pyspark.pandas.merge#

pyspark.pandas.merge(obj, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, suffixes=('_x', '_y'))[source]#

Merge DataFrame objects with a database-style join.

The index of the resulting DataFrame will be one of the following:

  • 0…n if no index is used for merging

  • Index of the left DataFrame if merged only on the index of the right DataFrame

  • Index of the right DataFrame if merged only on the index of the left DataFrame

  • All involved indices if merged using the indices of both DataFrames

    e.g. if left with indices (a, x) and right with indices (b, x), the result will be an index (x, a, b)

Parameters
right: Object to merge with.
how: Type of merge to be performed.

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

left: use only keys from left frame, like a SQL left outer join; preserve key

order.

right: use only keys from right frame, like a SQL right outer join; preserve key

order.

outer: use union of keys from both frames, like a SQL full outer join; sort keys

lexicographically.

inner: use intersection of keys from both frames, like a SQL inner join;

preserve the order of the left keys.

on: 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_on: 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_on: 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_index: 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_index: Use the index from the right DataFrame as the join key. Same caveats as

left_index.

suffixes: Suffix to apply to overlapping column names in the left and right side,

respectively.

Returns
DataFrame

A DataFrame of the two merged objects.

Notes

As described in #263, joining string columns currently returns None for missing values

instead of NaN.

Examples

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

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> merged = ps.merge(df1, df2, left_on='lkey', right_on='rkey')
>>> merged.sort_values(by=['lkey', 'value_x', 'rkey', 'value_y'])  
  lkey  value_x rkey  value_y
...bar        2  bar        6
...baz        3  baz        7
...foo        1  foo        5
...foo        1  foo        8
...foo        5  foo        5
...foo        5  foo        8

>>> left_psdf = ps.DataFrame({'A': [1, 2]})
>>> right_psdf = ps.DataFrame({'B': ['x', 'y']}, index=[1, 2])

>>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True).sort_index()
   A  B
1  2  x

>>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='left').sort_index()
   A     B
0  1  None
1  2     x

>>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='right').sort_index()
     A  B
1  2.0  x
2  NaN  y

>>> ps.merge(left_psdf, right_psdf, left_index=True, right_index=True, how='outer').sort_index()
     A     B
0  1.0  None
1  2.0     x
2  NaN     y