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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484
14
章 データ分析の実例
(100000, 1000000] 1.000000 NaN
(1000000, 10000000] 1.000000 NaN
In [219]: normed_sums[:-2].plot(kind='barh')
図14-13 寄付金額の枠ごとの候補者別の寄付の割合
寄付金額枠の上位
2
つは個人のものでないため除外しました。
さらにこの分析を改良するのに、さまざまな方法が考えられます。例えば、少額の多数の寄付をし
た人々がおり、一方で一度（もしくは数度）の大きな寄付をした人々がいます。これらを比較できるよう、
寄付を提供者名と郵便番号で集計することが考えられるでしょう。ぜひデータをダウンロードし、手を
動かして分析してみることをお勧めします。
14.5.3
州ごとの寄付の分析
このデータを候補者別、かつ州ごとに集計してみましょう。もはやルーチンに近い手順になってきた
でしょうか。
In [220]: grouped = fec_mrbo.groupby(['cand_nm', 'contbr_st'])
In [221]: totals = grouped.contb_receipt_amt.sum().unstack(
0).fillna(0)
In [222]: totals = totals[totals.sum(1) > 100000]
In [223]: totals[:10]
Out[223]:
cand_nm Obama, Barack Romney, Mitt
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