = make_regression(n_samples=1000, n_features=200, coef=True,
                          random_state=1, bias=0, noise=3, tail_strength=0.9, effective_rank=10)