Pandas: How to check if the column contains value 0 then sort the selected rows data according to some rule?

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I have a data frame

Testcase   Processing_time   Pass   Fail   avg_failure_rate   Ranking_value
   t1         1.102088        8    26        76.47           69.38
   t2         1.718864        19    3        13.63           7.93
   t3         25              22    0         0               0
   t4         15              22    0         0               0

I want to keep the first two test cases as it is from the above data frame, but I want to sort the rest of the test cases based on the shortest processing time column.

Desired output:

TestCase   Processing_time  Pass  Fail  avg_failure_rate  Ranking_value
   t1         1.102088        8    26        76.47           69.38
   t2         1.718864        19    3        13.63           7.93
   t4         15              22    0         0               0
   t3         25              22    0         0               0

If the test cases have a ranking value equal to 0, they should be sorted based on the shortest processing time rule. Is there any way to accomplish this?

pandas numpy sorting python
3 Answers

Do it with two steps, split the dataframe then concat back

idx = df.index[df.Ranking_value==0]
out = pd.concat([df.drop(idx),df[idx].sort_values('Processing_time')])
Out[120]: 
  Testcase  Processing_time  Pass  Fail  avg_failure_rate  Ranking_value
0       t1         1.102088     8    26             76.47          69.38
1       t2         1.718864    19     3             13.63           7.93
3       t4        15.000000    22     0              0.00           0.00
2       t3        25.000000    22     0              0.00           0.00
user_79645270

Filter with rank value 0 with .loc and sort by .sort_values(). Then append back to the other part with rank value not equal 0 by .append(), as follows:

df.loc[df['Ranking_value'] != 0].append(df.loc[df['Ranking_value'] == 0].sort_values('Processing_time'))

Result:

  Testcase  Processing_time  Pass  Fail  avg_failure_rate  Ranking_value
0       t1         1.102088     8    26             76.47          69.38
1       t2         1.718864    19     3             13.63           7.93
3       t4        15.000000    22     0              0.00           0.00
2       t3        25.000000    22     0              0.00           0.00
user_150706970

Try maskning.

mask = df.loc[:,“Ranking_value”] == 0. 

df.loc[mask,:].sort_values(“Processing_time”,inplace=True)
user_119177300
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