pandas DataFrameの反復処理入門

効率的なPythonコードの書き方

Logan Thomas

Scientific Software Technical Trainer, Enthought

pandasの復習

pandasの概要はIntermediate Pythonを参照
データ分析に使用するライブラリ
主なデータ構造はDataFrame
- 行と列にラベルが付いた表形式データ
- NumPy配列構造をベースに構築
本章の目標:
- pandas DataFrameの反復処理のベストプラクティス

野球の統計データ

import pandas as pd

baseball_df = pd.read_csv('baseball_stats.csv')
print(baseball_df.head())

  Team League  Year   RS   RA   W    G  Playoffs
0  ARI     NL  2012  734  688  81  162         0
1  ATL     NL  2012  700  600  94  162         1
2  BAL     AL  2012  712  705  93  162         1
3  BOS     AL  2012  734  806  69  162         0
4  CHC     NL  2012  613  759  61  162         0

野球の統計データ

  Team
0  ARI     
1  ATL     
2  BAL     
3  BOS     
4  CHC

アリゾナ・ダイヤモンドバックスのロゴとARI、アトランタ・ブレーブスのロゴとATL、ボルチモア・オリオールズのロゴとBAL、ボストン・レッドソックスのロゴとBOS、シカゴ・カブスのロゴとCHCの略称画像

野球の統計データ

  Team League  Year   RS   RA   W    G  Playoffs
0  ARI     NL  2012  734  688  81  162         0
1  ATL     NL  2012  700  600  94  162         1
2  BAL     AL  2012  712  705  93  162         1
3  BOS     AL  2012  734  806  69  162         0
4  CHC     NL  2012  613  759  61  162         0

勝率の計算

import numpy as np

def calc_win_perc(wins, games_played):

    win_perc = wins / games_played

    return np.round(win_perc,2)

win_perc = calc_win_perc(50, 100)
print(win_perc)

0.5

DataFrameへの勝率の追加

win_perc_list = []

for i in range(len(baseball_df)):
    row = baseball_df.iloc[i]

    wins = row['W']
    games_played = row['G']

    win_perc = calc_win_perc(wins, games_played)

    win_perc_list.append(win_perc)

baseball_df['WP'] = win_perc_list

DataFrameへの勝率の追加

print(baseball_df.head())

  Team League  Year   RS   RA   W    G  Playoffs    WP
0  ARI     NL  2012  734  688  81  162         0  0.50
1  ATL     NL  2012  700  600  94  162         1  0.58
2  BAL     AL  2012  712  705  93  162         1  0.57
3  BOS     AL  2012  734  806  69  162         0  0.43
4  CHC     NL  2012  613  759  61  162         0  0.38

.ilocを使った反復処理

%%timeit
win_perc_list = []

for i in range(len(baseball_df)):
    row = baseball_df.iloc[i]

    wins = row['W']
    games_played = row['G']

    win_perc = calc_win_perc(wins, games_played)
    win_perc_list.append(win_perc)

baseball_df['WP'] = win_perc_list

183 ms ± 1.73 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

.iterrows()を使った反復処理

win_perc_list = []

for i,row in baseball_df.iterrows():

    wins = row['W']
    games_played = row['G']

    win_perc = calc_win_perc(wins, games_played)

    win_perc_list.append(win_perc)

baseball_df['WP'] = win_perc_list

.iterrows()を使った反復処理

%%timeit
win_perc_list = []

for i,row in baseball_df.iterrows():

    wins = row['W']
    games_played = row['G']

    win_perc = calc_win_perc(wins, games_played)
    win_perc_list.append(win_perc)

baseball_df['WP'] = win_perc_list

95.3 ms ± 3.57 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

.iterrows()によるDataFrame反復処理の練習

効率的なPythonコードの書き方