Örnekleme yöntemlerini karşılaştırma

Python'da Örnekleme

James Chapman

Curriculum Manager, DataCamp

Örnekleme tekniklerine genel bakış - kurulum

top_counted_countries = ["Mexico", "Colombia", "Guatemala",
  "Brazil", "Taiwan", "United States (Hawaii)"]

subset_condition = coffee_ratings['country_of_origin'].isin(top_counted_countries)
coffee_ratings_top = coffee_ratings[subset_condition]

coffee_ratings_top.shape
(880, 8)
Python'da Örnekleme

Basit rastgele örnekleme: tekrar

coffee_ratings_srs = coffee_ratings_top.sample(frac=1/3, random_state=2021)
coffee_ratings_srs.shape
(293, 8)
Python'da Örnekleme

Katmanlı örnekleme: tekrar

coffee_ratings_strat = coffee_ratings_top.groupby("country_of_origin")\
    .sample(frac=1/3, random_state=2021)
coffee_ratings_strat.shape
(293, 8)
Python'da Örnekleme

Küme örnekleme: tekrar

import random
top_countries_samp = random.sample(top_counted_countries, k=2)
top_condition = coffee_ratings_top['country_of_origin'].isin(top_countries_samp)
coffee_ratings_cluster = coffee_ratings_top[top_condition]
coffee_ratings_cluster['country_of_origin'] = coffee_ratings_cluster['country_of_origin']\
    .cat.remove_unused_categories()


coffee_ratings_clust = coffee_ratings_cluster.groupby("country_of_origin")\ .sample(n=len(coffee_ratings_top) // 6)
coffee_ratings_clust.shape
(292, 8)
Python'da Örnekleme

Ortalama kupa puanını hesaplama

Ana kütle

coffee_ratings_top['total_cup_points'].mean()
81.94700000000002

Basit rastgele örnek

coffee_ratings_srs['total_cup_points'].mean()
81.95982935153583

Katmanlı örneklem

coffee_ratings_strat['total_cup_points'].mean()
81.92566552901025

Küme örneklemi

coffee_ratings_clust['total_cup_points'].mean()
82.03246575342466
Python'da Örnekleme

Ülkeye göre ort. kupa puanı: basit rastgele

Ana kütle:

coffee_ratings_top.groupby("country_of_origin")\
    ['total_cup_points'].mean()
country_of_origin
Brazil                    82.405909
Colombia                  83.106557
Guatemala                 81.846575
Mexico                    80.890085
Taiwan                    82.001333
United States (Hawaii)    81.820411
Name: total_cup_points, dtype: float64

Basit rastgele örnek:

coffee_ratings_srs.groupby("country_of_origin")\
    ['total_cup_points'].mean()
country_of_origin
Brazil                    82.414878
Colombia                  82.925536
Guatemala                 82.045385
Mexico                    81.100714
Taiwan                    81.744333
United States (Hawaii)    82.008000
Name: total_cup_points, dtype: float64
Python'da Örnekleme

Ülkeye göre ort. kupa puanı: katmanlı

Ana kütle:

coffee_ratings_top.groupby("country_of_origin")\
    ['total_cup_points'].mean()
country_of_origin
Brazil                    82.405909
Colombia                  83.106557
Guatemala                 81.846575
Mexico                    80.890085
Taiwan                    82.001333
United States (Hawaii)    81.820411
Name: total_cup_points, dtype: float64

Katmanlı örneklem:

coffee_ratings_strat.groupby("country_of_origin")\
    ['total_cup_points'].mean()
country_of_origin
Brazil                    82.499773
Colombia                  83.288197
Guatemala                 81.727667
Mexico                    80.994684
Taiwan                    81.846800
United States (Hawaii)    81.051667
Name: total_cup_points, dtype: float64
Python'da Örnekleme

Ülkeye göre ort. kupa puanı: küme

Ana kütle:

coffee_ratings_top.groupby("country_of_origin")\
    ['total_cup_points'].mean()
country_of_origin
Brazil                    82.405909
Colombia                  83.106557
Guatemala                 81.846575
Mexico                    80.890085
Taiwan                    82.001333
United States (Hawaii)    81.820411
Name: total_cup_points, dtype: float64

Küme örneklemi:

coffee_ratings_clust.groupby("country_of_origin")\
    ['total_cup_points'].mean()
country_of_origin
Colombia    83.128904
Mexico      80.936027
Name: total_cup_points, dtype: float64
Python'da Örnekleme

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Python'da Örnekleme

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