JonathanLoscalzo

JonathanLoscalzo

from sklearn.datasets import *
from sklearn import tree
import graphviz

wine = load_wine()
clf = tree.DecisionTreeClassifier() # init the tree
clf = clf.fit(wine.data, wine.target) # train the tree
# export the learned decision tree
dot_data = tree.export_graphviz(clf, out_file=None,
                         feature_names=wine.feature_names,

JonathanLoscalzo

from sklearn.datasets import *
from sklearn import tree

from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize

n_classes = 3
wine = load_wine()
clf = tree.DecisionTreeClassifier()

JonathanLoscalzo

Counting USA vs. USSR Cold War Olympic Sports The Olympic competitions between 1952 and 1988 took place during the height of the Cold War between the United States of America (USA) & the Union of Soviet Socialist Republics (USSR). Your goal in this exercise is to aggregate the number of distinct sports in which the USA and the USSR won medals during the Cold War years.

The construction is mostly the same as in the preceding exercise. There is an additional filtering stage beforehand in which you reduce the original DataFrame medals by extracting data from the Cold War period that applies only to the US or to the USSR. The relevant country codes in the DataFrame, which has been pre-loaded as medals, are 'USA' & 'URS'.

JonathanLoscalzo

Counting USA vs. USSR Cold War Olympic Medals For this exercise, you want to see which country, the USA or the USSR, won the most medals consistently over the Cold War period.

There are several steps involved in carrying out this computation.

You'll need a pivot table with years ('Edition') on the index and countries ('NOC') on the columns. The entries will be the total number of medals each country won that year. If the country won no medals in a given edition, expect a NaN in that entry of the pivot table. You'll need to slice the Cold War period and subset the 'USA' and 'URS' columns. You'll need to make a Series from this slice of the pivot table that tells which country won the most medals in that edition using .idxmax(axis='columns'). If .max() returns the maximum value of Series or 1D array, .idxmax() returns the index of the maximizing element. The argument axis=columns or axis=1 is required because, by default, this aggregation would be done along columns for a DataFrame. The final Series contains

JonathanLoscalzo

Cleaning Data with python

JonathanLoscalzo

Merging DataFrames with pandas.md

JonathanLoscalzo

Olimpic Medals DataSets

JonathanLoscalzo

# Import create_engine function
from sqlalchemy import create_engine
driver = 'mysql+pymysql://'
cred = 'student:datacamp'
url = '@courses.csrrinzqubik.us-east-1.rds.amazonaws.com:3306/'
port = 'census'

# Create an engine to the census database
engine = create_engine(driver + cred + url + port)

JonathanLoscalzo