只是添加另一个解决方案，如果您正在使用新的pandas评估器，则特别有用，其他解决方案将取代原来的pandas并失去评估器

df.drop(df.loc[df['line_race']==0].index, inplace=True)

对于像这样的简单示例，这没有太大区别，但对于复杂的逻辑，我更喜欢在删除行时使用drop()，因为它比使用反向逻辑更直接。例如，删除A=1和(B=2或C=3)的行。

下面是一个易于理解并能处理复杂逻辑的可伸缩语法:

df.drop( df.query(" `line_race` == 0 ").index)

有很多方法可以实现这一点。将在下面留下各种选项，可以使用，这取决于一个用例的特殊性。

可以认为OP的数据帧存储在变量df中。

---

选项1

对于OP的情况，考虑到唯一值为0的列是line_race，下面将完成工作

 df_new = df[df != 0].dropna()
 
[Out]:
     line_date  daysago  line_race  rating        rw    wrating
0   2007-03-31       62       11.0      56  1.000000  56.000000
1   2007-03-10       83       11.0      67  1.000000  67.000000
2   2007-02-10      111        9.0      66  1.000000  66.000000
3   2007-01-13      139       10.0      83  0.880678  73.096278
4   2006-12-23      160       10.0      88  0.793033  69.786942
5   2006-11-09      204        9.0      52  0.636655  33.106077
6   2006-10-22      222        8.0      66  0.581946  38.408408
7   2006-09-29      245        9.0      70  0.518825  36.317752
8   2006-09-16      258       11.0      68  0.486226  33.063381
9   2006-08-30      275        8.0      72  0.446667  32.160051
10  2006-02-11      475        5.0      65  0.164591  10.698423

然而，由于情况并非总是如此，建议检查以下选项，其中将指定列名。

---

选项2

tshauck的方法最终比选项1更好，因为它可以指定列。然而，根据想要引用列的方式，还有其他的变化:

例如，使用数据框架中的位置

df_new = df[df[df.columns[2]] != 0]

或者通过如下显式地指示列

df_new = df[df['line_race'] != 0]

也可以遵循相同的登录，但使用自定义lambda函数，例如

df_new = df[df.apply(lambda x: x['line_race'] != 0, axis=1)]

[Out]:
     line_date  daysago  line_race  rating        rw    wrating
0   2007-03-31       62       11.0      56  1.000000  56.000000
1   2007-03-10       83       11.0      67  1.000000  67.000000
2   2007-02-10      111        9.0      66  1.000000  66.000000
3   2007-01-13      139       10.0      83  0.880678  73.096278
4   2006-12-23      160       10.0      88  0.793033  69.786942
5   2006-11-09      204        9.0      52  0.636655  33.106077
6   2006-10-22      222        8.0      66  0.581946  38.408408
7   2006-09-29      245        9.0      70  0.518825  36.317752
8   2006-09-16      258       11.0      68  0.486226  33.063381
9   2006-08-30      275        8.0      72  0.446667  32.160051
10  2006-02-11      475        5.0      65  0.164591  10.698423

---

选项3

使用pandas.Series.map和自定义lambda函数

df_new = df['line_race'].map(lambda x: x != 0)

[Out]:
     line_date  daysago  line_race  rating        rw    wrating
0   2007-03-31       62       11.0      56  1.000000  56.000000
1   2007-03-10       83       11.0      67  1.000000  67.000000
2   2007-02-10      111        9.0      66  1.000000  66.000000
3   2007-01-13      139       10.0      83  0.880678  73.096278
4   2006-12-23      160       10.0      88  0.793033  69.786942
5   2006-11-09      204        9.0      52  0.636655  33.106077
6   2006-10-22      222        8.0      66  0.581946  38.408408
7   2006-09-29      245        9.0      70  0.518825  36.317752
8   2006-09-16      258       11.0      68  0.486226  33.063381
9   2006-08-30      275        8.0      72  0.446667  32.160051
10  2006-02-11      475        5.0      65  0.164591  10.698423

---

选项4

使用pandas. datafframe .drop，如下所示

df_new = df.drop(df[df['line_race'] == 0].index)

