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csvsql --db postgresql:///test1 --insert Problem_Set_Operations_Research.csv | |
# I was having some trouble getting a csv file inserted into a Postgres Database. The above line worked. | |
\dt | |
#Shows the tables in a particular database | |
\connect test1 | |
#Connects to a database with a particular name in the case above "test1" |
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# -*- coding: utf-8 -*- | |
""" | |
LICENSE: BSD (same as pandas) | |
example use of pandas with oracle mysql postgresql sqlite | |
- updated 9/18/2012 with better column name handling; couple of bug fixes. | |
- used ~20 times for various ETL jobs. Mostly MySQL, but some Oracle. | |
to do: | |
save/restore index (how to check table existence? just do select count(*)?), | |
finish odbc, |
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install.packages("caret") | |
install.packages("rpart") | |
install.packages("tree") | |
install.packages("randomForest") | |
install.packages("e1071") | |
install.packages("ggplot2") | |
table <- read.csv("DataSciIntro/assignment5//seaflow_21min.csv") | |
library("caret") | |
library("rpart") |
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install.packages("caret") | |
install.packages("rpart") | |
install.packages("tree") | |
install.packages("randomForest") | |
install.packages("e1071") | |
install.packages("ggplot2") | |
table <- read.csv("DataSciIntro/assignment5//seaflow_21min.csv") | |
library("caret") | |
library("rpart") |
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#!/bin/bash | |
virtualenv .env && | |
source .env/bin/activate && | |
sudo apt-get install -y libevent-dev && | |
sudo apt-get install -y libyaml-0-2 && | |
sudo apt-get install --yes --force-yes automake autoconf libtool* gfortran && | |
sudo apt-get install --yes --force-yes liblapack-dev && | |
sudo apt-get install --yes --force-yes binutils && | |
sudo apt-get install -y libatlas-dev && | |
sudo apt-get install -y build-essential --upgrade && |
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{ | |
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# -*- coding: utf-8 -*- | |
""" | |
Sample report generation script from pbpython.com | |
This program takes an input Excel file, reads it and turns it into a | |
pivot table. | |
The output is saved in multiple tabs in a new Excel file. | |
""" |
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g = simuls.groupby('Team') | |
season_hdis = pd.DataFrame({'points_lower': g.points.quantile(.05), | |
'points_upper': g.points.quantile(.95), | |
'goals_for_lower': g.gf.quantile(.05), | |
'goals_for_median': g.gf.median(), | |
'goals_for_upper': g.gf.quantile(.95), | |
'goals_against_lower': g.ga.quantile(.05), | |
'goals_against_upper': g.ga.quantile(.95), | |
}) | |
season_hdis = pd.merge(season_hdis, df_observed, left_index=True, right_on='Team') |
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