python DataFrame.merge()

Python/Pandas

python DataFrame.merge()

쪼쪼랩 2021. 6. 4. 14:57

import pandas as pd

from pandas import DataFrame


df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [1, 2, 3, 5]})
df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [5, 6, 7, 8]})
                    ======OUTPUT=========
df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

                    ======INPUT=========
df1.merge(df2, left_on='lkey', right_on='rkey')

                    ======OUTPUT=========

  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  foo        5  foo        5
3  foo        5  foo        8
4  bar        2  bar        6
5  baz        3  baz        7

DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

Merge DataFrame or named Series objects with a database-style join.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Parameters

right : DataFrame or named Series # merge 할 data 객체 이름

Object to merge with.

how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘inner’

Type of merge to be performed.

left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
cross: creates the cartesian product from both frames, preserves the order of the left keys.
New in version 1.2.0.

onlabel or list # merge의 기준이되는 key 값 (변수)

Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.

left_onlabel or list, or array-like # 왼쪽 DataFrame의 변수를 key로 사용

Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.

right_onlabel or list, or array-like # 오른쪽 DataFrame 의 변수를 key로 사용

Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.

left_indexbool, default False # left_index=True -> 왼쪽 DataFerame의 index를 merge key로 사용

Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.

right_indexbool, default False # right_index=True -> 오른쪽 DataFerame의 index를 merge key로 사용

Use the index from the right DataFrame as the join key. Same caveats as left_index.

sortbool, default False #merge 시키고 join key 기준으로 정렬.

Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword).

suffixeslist-like, default is (“_x”, “_y”) # 중복되는 변수가 있다면 접미사 붙임.

A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.

copybool, default True

If False, avoid copy if possible.

indicatorbool or str, default False

If True, adds a column to the output DataFrame called “_merge” with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left DataFrame, “right_only” for observations whose merge key only appears in the right DataFrame, and “both” if the observation’s merge key is found in both DataFrames.

validatestr, optional

If specified, checks if merge is of specified type.

“one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
“one_to_many” or “1:m”: check if merge keys are unique in left dataset.
“many_to_one” or “m:1”: check if merge keys are unique in right dataset.
“many_to_many” or “m:m”: allowed, but does not result in checks.

ReturnsDataFrame

A DataFrame of the two merged objects.

df1 = pd.DataFrame({'left': ['foo', 'bar']})
df2 = pd.DataFrame({'right': [7, 8]})
df1
    left
0   foo
1   bar
df2
    right
0   7
1   8

# cross
df1.merge(df2, how='cross')
   left  right
0   foo      7
1   foo      8
2   bar      7
3   bar      8

참고 사이트 : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html?highlight=merge#pandas.DataFrame.merge

pandas.DataFrame.merge — pandas 1.2.4 documentation

pandas.pydata.org