Python 根据条件获取数据框行数
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get dataframe row count based on conditions
提问by Nilani Algiriyage
I want to get the count of dataframe rows based on conditional selection. I tried the following code.
我想根据条件选择获取数据帧行数。我尝试了以下代码。
print df[(df.IP == head.idxmax()) & (df.Method == 'HEAD') & (df.Referrer == '"-"')].count()
output:
输出：
IP 57
Time 57
Method 57
Resource 57
Status 57
Bytes 57
Referrer 57
Agent 57
dtype: int64
The output shows the count for each an every column in the dataframe. Instead I need to get a single count where all of the above conditions satisfied? How to do this? If you need more explanation about my dataframe please let me know.
输出显示数据帧中每一列的计数。相反，我需要在满足上述所有条件的情况下进行一次计数？这该怎么做？如果您需要有关我的数据框的更多解释，请告诉我。
采纳答案by Jeff
You are asking for the condition where all the conditions are true, so len of the frame is the answer, unless I misunderstand what you are asking
你要求的条件是所有条件都为真，所以框架的 len 就是答案，除非我误解了你在问什么
In [17]: df = DataFrame(randn(20,4),columns=list('ABCD'))
In [18]: df[(df['A']>0) & (df['B']>0) & (df['C']>0)]
Out[18]:
A B C D
12 0.491683 0.137766 0.859753 -1.041487
13 0.376200 0.575667 1.534179 1.247358
14 0.428739 1.539973 1.057848 -1.254489
In [19]: df[(df['A']>0) & (df['B']>0) & (df['C']>0)].count()
Out[19]:
A 3
B 3
C 3
D 3
dtype: int64
In [20]: len(df[(df['A']>0) & (df['B']>0) & (df['C']>0)])
Out[20]: 3
回答by Enias Cailliau
For increased performance you should not evaluate the dataframe using your predicate. You can just use the outcome of your predicate directly as illustrated below:
为了提高性能，您不应使用谓词评估数据帧。您可以直接使用谓词的结果，如下所示：
In [1]: import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(20,4),columns=list('ABCD'))
In [2]: df.head()
Out[2]:
A B C D
0 -2.019868 1.227246 -0.489257 0.149053
1 0.223285 -0.087784 -0.053048 -0.108584
2 -0.140556 -0.299735 -1.765956 0.517803
3 -0.589489 0.400487 0.107856 0.194890
4 1.309088 -0.596996 -0.623519 0.020400
In [3]: %time sum((df['A']>0) & (df['B']>0))
CPU times: user 1.11 ms, sys: 53 μs, total: 1.16 ms
Wall time: 1.12 ms
Out[3]: 4
In [4]: %time len(df[(df['A']>0) & (df['B']>0)])
CPU times: user 1.38 ms, sys: 78 μs, total: 1.46 ms
Wall time: 1.42 ms
Out[4]: 4
Keep in mind that this technique only works for counting the number of rows that comply with your predicate.
请记住，此技术仅适用于计算符合谓词的行数。
