使用 numpy 从 CSV 文件中提取数据

Question

我正在与numpy 合作，并试图找出哪个平台在北美地区的销量最高。

我有一个包含大量数据的 CSV 文件，如下所示：

Rank,Name,Platform,Year,Genre,Publisher,NA_Sales,EU_Sales,JP_Sales,Other_Sales,Global_Sales
1,Wii Sports,Wii,2006,Sports,Nintendo,41.49,29.02,3.77,8.46,82.74
2,Super Mario Bros.,NES,1985,Platform,Nintendo,29.08,3.58,6.81,0.77,40.24
3,Mario Kart Wii,Wii,2008,Racing,Nintendo,15.85,12.88,3.79,3.31,35.82
4,Wii Sports Resort,Wii,2009,Sports,Nintendo,15.75,11.01,3.28,2.96,33
5,Pokemon Red/Pokemon Blue,GB,1996,Role-Playing,Nintendo,11.27,8.89,10.22,1,31.37
6,Tetris,GB,1989,Puzzle,Nintendo,23.2,2.26,4.22,0.58,30.26
7,New Super Mario Bros.,DS,2006,Platform,Nintendo,11.38,9.23,6.5,2.9,30.01
8,Wii Play,Wii,2006,Misc,Nintendo,14.03,9.2,2.93,2.85,29.02
9,New Super Mario Bros. Wii,Wii,2009,Platform,Nintendo,14.59,7.06,4.7,2.26,28.62
10,Duck Hunt,NES,1984,Shooter,Nintendo,26.93,0.63,0.28,0.47,28.31
11,Nintendogs,DS,2005,Simulation,Nintendo,9.07,11,1.93,2.75,24.76

我想打印北美地区销售额和销售额最高的平台。我该怎么做？

Answer 1

用genfromtxt 加载这个很简单：

In [280]: data=np.genfromtxt('stack42602390.csv',delimiter=',',names=True, dtype=None)

In [281]: data
Out[281]: 
array([ ( 1, b'Wii Sports', b'Wii', 2006, b'Sports', b'Nintendo',  41.49,  29.02,   3.77,  8.46,  82.74),
       ( 2, b'Super Mario Bros.', b'NES', 1985, b'Platform', b'Nintendo',  29.08,   3.58,   6.81,  0.77,  40.24),
       ( 3, b'Mario Kart Wii', b'Wii', 2008, b'Racing', b'Nintendo',  15.85,  12.88,   3.79,  3.31,  35.82),
....
       (11, b'Nintendogs', b'DS', 2005, b'Simulation', b'Nintendo',   9.07,  11.  ,   1.93,  2.75,  24.76)], 
      dtype=[('Rank', '<i4'), ('Name', 'S25'), ('Platform', 'S3'), ('Year', '<i4'), ('Genre', 'S12'), ('Publisher', 'S8'), ('NA_Sales', '<f8'), ('EU_Sales', '<f8'), ('JP_Sales', '<f8'), ('Other_Sales', '<f8'), ('Global_Sales', '<f8')])

b'string' 只是 Python3 显示字节串的方式，genfromtxt 的默认字符串格式。它们不会在 Py2 中显示。

结果是一个结构化数组，具有不同的字段名称和类型。它不是具有行和列的二维数组。

NA_Sales 数据：

In [282]: data['NA_Sales']
Out[282]: 
array([ 41.49,  29.08,  15.85,  15.75,  11.27,  23.2 ,  11.38,  14.03,
        14.59,  26.93,   9.07])

以及其中的最大值：

In [283]: np.argmax(data['NA_Sales'])
Out[283]: 0

以及对应的记录：

In [284]: data[0]
Out[284]: (1, b'Wii Sports', b'Wii', 2006, b'Sports', b'Nintendo',  41.49,  29.02,  3.77,  8.46,  82.74)

要充分利用这个数组，您必须阅读结构化数组。

Answer 2

对于 pandas，这相当简单。

代码：

# read csv data into a dataframe
df = pd.read_csv(data, skipinitialspace=True)

# roll up by NA Sales
platform_roll_up = df.groupby('Platform')['NA_Sales'].sum()

# find row with max sales
idx_max = platform_roll_up.idxmax()

# show platform and sales for max
print(idx_max, platform_roll_up[idx_max])

结果：

Wii 101.71

测试数据：

data = StringIO(u"""
    Rank,Name,Platform,Year,Genre,Publisher,NA_Sales,EU_Sales,JP_Sales,Other_Sales,Global_Sales
    1,Wii Sports,Wii,2006,Sports,Nintendo,41.49,29.02,3.77,8.46,82.74
    2,Super Mario Bros.,NES,1985,Platform,Nintendo,29.08,3.58,6.81,0.77,40.24
    3,Mario Kart Wii,Wii,2008,Racing,Nintendo,15.85,12.88,3.79,3.31,35.82
    4,Wii Sports Resort,Wii,2009,Sports,Nintendo,15.75,11.01,3.28,2.96,33
    5,Pokemon Red/Pokemon Blue,GB,1996,Role-Playing,Nintendo,11.27,8.89,10.22,1,31.37
    6,Tetris,GB,1989,Puzzle,Nintendo,23.2,2.26,4.22,0.58,30.26
    7,New Super Mario Bros.,DS,2006,Platform,Nintendo,11.38,9.23,6.5,2.9,30.01
    8,Wii Play,Wii,2006,Misc,Nintendo,14.03,9.2,2.93,2.85,29.02
    9,New Super Mario Bros. Wii,Wii,2009,Platform,Nintendo,14.59,7.06,4.7,2.26,28.62
    10,Duck Hunt,NES,1984,Shooter,Nintendo,26.93,0.63,0.28,0.47,28.31
    11,Nintendogs,DS,2005,Simulation,Nintendo,9.07,11,1.93,2.75,24.76
""")