相关数据见(github)
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16
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RangeIndex: 16 entries, 0 to 15
Data columns (total 35 columns):
Team 16 non-null object
Goals 16 non-null int64
Shots on target 16 non-null int64
Shots off target 16 non-null int64
Shooting Accuracy 16 non-null object
% Goals-to-shots 16 non-null object
Total shots (inc. Blocked) 16 non-null int64
Hit Woodwork 16 non-null int64
Penalty goals 16 non-null int64
Penalties not scored 16 non-null int64
Headed goals 16 non-null int64
Passes 16 non-null int64
Passes completed 16 non-null int64
Passing Accuracy 16 non-null object
Touches 16 non-null int64
Crosses 16 non-null int64
Dribbles 16 non-null int64
Corners Taken 16 non-null int64
Tackles 16 non-null int64
Clearances 16 non-null int64
Interceptions 16 non-null int64
Clearances off line 15 non-null float64
Clean Sheets 16 non-null int64
Blocks 16 non-null int64
Goals conceded 16 non-null int64
Saves made 16 non-null int64
Saves-to-shots ratio 16 non-null object
Fouls Won 16 non-null int64
Fouls Conceded 16 non-null int64
Offsides 16 non-null int64
Yellow Cards 16 non-null int64
Red Cards 16 non-null int64
Subs on 16 non-null int64
Subs off 16 non-null int64
Players Used 16 non-null int64
dtypes: float64(1), int64(29), object(5)
memory usage: 4.5+ KB
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7.0
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1、http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook
2、https://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/
3、https://github.com/guipsamora/pandas_exercises
The End
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