将更正应用于 dataframe 的子采样副本回到原始 dataframe？

Question

I'm a Pandas newbie, so please bear with me.

Overview: I started with a free-form text file created by a data harvesting script that remotely accessed dozens of different kinds of devices, and multiple instances of each. I used OpenRefine ( a truly wonderful tool ) to munge that into a CSV that was then input to dataframe df using Pandas in a JupyterLab notebook.

My first inspection of the data showed the 'Timestamp' column was not monotonic. I accessed individual data sources as follows, in this case for the 'T-meter' data source. ( The technique was taken from a search result - I don't really understand it, but it worked. )

cond = df['Source']=='T-meter'
rows = df.loc[cond, :]
df_tmeter = pd.DataFrame(columns=df.columns)
df_tmeter = df_tmeter.append(rows, ignore_index=True)

then checked each as follows:

df_tmeter['Timestamp'].is_monotonic

Fortunately, the problem was easy to identify and fix: Some sensors were resetting, then sending bad (but still monotonic) timestamps until their clocks were updated. I wrote the function healing() to cleanly patch such errors, and it worked a treat:

df_tmeter['healed'] = df_tmeter['Timestamp'].apply(healing)

Now for my questions:

1. How do I get the 'healed' values back into the original df['Timestamp'] column for only the 'T-meter' items in df['Source'] ?

2. Given the function healing() , is there a clean way to do this directly on df ?

Thanks!

Edit: I first thought I should be using 'views' into df , but other operations on the data would either generate errors, or silently turn the views into copies.

Answer 1

I wrote a wrapper function heal_row() for healing() :

def heal_row( row ):
    if row['Source'] == 'T-meter':   # Redundant check, but safe!
        row['Timestamp'] = healing(row['Timestamp'])
    return row

then did the following:

df = df.apply(lambda row: row if row['Source'] != 'T-meter' else heal_row(row), axis=1)

This ordering is important, since healing() is stateful based on the prior row(s), and thus can't be the default operation.