Python 以整数形式获取 Pandas 数据帧行的索引
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Get index of a row of a pandas dataframe as an integer
提问by durbachit
Assume an easy dataframe, for example
假设一个简单的数据框，例如
A B
0 1 0.810743
1 2 0.595866
2 3 0.154888
3 4 0.472721
4 5 0.894525
5 6 0.978174
6 7 0.859449
7 8 0.541247
8 9 0.232302
9 10 0.276566
How can I retrieve an index value of a row, given a condition?
For example:
dfb = df[df['A']==5].index.values.astype(int)returns [4], but what I would like to get is just 4. This is causing me troubles later in the code.
给定条件，如何检索行的索引值？例如：
dfb = df[df['A']==5].index.values.astype(int)返回[4]，但我想得到的只是4. 这给我后面的代码带来了麻烦。
Based on some conditions, I want to have a record of the indexes where that condition is fulfilled, and then select rows between.
根据某些条件，我想记录满足该条件的索引，然后在其中选择行。
I tried
我试过
dfb = df[df['A']==5].index.values.astype(int)
dfbb = df[df['A']==8].index.values.astype(int)
df.loc[dfb:dfbb,'B']
for a desired output
对于所需的输出
A B
4 5 0.894525
5 6 0.978174
6 7 0.859449
but I get TypeError: '[4]' is an invalid key
但我明白了 TypeError: '[4]' is an invalid key
回答by jezrael
The easier is add [0]- select first value of list with one element:
更容易的是添加[0]- 用一个元素选择列表的第一个值：
dfb = df[df['A']==5].index.values.astype(int)[0]
dfbb = df[df['A']==8].index.values.astype(int)[0]
dfb = int(df[df['A']==5].index[0])
dfbb = int(df[df['A']==8].index[0])
But if possible some values not match, error is raised, because first value not exist.
但是如果可能的话，有些值不匹配，则会引发错误，因为第一个值不存在。
Solution is use nextwith iterfor get default parameetr if values not matched:
解决方案是使用next与iter用于获取缺省parameetr如果没有匹配的值：
dfb = next(iter(df[df['A']==5].index), 'no match')
print (dfb)
4
dfb = next(iter(df[df['A']==50].index), 'no match')
print (dfb)
no match
Then it seems need substract 1:
那么似乎需要减去1：
print (df.loc[dfb:dfbb-1,'B'])
4 0.894525
5 0.978174
6 0.859449
Name: B, dtype: float64
Another solution with boolean indexingor query:
使用boolean indexingor 的另一种解决方案query：
print (df[(df['A'] >= 5) & (df['A'] < 8)])
A B
4 5 0.894525
5 6 0.978174
6 7 0.859449
print (df.loc[(df['A'] >= 5) & (df['A'] < 8), 'B'])
4 0.894525
5 0.978174
6 0.859449
Name: B, dtype: float64
print (df.query('A >= 5 and A < 8'))
A B
4 5 0.894525
5 6 0.978174
6 7 0.859449
回答by dmdip
To answer the original question on how to get the index as an integer for the desired selection, the following will work :
要回答有关如何将索引作为所需选择的整数获取的原始问题，以下将起作用：
df[df['A']==5].index.item()
回答by piRSquared
The nature of wanting to include the row where A == 5and all rows upto but notincluding the row where A == 8means we will end up using iloc(locincludes both ends of slice).
想要包含行 whereA == 5和所有行但不包括行 where 的性质A == 8意味着我们最终将使用iloc(loc包括切片的两端)。
In order to get the index labels we use idxmax. This will return the first position of the maximum value. I run this on a boolean series where A == 5(then when A == 8) which returns the index value of when A == 5first happens (same thing for A == 8).
为了获得索引标签，我们使用idxmax. 这将返回最大值的第一个位置。我在一个布尔系列上运行它，其中A == 5(then when A == 8) 返回A == 5第一次发生时的索引值（与 相同A == 8）。
Then I use searchsortedto find the ordinal position of where the index label (that I found above) occurs. This is what I use in iloc.
然后我用它searchsorted来查找索引标签（我在上面找到的）出现的顺序位置。这是我在iloc.
i5, i8 = df.index.searchsorted([df.A.eq(5).idxmax(), df.A.eq(8).idxmax()])
df.iloc[i5:i8]
numpy
麻木
you can further enhance this by using the underlying numpy objects the analogous numpy functions. I wrapped it up into a handy function.
您可以通过使用底层 numpy 对象和类似的 numpy 函数来进一步增强这一点。我把它包装成一个方便的函数。
def find_between(df, col, v1, v2):
vals = df[col].values
mx1, mx2 = (vals == v1).argmax(), (vals == v2).argmax()
idx = df.index.values
i1, i2 = idx.searchsorted([mx1, mx2])
return df.iloc[i1:i2]
find_between(df, 'A', 5, 8)
timing
定时
