英文:
how to count failure occurrences in a column using pandas?
问题
我需要使用Python的pandas来以CSV格式整理测试结果。结果可以是"passed"或有时是"failed"。在我导入Python和我的代码之后，代码如下：
import pandas as pd
df = pd.read_csv('myfile.csv')
pass_res = df['Status'].value_counts()['passed']
fail_res = df['Status'].value_counts().get('failed', 0)
这段代码将在有失败的情况下运行。然而，如果没有失败，最后一行代码会导致错误。如何检查是否有失败，然后我将执行最后一行呢？
英文:
I need to use python's pandas to tabulate the test result in a csv format. The result could be "passsed" or sometime "failed". After I
import python as pd,my code is:
df = pd.read_csv('myfile.csv')
pass_res =df['Status'].value_counts()['passed']
fail_res =df['Status'].value_counts()['failed']
this code will work if there IS a case of fail. However, when there is no failure, the last line of code will cause an error. How do check, if there is a failure, then I will execute my last line.
答案1
得分: 2
使用Series.get来提取找到的值，否则返回0。
s = df['Status'].value_counts()
passed = s.get('passed', 0)
failed = s.get('failed', 0)
英文:
Lets use Series.get to yank the value if found otherwise return 0
s = df['Status'].value_counts()
passed = s.get('passed', 0)
failed = s.get('failed', 0)
答案2
得分: 2
以下是代码的翻译部分：
# 示例
df = pd.DataFrame({'Status': ['passed']*5 + ['other']*3})
status = pd.CategoricalDtype(['passed', 'failed'], ordered=True)
passed, failed = df['Status'].astype(status).value_counts().sort_index()
输出：
>>> passed
5
>>> failed
0
>>> df['Status'].astype(status).value_counts().sort_index()
Status
passed 5
failed 0
Name: count, dtype: int64
>>> df
Status
0 passed
1 passed
2 passed
3 passed
4 passed
5 other
6 other
7 other
请注意，上述内容只是代码的翻译，不包括问题的回答。
英文:
You can also add a CategoricalDType as value_counts returns all observed:
# sample
df = pd.DataFrame({'Status': ['passed']*5 + ['other']*3})
status = pd.CategoricalDtype(['passed', 'failed'], ordered=True)
passed, failed = df['Status'].astype(status).value_counts().sort_index()
Output:
>>> passed
5
>>> failed
0
>>> df['Status'].astype(status).value_counts().sort_index()
Status
passed 5
failed 0
Name: count, dtype: int64
>>> df
Status
0 passed
1 passed
2 passed
3 passed
4 passed
5 other
6 other
7 other
答案3
得分: 1
与@Corralien的方法类似，但使用reindex函数：
df['Status'].value_counts(sort=False).reindex(['passed', 'failed'], fill_value=0)
一次性定义变量：
passed, failed = (df['Status'].value_counts(sort=False)
.reindex(['passed', 'failed'], fill_value=0)
)
英文:
Similar to @Corralien's but with reindex:
df['Status'].value_counts(sort=False).reindex(['passed', 'failed'], fill_value=0)
Defining the variables in one shot:
passed, failed = (df['Status'].value_counts(sort=False)
.reindex(['passed', 'failed'], fill_value=0)
)
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