pandas合并

concat

Concatenate pandas objects along a particular axis.

Allows optional set logic along the other axes.

Can also add a layer of hierarchical indexing on the concatenation
axis, which may be useful if the labels are the same (or overlapping)
on the passed axis number.
pandas.concat

pandas.concat(objs, *, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=True)
1

Merge vertically by default

a = pd.DataFrame(np.random.normal(0, 1, (3, 3)))
b = pd.DataFrame(np.random.normal(0, 1, (5, 5)))
c = pd.concat([a, b], axis=1, join='inner', ignore_index=True)
1
2
3

a：

b：

c：

merge

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

A named Series object is treated as a DataFrame with a single named
column.

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.
pandas.merge

pandas.merge(left, 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)
1

You can use Pandas merge to implement vlookup like functions

a = pd.DataFrame(np.random.normal(0, 1, (3, 3)))
a['merge'] = [1, 2, 3]
b = pd.DataFrame(np.random.normal(0, 1, (5, 5)), index=[2, 9, 10, 11, 12])
b['merge'] = [2, 3, 4, 5, 6]
c = pd.merge(a, b, how='left', on='merge', suffixes=('_a', '_b'))
1
2
3
4
5

a:

b:

c：

join

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.
pandas.DataFrame.join

DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False, validate=None)
1

joins index-on-index by default

a = pd.DataFrame(np.random.normal(0, 1, (3, 3)))
a['merge'] = [1, 2, 3]
b = pd.DataFrame(np.random.normal(0, 1, (5, 5)), index=[2, 9, 10, 11, 12])
b['merge'] = [2, 3, 4, 5, 6]
c = a.join(b,  rsuffix='_b')
1
2
3
4
5

a:

b:

c: