DataFrame.merge

Merges the input DataFrame with this one using a database-style join.

Joins this DataFrame with the passed in right DataFrame according to the specified Join type. The DataFrame that we apply this on is the left DataFrame. The one passed in as an argument is the right DataFrame. If the join keys do not have the same type, this will error out.

Arguments

Name	Type	Description
right	px.DataFrame	The DataFrame to join with this DataFrame.
how	['inner', 'outer', 'left', 'right'], default 'inner'	the type of merge (join) to perform. * inner: use the intersection of the left and right keys. * outer: use the union of the left and right keys. * left: use the keys from the left DataFrame. * right: use the keys from the right DataFrame.
left_on	Union[string, List[string]]	Column name from this DataFrame, either as a string or a list of strings.
right_on	Union[string, List[string]]	Column name from the right DataFarme to join on. Must be the same type as the `left_on` column.
suffixes	Tuple[string, string], default ['_x', '_y']	The suffixes to apply to duplicate columns.

Returns

px.DataFrame: Merged DataFrame with the relation [left_join_col, ...remaining_left_columns, ...remaining_right_columns].

Examples

# Single join key: Group by UPID and calculate maximum user time for the each UPID group.
left_df = px.DataFrame('process_stats', start_time='-10s')
left_df = left_df.groupby('upid').agg(cpu_utime=('cpu_utime_ns', px.max))
right_df = px.DataFrame('http_events', start_time='-10s')
right_df = right_df.groupby('upid').agg(count=('resp_body', px.count))
df = left_df.merge(right_df, how='inner', left_on='upid', right_on='upid', suffixes=['', '_x'])
# Output relation: ['upid', 'cpu_utime', 'upid_x', 'count']
# Multiple join keys: Calculate maximum user time for the each service/node pair.
left_df = px.DataFrame('process_stats', start_time='-10s')
left_df.node = left_df.ctx['node']
left_df.service = left_df.ctx['service']
left_df = left_df.groupby(['service', 'node']).agg(cpu_utime=('cpu_utime_ns', px.max))
right_df = px.DataFrame('http_events', start_time='-10s')
right_df.node = right_df.ctx['node']
right_df.service = right_df.ctx['service']
right_df = right_df.groupby(['service', 'node']).agg(count=('resp_body', px.count))
df = left_df.merge(right_df, how='inner', left_on=['service', 'node'], right_on=['service', 'node'], suffixes=['', '_x'])
# Output relation: ['service', 'node', 'cpu_utime', 'service_x', 'node_x', 'count']

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