Pandas - Find and index rows that match row sequence pattern

Question

I would like to find a pattern in a dataframe in a categorical variable going down rows. I can see how to use Series.shift() to look up / down and using boolean logic to fi

Answer 1

Expanding on Emmet02's answer: using the rolling function for all groups and setting match-column to 1 for all matching pattern indices:

pattern = np.asarray([1,2,2,0])

# Create a match column in the main dataframe
df.assign(match=False, inplace=True)

for group_var, group in df.groupby("group_var"):

    # Per group do rolling window matching, the last 
    # values of matching patterns in array 'match'
    # will be True
    match = (
        group['row_pat']
        .rolling(window=len(pattern), min_periods=len(pattern))
        .apply(lambda x: (x==pattern).all())
    )

    # Get indices of matches in current group
    idx = np.arange(len(group))[match == True]

    # Include all indices of matching pattern, 
    # counting back from last index in pattern
    idx = idx.repeat(len(pattern)) - np.tile(np.arange(len(pattern)), len(idx))

    # Update matches
    match.values[idx] = True
    df.loc[group.index, 'match'] = match

df[df.match==True]

edit: Without a for loop

# Do rolling matching in group clause
match = (
    df.groupby("group_var")
    .rolling(len(pattern))
    .row_pat.apply(lambda x: (x==pattern).all())
)

# Convert NaNs
match = (~match.isnull() & match)

# Get indices of matches in current group
idx = np.arange(len(df))[match]
# Include all indices of matching pattern
idx = idx.repeat(len(pattern)) - np.tile(np.arange(len(pattern)), len(idx))

# Mark all indices that are selected by "idx" in match-column
df = df.assign(match=df.index.isin(df.index[idx]))