Merge two DataFrames based on containing string without iterator

I have two csv files imported as dataframes A and C. I want to match the strings of column content with the entry in data.data that contains the string from A.

A  time_a content    C  time_c data.data
   100    f00           400    otherf00other
   101    ba7           402    onlyrandom
   102    4242          407    otherba7other
                        409    other4242other

Should become:
time_a time_c content
100    400    f00
101    407    ba7
102    409    4242

My solution below uses iterators. But it works too slow. This answer explains why and gives methods how to improve. But I struggle to implement any.

How can I do this with the optimized methods from pandas?

# reset_index() on both df
df_alsa_copy = df_alsa.copy() # Never modify your iterator
df_alsa_copy['cap_fno'] = -1

for aIndex, aRow in df_alsa.iterrows():
    for cIndex, cRow in df_c.iterrows():
        if str(aRow['content']) in str(cRow['data.data']):
            df_alsa_copy.loc[aIndex, 'cap_fno'] = df_c.loc[cIndex, 'frame.number']
# https://stackoverflow.com/questions/31528819/using-merge-on-a-column-and-index-in-pandas
# Merge on frame.number column (bc I chose it to be included in alsa_copy as a column)
df_ltnc = pd.merge(df_alsa_copy, df_c, left_on='cap_fno', right_on='frame.number')

Also tried:

• Would work, if there is an exact match: https://stackoverflow.com/questions/44080248/pandas-join-dataframe-with-condition.
• I also managed to match my second frame against a known string with series.str.contains.
• The problem is, I fail to enter a dataframe column to match in merge on=. I can only enter a known string.
• The same problem arose, when I used apply.
• I did not succeed with isin or similar.

More info:

A holds timestamped content I fed into the program. C is a network capture. I want to know the time in between feeding and capture. I assume:

• The string occur in the same order in A and C.

• But in C there might be lines in between.

• Strings represent hex values.

• data.data contains other chars as well as the string I look for.

• Maybe I lack the pandas vocabulary, to look for the correct method.

Try this approach using pandas.unique(), pandas.Series.str.contains and pandas.DataFrame.merge

unique_str = A['content'].unique()
matching_rows = C[C['data.data'].str.contains('|'.join(unique_str))]

out = pd.merge(matching_rows, A, left_on=matching_rows['data.data']
               .str.extract(f'({"|".join(unique_str)})')[0],
                right_on='content')[['time_a', 'time_c', 'content']]
print(out)

   time_a  time_c content
0     100     400     f00
1     101     407     ba7
2     102     409    4242

if you want to improve speed, a different option you might consider is using Polars (https://www.pola.rs/). You can install Polars with pip install polars

The solution is the same to what @Jamiu proposes, I think he has the right approach. The only difference is Polars instead of Pandas.

I tested the 2 solutions by multiplying the number of rows by 1000. The Pandas solution takes 400ms while the Polars one takes 92ms.

import polars as pl

# convert the data to Polars dataframes
a, c = pl.from_pandas(A), pl.from_pandas(C)

# calculate unique values and join
unique_values = f"({a['content'].unique().str.concat('|').item()})"
out = (
    a
    .join(c.filter(pl.col('data.data').str.contains(unique_values)), 
    left_on = 'content', right_on = pl.col('data.data').str.extract(unique_values))
)

# convert back to Pandas if needed
out_pandas = out.to_pandas()

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