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# pandas.DataFrame.join

DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False, validate=None)

  Join columns of another DataFrame.

  Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

  Parameters:
    otherDataFrame, Series, or a list containing any combination of them

      Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.

    onstr, list of str, or array-like, optional

      Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.

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

      How to handle the operation of the two objects.

      - left: use calling frame’s index (or column if on is specified)

      - right: use other’s index.

      - outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it lexicographically.

      - inner: form intersection of calling frame’s index (or column if on is specified) with other’s index, preserving the order of the calling’s one.

      - cross: creates the cartesian product from both frames, preserves the order of the left keys.

    lsuffixstr, default ‘’

      Suffix to use from left frame’s overlapping columns.

    rsuffixstr, default ‘’

      Suffix to use from right frame’s overlapping columns.

    sortbool, default False

      Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).

    validatestr, optional

      If specified, checks if join is of specified type.

      - “one_to_one” or “1:1”: check if join keys are unique in both left and right datasets.

      - “one_to_many” or “1:m”: check if join keys are unique in left dataset.

      - “many_to_one” or “m:1”: check if join keys are unique in right dataset.

      - “many_to_many” or “m:m”: allowed, but does not result in checks.

      Added in version 1.5.0.

  Returns:
    DataFrame

      A dataframe containing columns from both the caller and other.

  See also

  `DataFrame.merge`

    For column(s)-on-column(s) operations.

  Notes

  Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

  Examples

      >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
      ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

      >>> df
        key   A
      0  K0  A0
      1  K1  A1
      2  K2  A2
      3  K3  A3
      4  K4  A4
      5  K5  A5

      >>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
      ...                       'B': ['B0', 'B1', 'B2']})

      >>> other
        key   B
      0  K0  B0
      1  K1  B1
      2  K2  B2

  Join DataFrames using their indexes.

      >>> df.join(other, lsuffix='_caller', rsuffix='_other')
        key_caller   A key_other    B
      0         K0  A0        K0   B0
      1         K1  A1        K1   B1
      2         K2  A2        K2   B2
      3         K3  A3       NaN  NaN
      4         K4  A4       NaN  NaN
      5         K5  A5       NaN  NaN

  If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

      >>> df.set_index('key').join(other.set_index('key'))
            A    B
      key
      K0   A0   B0
      K1   A1   B1
      K2   A2   B2
      K3   A3  NaN
      K4   A4  NaN
      K5   A5  NaN

  Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result.

      >>> df.join(other.set_index('key'), on='key')
        key   A    B
      0  K0  A0   B0
      1  K1  A1   B1
      2  K2  A2   B2
      3  K3  A3  NaN
      4  K4  A4  NaN
      5  K5  A5  NaN

  Using non-unique key values shows how they are matched.

      >>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
      ...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

      >>> df
        key   A
      0  K0  A0
      1  K1  A1
      2  K1  A2
      3  K3  A3
      4  K0  A4
      5  K1  A5

      >>> df.join(other.set_index('key'), on='key', validate='m:1')
        key   A    B
      0  K0  A0   B0
      1  K1  A1   B1
      2  K1  A2   B1
      3  K3  A3  NaN
      4  K0  A4   B0
      5  K1  A5   B1