franc3000

# prompt
# export PS1="\u [\T]$ "
# export PS1="[\u]\$PWD$"
export PS1="\[\033[38;5;6m\][\w]\[$(tput sgr0)\]\[\033[38;5;15m\] \T\[$(tput sgr0)\]\[\033[38;5;6m\]\[$(tput sgr0)\]\[\0
33[38;5;15m\] \\$\[$(tput sgr0)\] "
# export PS1="___________________    | \w @ \h (\u) \n| => "
# export PS2="| => "


alias ll='ls -FGlAhp'

franc3000

# prompt
# export PS1="\u [\T]$ "
# export PS1="[\u]\$PWD$"
export PS1="\[\033[38;5;6m\][\w]\[$(tput sgr0)\]\[\033[38;5;15m\] \T\[$(tput sgr0)\]\[\033[38;5;6m\]\[$(tput sgr0)\]\[\0
33[38;5;15m\] \\$\[$(tput sgr0)\] "
# export PS1="___________________    | \w @ \h (\u) \n| => "
# export PS2="| => "


alias ll='ls -FGlAhp'

franc3000

SELECT 
     table_schema, 
     table_name, 
     round(((data_length + index_length) / 1024 / 1024), 2) `Size in MB`,
     sum(table_rows) Rows
FROM information_schema.TABLES 
WHERE table_schema='paretodb'
GROUP BY 1,2
ORDER BY (data_length + index_length) DESC
LIMIT 100

franc3000

sudo apt-get update        # Fetches the list of available updates
sudo apt-get upgrade       # Strictly upgrades the current packages
sudo apt-get dist-upgrade  # Installs updates (new ones)

franc3000

General Purpose - Current Generation

            | vCPU | ECU      | Memory (GiB) | Instance Storage (GB) | Linux/UNIX Usage |          
============|======|==========|==============|=======================|==================|==========
t2.nano     | 1    | Variable | 0.5          | EBS Only              | $0.0058          | per Hour 
t2.micro    | 1    | Variable | 1            | EBS Only              | $0.0116          | per Hour 
t2.small    | 1    | Variable | 2            | EBS Only              | $0.023           | per Hour 
t2.medium   | 2    | Variable | 4            | EBS Only              | $0.0464          | per Hour 
t2.large    | 2    | Variable | 8            | EBS Only              | $0.0928          | per Hour 
t2.xlarge   | 4    | Variable | 16           | EBS Only              | $0.1856          | per Hour 

franc3000

update tableA a
inner join tableB b
    on a.RTPropertyUniqueIdentifier=b.RTPropertyUniqueIdentifier
SET a.fips=b.SitusStateCountyFIPS,
    a.col2=b.col2;

franc3000

# To select rows whose column value equals a scalar, some_value, use ==:

df.loc[df['column_name'] == some_value]

# To select rows whose column value is in an iterable, some_values, use isin:

df.loc[df['column_name'].isin(some_values)]

# Combine multiple conditions with &:

franc3000

# empty dictionary for storing DataFrames
results = {}

# list of params for query
my_list = ['MX19', 'MX20', 'MX21', 'MX22', 'MX23', 'MX24', 'MX25', 'MX26', 'MX27', 'MX28', 'MX29', 'MX30', 'MX31', 'MX32']

for x in my_list:
    ADM = """
    SELECT a.SQLDATE,

franc3000

SitusAddress,
SitusCity,
SitusState,
SitusZip,
SitusZip4,
SitusCounty,
SitusStateCountyFIPS,
PropertyType,
PropertyZoning,
PropertyGroup,

franc3000

import numpy as np
import pandas as pd

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, Binarizer
from sklearn.base import TransformerMixin, BaseEstimator

"""
PyData Chicago