nickckim
commented
1 year ago
def consecutive_duplicates(df):
# Boolean mask of 'series_order' consecutive duplicates
consecutive_duplicates = df['series_order'].eq(df['series_order'].shift())
# If there are any 'series_order' consecutive duplicates
if consecutive_duplicates.any():
# Indicate which 'series_order' have consecutive duplicates in new 'temporary' column
df['temporary'] = consecutive_duplicates
# Iterate through rows of dataframe
for row in df.iloc[1:].itertuples(): # .iloc[1:] required because first row contains float
if row.temporary: # If 'series_order' consecutive duplicate indicated in 'temporary'
# Insert 'Consecutive duplicate detected' cell at index
df['series_order_target'] = pd.concat([
df['series_order_target'].iloc[:row.Index], # Values up to `row.Index`
pd.Series(['Consecutive duplicate detected']), # New Series object with message
df['series_order_target'].iloc[row.Index:] # Values beyond `row.Index`
]).reset_index(drop=True)
# Drop 'temporary' column
df.drop('temporary', axis=1, inplace=True)
# Iterate through rows of dataframe
for row in df.iloc[1:].itertuples(): # .iloc[1:] required because first row contains float
df.at[row.Index, 'order_diff'] = row.series_order_target == row.series_order # Update 'order_diff'
return df```
kcho
The
Scan ordertable in the QQC report checks the series order compared to the template dataset. This table is created byqqc.qqc.dicom.check_order_of_series.You can try using the function by running the lines below in python
However, when a series gets repeated due to an issue in the initial scan, the order of series will be shifted by one and will not match the standard template anymore.
We need a function that
order_check_df(pd.DataFrame)FailintoPassif just extra series were added toseries_ordercolumn.series_order_targetcolumn, and check if this mapping applies to each row in theseries_ordercolumn?