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# Import KNeighborsClassifier from sklearn.neighbors
from sklearn.neighbors import KNeighborsClassifier 

# Create arrays for the features and the response variable
y = df['party'].values
X = df.drop('party', axis=1).values

# Create a k-NN classifier with 6 neighbors: knn
knn = KNeighborsClassifier(n_neighbors=6)

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# Import pandas as pd
import pandas as pd

# Fix import by including index_col
cars = pd.read_csv('cars.csv', index_col=0)

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import matplotlib.pyplot as plt
import seaborn as sns

gdp_cap = [10, 11, 12, 14, 18]
life_exp = [11, 30, 13, 41, 12]
# Specify c and alpha inside plt.scatter()
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha = 0.8)

# Previous customizations
plt.xscale('log') 

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alter session set "_ORACLE_SCRIPT"=true;

create user julian identified by julian;

grant dba to julian;

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<application id="Application_ID" >
  <display-name>Application Name</display-name>
  <description>This is the application description</<description>
  <module id="WebModule_1" >
    <web>
      <web-uri>pandorabe.war</web-uri>
      <context-root>/</context-root>
    </web>
  </module>
</application>

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SELECT to_char(creation_date,  'dd-mm-yy hh24:mi:ss') as creation_date from PANDORA.NEPHROLOGIST;

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######################
### DESIGN CHANGES ###
######################

# panes
set -g pane-border-fg black
set -g pane-active-border-fg brightred

## Status bar design
# status line

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netstat -ln | grep "unix.*mysql"

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$("details").attr("open", "true")