Counting rows based on multiple conditions and add to list in pandas dataframe

Question

I have a file which is needed for some summarizing statistics and I have a list of various robots and the status of their execution. My issue is that I've indexed them to a new dataframe using the following code:

x1 = df["Title"].value_counts().index.tolist()

I have several columns where eg. 'Title' (Name of specific robot) is in column A and a column D named 'Status'(where it either states Completed or Failed)

How do I count the number of occurrences of the specific robot in column A with the condition that is says completed in column D?

import pandas as pd
df = pd.DataFrame({'Title':['Robot1', 'Robot1', 'Robot3', 'Robot1', 'Robot3'], 'Status':['Completed', 'Failed', 'Running', 'Completed', 'Completed']})

print(df.to_string(index=False))

Title       Status
Robot1      Completed
Robot1      Failed
Robot3      Running
Robot1      Completed
Robot3      Completed

difficult to answer without having an example of the data and the expected output — mozway, Jul 18 '22 at 09:37
Sorry I am very new to coding and this platform and cannot post pictures — Oliver Løkke, Jul 18 '22 at 09:42
Good, because pictures are useless, [reproducible data **as text**](https://stackoverflow.com/questions/20109391/how-to-make-good-reproducible-pandas-examples) is uselful ;) — mozway, Jul 18 '22 at 09:45
I'm following the question, looking forward to your update ;) — mozway, Jul 18 '22 at 09:53

score 0 · Answer 1 · answered Jul 18 '22 at 10:54

0

Just slice before value_counts:

df.loc[df['Status'].eq('Completed'), 'Title'].value_counts()

Output:

Robot1    2
Robot3    1
Name: Title, dtype: int64

answered Jul 18 '22 at 10:54

mozway

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Counting rows based on multiple conditions and add to list in pandas dataframe

1 Answers1