我有一个大型目录，我根据以下标准选择数据：
columns = ["System", "rp", "mp", "logg"]
catalog = pd.read_csv('data.txt', skiprows=1, sep ='\s+', names=columns)
# CUTS
i = (catalog.rp != -1) & (catalog.mp != -1)
new_catalog = pd.DataFrame(catalog[i])
print("{0} targets after cuts".format(len(new_catalog)))
当我执行上述切割时，代码工作正常。接下来，我想再添加一个剪切：我想选择所有具有4.0 < logg < 5.0的目标。但是，有些目标有logg = -1（代表价值不可用的事实）。幸运的是，我可以从其他可用参数计算logg。所以这是我更新的剪辑：
# CUTS
i = (catalog.rp != -1) & (catalog.mp != -1)
if catalog.logg[i] == -1:
catalog.logg[i] = catalog.mp[i] / catalog.rp[i]
i &= (4 <= catalog.logg) & (catalog.logg <= 5)
但是，我收到一个错误：
if catalog.logg[i] == -1:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
有人可以解释一下我做错了什么以及如何解决它。谢谢
我的数据框如下所示：
Data columns:
System 477 non-null values
rp 477 non-null values
mp 477 non-null values
logg 477 non-null values
dtypes: float64(37), int64(3), object(3)None
System rp mp logg FeH FeHu FeHl Mstar Mstaru Mstarl
0 target-01 5196 24 24 0.31 0.04 0.04 0.905 0.015 0.015
1 target-02 5950 150 150 -0.30 0.25 0.25 0.950 0.110 0.110
2 target-03 5598 50 50 0.04 0.05 0.05 0.997 0.049 0.049
3 target-04 6558 44 -1 0.14 0.04 0.04 1.403 0.061 0.061
4 target-05 6190 60 60 0.05 0.07 0.07 1.194 0.049 0.050
....
[5 rows x 43 columns]
我理解的格式的代码应该是：
for row in range(len(catalog)):
parameter = catalog['logg'][row]
if parameter == -1:
parameter = catalog['mp'][row] / catalog['rp'][row]
if parameter > 4.0 and parameter < 5.0:
# select this row for further analysis
但是，我正在尝试以更简单和专业的方式编写代码。我不想使用for循环。我该怎么办？
考虑以下小例子：
System rp mp logg
target-01 2 -1 2 # will NOT be selected since mp = -1
target-02 -1 3 4 # will NOT be selected since rp = -1
target-03 7 6 4.3 # will be selected since mp != -1, rp != -1, and 4 < logg <5
target-04 3.2 15 -1 # will be selected since mp != -1, rp != -1, logg = mp / rp = 15/3.2 = 4.68 (which is between 4 and 5)
答案 0 :(得分：0)
而不是代码：
if catalog.logg[i] == -1:
catalog.logg[i] = catalog.mp[i] / catalog.rp[i]
您可以使用以下内容：
i &= df.logg == -1
df.loc[i, 'logg'] = df.loc[i, 'mp'] / df.loc[i, 'rp']
# or
df.ix[i, 'logg'] = df.ix[i, 'mp'] / df.ix[i, 'rp']
对于您的编辑3，您需要添加该行：
your_rows = df[(df.logg > 4) & (df.logg < 5)]
完整代码：
i = (catalog.rp != -1) & (catalog.mp != -1)
i &= df.logg == -1
df.ix[i, 'logg'] = df.ix[i, 'mp'] / df.ix[i, 'rp']
your_rows = df[(df.logg > 4) & (df.logg < 5)]
修改
可能我仍然不明白你想要什么，但我得到你想要的输出：
import pandas as pd
from io import StringIO
data = """
System rp mp logg
target-01 2 -1 2
target-02 -1 3 4
target-03 7 6 4.3
target-04 3.2 15 -1
"""
catalog = pd.read_csv(StringIO(data), sep='\s+')
i = (catalog.rp != -1) & (catalog.mp != -1)
i &= catalog.logg == -1
catalog.ix[i, 'logg'] = catalog.ix[i, 'mp'] / catalog.ix[i, 'rp']
your_rows = catalog[(catalog.logg > 4) & (catalog.logg < 5)]
In [7]: your_rows
Out[7]:
System rp mp logg
2 target-03 7.0 6 4.3000
3 target-04 3.2 15 4.6875
我还错吗？