I am trying to determine whether there is an entry in a Pandas column that has a particular value. I tried to do this with if x in df['id']. I thought this was working, except when I fed it a value that I knew was not in the column 43 in df['id'] it still returned True. When I subset to a data frame only containing entries matching the missing id df[df['id'] == 43] there are, obviously, no entries in it. How to I determine if a column in a Pandas data frame contains a particular value and why doesn't my current method work? (FYI, I have the same problem when I use the implementation in this answer to a similar question).
当前回答
假设你的数据框架是这样的:
现在你要检查文件名“80900026941984”是否存在于数据帧中。
你可以简单地写:
if sum(df["filename"].astype("str").str.contains("80900026941984")) > 0:
print("found")
其他回答
你可以试着检查一个名为“id”的列中的特定值“x”
if x in df['id'].values
Use
df[df['id']==x].index.tolist()
如果x存在于id中，那么它将返回它所在位置的索引列表，否则它将给出一个空列表。
假设你的数据框架是这样的:
现在你要检查文件名“80900026941984”是否存在于数据帧中。
你可以简单地写:
if sum(df["filename"].astype("str").str.contains("80900026941984")) > 0:
print("found")
我有一个CSV文件要读取:
df = pd.read_csv('50_states.csv')
在尝试之后:
if value in df.column:
print(True)
即使值在列中，它也不会输出true;
我试着:
for values in df.column:
if value == values:
print(True)
#Or do something
else:
print(False)
这工作。希望这能有所帮助!
我做了一些简单的测试:
In [10]: x = pd.Series(range(1000000))
In [13]: timeit 999999 in x.values
567 µs ± 25.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [24]: timeit 9 in x.values
666 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [16]: timeit (x == 999999).any()
6.86 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [21]: timeit x.eq(999999).any()
7.03 ms ± 33.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [22]: timeit x.eq(9).any()
7.04 ms ± 60 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [15]: timeit x.isin([999999]).any()
9.54 ms ± 291 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [17]: timeit 999999 in set(x)
79.8 ms ± 1.98 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
有趣的是，不管你是查找9还是999999，似乎使用in语法所花费的时间是相同的(一定是使用了某种向量化计算)
In [24]: timeit 9 in x.values
666 µs ± 15.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [25]: timeit 9999 in x.values
647 µs ± 5.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [26]: timeit 999999 in x.values
642 µs ± 2.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [27]: timeit 99199 in x.values
644 µs ± 5.31 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [28]: timeit 1 in x.values
667 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
似乎使用x.values是最快的，但也许在pandas中有更优雅的方法?
