Python 检查熊猫数据帧索引中是否存在值
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Check if a value exists in pandas dataframe index
提问by Abhi
I am sure there is an obvious way to do this but cant think of anything slick right now.
我相信有一种明显的方法可以做到这一点，但现在想不出任何巧妙的方法。
Basically instead of raising exception I would like to get Trueor Falseto see if a value exists in pandas dfindex.
基本上，我想获取True或False查看熊猫df索引中是否存在值而不是引发异常。
import pandas as pd
df = pd.DataFrame({'test':[1,2,3,4]}, index=['a','b','c','d'])
df.loc['g'] # (should give False)
What I have working now is the following
我现在的工作如下
sum(df.index == 'g')
采纳答案by Guillaume Jacquenot
This should do the trick
这应该可以解决问题
'g' in df.index
回答by Ezekiel Kruglick
Just for reference as it was something I was looking for, you can test for presence within the values or the index by appending the ".values" method, e.g.
仅供参考，因为它是我正在寻找的东西，您可以通过附加“.values”方法来测试值或索引中是否存在，例如
g in df.<your selected field>.values
g in df.index.values
I find that adding the ".values" to get a simple list or ndarray out makes exist or "in" checks run more smoothly with the other python tools. Just thought I'd toss that out there for people.
我发现添加“.values”以获得一个简单的列表或 ndarray 使得存在或“in”检查与其他 python 工具一起运行更顺畅。只是想我会把它扔给人们。
回答by Gank
df = pandas.DataFrame({'g':[1]}, index=['isStop'])
#df.loc['g']
if 'g' in df.index:
print("find g")
if 'isStop' in df.index:
print("find a")
回答by broccoli2000
Multi index works a little different from single index. Here are some methods for multi-indexed dataframe.
多索引的工作原理与单索引略有不同。以下是多索引数据帧的一些方法。
df = pd.DataFrame({'col1': ['a', 'b','c', 'd'], 'col2': ['X','X','Y', 'Y'], 'col3': [1, 2, 3, 4]}, columns=['col1', 'col2', 'col3'])
df = df.set_index(['col1', 'col2'])
in df.indexworks for the first level only when checking single index value.
in df.index仅在检查单个索引值时适用于第一级。
'a' in df.index # True
'X' in df.index # False
Check df.index.levelsfor other levels.
检查df.index.levels其他级别。
'a' in df.index.levels[0] # True
'X' in df.index.levels[1] # True
Check in df.indexfor an index combination tuple.
签入df.index索引组合元组。
('a', 'X') in df.index # True
('a', 'Y') in df.index # False
回答by Quoc Chi
with DataFrame: df_data
使用数据框：df_data
>>> df_data
id name value
0 a ampha 1
1 b beta 2
2 c ce 3
I tried:
我试过：
>>> getattr(df_data, 'value').isin([1]).any()
True
>>> getattr(df_data, 'value').isin(['1']).any()
True
but:
但：
>>> 1 in getattr(df_data, 'value')
True
>>> '1' in getattr(df_data, 'value')
False
So fun :D
太有趣了 :D
回答by xxyjoel
Code below does not print boolean, but allows for dataframe subsetting by index... I understand this is likely not the most efficient way to solve the problem, but I (1) like the way this reads and (2) you can easily subset where df1 index exists in df2:
下面的代码不打印布尔值，但允许按索引对数据帧进行子集设置...我知道这可能不是解决问题的最有效方法，但我 (1) 喜欢这种读取方式和 (2) 您可以轻松地进行子集化其中 df1 索引存在于 df2 中：
df3 = df1[df1.index.isin(df2.index)]
or where df1 index does not exist in df2...
或者 df2 中不存在 df1 索引...
df3 = df1[~df1.index.isin(df2.index)]