[Out]:
     line_date  daysago  line_race  rating        rw    wrating
0   2007-03-31       62       11.0      56  1.000000  56.000000
1   2007-03-10       83       11.0      67  1.000000  67.000000
2   2007-02-10      111        9.0      66  1.000000  66.000000
3   2007-01-13      139       10.0      83  0.880678  73.096278
4   2006-12-23      160       10.0      88  0.793033  69.786942
5   2006-11-09      204        9.0      52  0.636655  33.106077
6   2006-10-22      222        8.0      66  0.581946  38.408408
7   2006-09-29      245        9.0      70  0.518825  36.317752
8   2006-09-16      258       11.0      68  0.486226  33.063381
9   2006-08-30      275        8.0      72  0.446667  32.160051
10  2006-02-11      475        5.0      65  0.164591  10.698423

---

选择5

使用pandas.DataFrame.query如下所示

df_new = df.query('line_race != 0')

[Out]:
     line_date  daysago  line_race  rating        rw    wrating
0   2007-03-31       62       11.0      56  1.000000  56.000000
1   2007-03-10       83       11.0      67  1.000000  67.000000
2   2007-02-10      111        9.0      66  1.000000  66.000000
3   2007-01-13      139       10.0      83  0.880678  73.096278
4   2006-12-23      160       10.0      88  0.793033  69.786942
5   2006-11-09      204        9.0      52  0.636655  33.106077
6   2006-10-22      222        8.0      66  0.581946  38.408408
7   2006-09-29      245        9.0      70  0.518825  36.317752
8   2006-09-16      258       11.0      68  0.486226  33.063381
9   2006-08-30      275        8.0      72  0.446667  32.160051
10  2006-02-11      475        5.0      65  0.164591  10.698423

---

选择6

使用pandas.DataFrame.drop和pandas.DataFrame.query如下所示

df_new = df.drop(df.query('line_race == 0').index)

[Out]:
     line_date  daysago  line_race  rating        rw    wrating
0   2007-03-31       62       11.0      56  1.000000  56.000000
1   2007-03-10       83       11.0      67  1.000000  67.000000
2   2007-02-10      111        9.0      66  1.000000  66.000000
3   2007-01-13      139       10.0      83  0.880678  73.096278
4   2006-12-23      160       10.0      88  0.793033  69.786942
5   2006-11-09      204        9.0      52  0.636655  33.106077
6   2006-10-22      222        8.0      66  0.581946  38.408408
7   2006-09-29      245        9.0      70  0.518825  36.317752
8   2006-09-16      258       11.0      68  0.486226  33.063381
9   2006-08-30      275        8.0      72  0.446667  32.160051
10  2006-02-11      475        5.0      65  0.164591  10.698423

---

选择7

如果对输出没有强烈的意见，可以使用numpy.select的向量化方法

df_new = np.select([df != 0], [df], default=np.nan)

[Out]:
[['2007-03-31' 62 11.0 56 1.0 56.0]
 ['2007-03-10' 83 11.0 67 1.0 67.0]
 ['2007-02-10' 111 9.0 66 1.0 66.0]
 ['2007-01-13' 139 10.0 83 0.880678 73.096278]
 ['2006-12-23' 160 10.0 88 0.793033 69.786942]
 ['2006-11-09' 204 9.0 52 0.636655 33.106077]
 ['2006-10-22' 222 8.0 66 0.581946 38.408408]
 ['2006-09-29' 245 9.0 70 0.518825 36.317752]
 ['2006-09-16' 258 11.0 68 0.486226 33.063381]
 ['2006-08-30' 275 8.0 72 0.446667 32.160051]
 ['2006-02-11' 475 5.0 65 0.164591 10.698423]]

这也可以转换为一个数据框架

df_new = pd.DataFrame(df_new, columns=df.columns)

[Out]:
     line_date daysago line_race rating        rw    wrating
0   2007-03-31      62      11.0     56       1.0       56.0
1   2007-03-10      83      11.0     67       1.0       67.0
2   2007-02-10     111       9.0     66       1.0       66.0
3   2007-01-13     139      10.0     83  0.880678  73.096278
4   2006-12-23     160      10.0     88  0.793033  69.786942
5   2006-11-09     204       9.0     52  0.636655  33.106077
6   2006-10-22     222       8.0     66  0.581946  38.408408
7   2006-09-29     245       9.0     70  0.518825  36.317752
8   2006-09-16     258      11.0     68  0.486226  33.063381
9   2006-08-30     275       8.0     72  0.446667  32.160051
10  2006-02-11     475       5.0     65  0.164591  10.698423

---

至于最有效的解决方案，这将取决于人们如何衡量效率。假设要度量执行时间，可以使用time.perf_counter()。

如果测量上述所有选项的执行时间，就会得到以下结果

       method                   time
0    Option 1 0.00000110000837594271
1  Option 2.1 0.00000139995245262980
2  Option 2.2 0.00000369996996596456
3  Option 2.3 0.00000160001218318939
4    Option 3 0.00000110000837594271
5    Option 4 0.00000120000913739204
6    Option 5 0.00000140001066029072
7    Option 6 0.00000159995397552848
8    Option 7 0.00000150001142174006

但是，这可能会根据所使用的数据框架、需求(比如硬件)等而改变。

---

注:

There are various suggestions on using inplace=True. Would suggest reading this: https://stackoverflow.com/a/59242208/7109869 There are also some people with strong opinions on .apply(). Would suggest reading this: When should I (not) want to use pandas apply() in my code? If one has missing values, one might want to consider as well pandas.DataFrame.dropna. Using the option 2, it would be something like df = df[df['line_race'] != 0].dropna() There are additional ways to measure the time of execution, so I would recommend this thread: How do I get time of a Python program's execution?

如果我没理解错的话，应该是这么简单:

df = df[df.line_race != 0]

最好的方法是使用布尔屏蔽:

In [56]: df
Out[56]:
     line_date  daysago  line_race  rating    raw  wrating
0   2007-03-31       62         11      56  1.000   56.000
1   2007-03-10       83         11      67  1.000   67.000
2   2007-02-10      111          9      66  1.000   66.000
3   2007-01-13      139         10      83  0.881   73.096
4   2006-12-23      160         10      88  0.793   69.787
5   2006-11-09      204          9      52  0.637   33.106
6   2006-10-22      222          8      66  0.582   38.408
7   2006-09-29      245          9      70  0.519   36.318
8   2006-09-16      258         11      68  0.486   33.063
9   2006-08-30      275          8      72  0.447   32.160
10  2006-02-11      475          5      65  0.165   10.698
11  2006-01-13      504          0      70  0.142    9.969
12  2006-01-02      515          0      64  0.135    8.627
13  2005-12-06      542          0      70  0.118    8.246
14  2005-11-29      549          0      70  0.114    7.963
15  2005-11-22      556          0      -1  0.110   -0.110
16  2005-11-01      577          0      -1  0.099   -0.099
17  2005-10-20      589          0      -1  0.093   -0.093
18  2005-09-27      612          0      -1  0.083   -0.083
19  2005-09-07      632          0      -1  0.075   -0.075
20  2005-06-12      719          0      69  0.049    3.360
21  2005-05-29      733          0      -1  0.045   -0.045
22  2005-05-02      760          0      -1  0.040   -0.040
23  2005-04-02      790          0      -1  0.034   -0.034
24  2005-03-13      810          0      -1  0.031   -0.031
25  2004-11-09      934          0      -1  0.017   -0.017

In [57]: df[df.line_race != 0]
Out[57]:
     line_date  daysago  line_race  rating    raw  wrating
0   2007-03-31       62         11      56  1.000   56.000
1   2007-03-10       83         11      67  1.000   67.000
2   2007-02-10      111          9      66  1.000   66.000
3   2007-01-13      139         10      83  0.881   73.096
4   2006-12-23      160         10      88  0.793   69.787
5   2006-11-09      204          9      52  0.637   33.106
6   2006-10-22      222          8      66  0.582   38.408
7   2006-09-29      245          9      70  0.519   36.318
8   2006-09-16      258         11      68  0.486   33.063
9   2006-08-30      275          8      72  0.447   32.160
10  2006-02-11      475          5      65  0.165   10.698

更新:现在熊猫0.13出来了，另一种方法是df。查询('line_race != 0')。

但对于任何未来的旁路器，你可以提到df = df[df。line_race != 0]在尝试过滤无值/缺失值时不做任何事情。

做的工作:

df = df[df.line_race != 0]

什么都不做:

df = df[df.line_race != None]

做的工作:

df = df[df.line_race.notnull()]