July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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460
14
章 データ分析の実例
0 Mary F 7065 1880 0.077643
1 Anna F 2604 1880 0.028618
2 Emma F 2003 1880 0.022013
3 Elizabeth F 1939 1880 0.021309
4 Minnie F 1746 1880 0.019188
... ... .. ... ... ...
261872 Camilo M 194 2010 0.000102
261873 Destin M 194 2010 0.000102
261874 Jaquan M 194 2010 0.0
00102
261875 Jaydan M 194 2010 0.000102
261876 Maxton M 193 2010 0.000102
[261877 rows x 5 columns]
以降、この上位
1,000
件の名前データを使って分析を進めていくことにしましょう。
14.3.1
名付けの傾向分析
ここまでの準備で、全データセットと上位
1,000
件のデータセットのそれぞれが用意できました。こ
こから名前の傾向分析に入りたいと思います。まず
top1000
を男女別に選り分けておきます。
In [109]: boys = top1000[top1000.sex == 'M']
In [110]: girls = top1000[top1000.sex == 'F']
John
や
Mary
といった（よく見られる）名前の年度別推移を見るだけであれば、単純
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