试图忽略csv文件中的Nan抛出一个typeerror

Question

我正在加载一个包含数据的本地csv文件。我试图在行中找到NaN和数字混合的最小浮点数。

我试过使用名为np.nanmin的numpy函数，但它抛出：

"TypeError: '<=' not supported between instances of 'str' and 'float'"

database = pd.read_csv('database.csv',quotechar='"',skipinitialspace=True, delimiter=',')

    coun_weight = database[['Country of Operator/Owner', 'Launch Mass (Kilograms)']]
    print(coun_weight)

lightest = np.nanmin(coun_weight['Launch Mass (Kilograms)'])

对于为什么nanmin不起作用，有什么建议吗？

指向整个csv文件的链接：http://www.sharecsv.com/s/5aea6381d1debf75723a45aacd40abf8/database.csv

下面是我的coun_weight示例：

                 Country of Operator/Owner Launch Mass (Kilograms)
1390                     China                     NaN
1391                     China                    1040
1392                     China                    1040
1393                     China                    2700
1394                     China                    2700
1395                     China                    1800
1396                     China                    2700
1397                     China                     NaN
1398                     China                     NaN
1399                     China                     NaN
1400                     China                     NaN
1401                     India                      92
1402                    Russia                      45
1403              South Africa                       1
1404                     China                     NaN
1405                     China                       4
1406                     China                       4
1407                     China                      12

Answer 1

我试着测试它，所有有问题的值都是：

coun_weight = pd.read_csv('database.csv')

print (coun_weight.loc[pd.to_numeric(coun_weight['Launch Mass (Kilograms)'], errors='coerce').isnull(), 'Launch Mass (Kilograms)'].dropna())
1091    5,000+
1092    5,000+
1093    5,000+
1094    5,000+
1096    5,000+
Name: Launch Mass (Kilograms), dtype: object

解决办法是：

coun_weight['Launch Mass (Kilograms)'] = 
coun_weight['Launch Mass (Kilograms)'].replace('5,000+', 5000).astype(float)

print (coun_weight['Launch Mass (Kilograms)'].iloc[1091:1098])
1091    5000.0
1092    5000.0
1093    5000.0
1094    5000.0
1095       NaN
1096    5000.0
1097    6500.0
Name: Launch Mass (Kilograms), dtype: float64

如果需要，使用NaNs - Series.min查找最小值，其中跳过NaNs：

print (coun_weight['Launch Mass (Kilograms)'].min())
0.0

测试某些0是否在列中：

a = coun_weight['Launch Mass (Kilograms)']
print (a[a == 0])
912    0.0
Name: Launch Mass (Kilograms), dtype: float64

另一个可能的解决方案是将此值替换为NaNs：

coun_weight['Launch Mass (Kilograms)'] = 
pd.to_numeric(coun_weight['Launch Mass (Kilograms)'], errors='coerce')

print (coun_weight['Launch Mass (Kilograms)'].iloc[1091:1098])
1091       NaN
1092       NaN
1093       NaN
1094       NaN
1095       NaN
1096       NaN
1097    6500.0
Name: Launch Mass (Kilograms), dtype: float64

Answer 2

试图将列转换为浮动会显式显示问题，但"5,000+“不会转换为‘float 64’。

coun_weight['Launch Mass (Kilograms)'].astype('float64')

结果：

    ValueError: invalid literal for float(): 5,000+