Функция merge pandas python

pandas.DataFrame.merge#

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 = None , indicator = False , validate = None ) [source] #

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.

If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

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.

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 label or list, or array-like

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 label or list, or array-like

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 bool, default False

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 bool, default False

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

sort bool, default False

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

suffixes list-like, default is (“_x”, “_y”)

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.

copy bool, default True

If False, avoid copy if possible.

indicator bool 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.

validate str, 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.

A DataFrame of the two merged objects.

Merge with optional filling/interpolation.

Similar method using indices.

Support for specifying index levels as the on , left_on , and right_on parameters was added in version 0.23.0 Support for merging named Series objects was added in version 0.24.0

>>> 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]>) >>> 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.

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

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', . suffixes=('_left', '_right')) lkey value_left rkey value_right 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 

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False)) Traceback (most recent call last): . ValueError: columns overlap but no suffix specified: Index(['value'], dtype='object') 

>>> df1 = pd.DataFrame('a': ['foo', 'bar'], 'b': [1, 2]>) >>> df2 = pd.DataFrame('a': ['foo', 'baz'], 'c': [3, 4]>) >>> df1 a b 0 foo 1 1 bar 2 >>> df2 a c 0 foo 3 1 baz 4 

>>> df1.merge(df2, how='inner', on='a') a b c 0 foo 1 3 

>>> df1.merge(df2, how='left', on='a') a b c 0 foo 1 3.0 1 bar 2 NaN 

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

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

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Pandas merge() — Merging Two DataFrame Objects

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Pandas DataFrame merge() function is used to merge two DataFrame objects with a database-style join operation. The joining is performed on columns or indexes. If the joining is done on columns, indexes are ignored. This function returns a new DataFrame and the source DataFrame objects are unchanged.

Pandas DataFrame merge() Function Syntax

def merge( self, 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, ) 

• right: The other DataFrame to merge with the source DataFrame.
• how: , default ‘inner’. This is the most important parameter to define the merge operation type. These are similar to SQL left outer join, right outer join, full outer join, and inner join.
• on: Column or index level names to join on. These columns must be present in both the DataFrames. If not provided, the intersection of the columns in both DataFrames are used.
• left_on: Column or index level names to join on in the left DataFrame.
• right_on: Column or index level names to join on in the right DataFrame.
• left_index: Use the index from the left DataFrame as the join key(s).
• right_index: Use the index from the right DataFrame as the join key.
• sort: Sort the join keys lexicographically in the result DataFrame.
• suffixes: Suffix to apply to overlapping column names in the left and right side, respectively.
• indicator: If True, adds a column to output DataFrame called “_merge” with information on the source of each row.
• validate: used to validate the merge process. The valid values are .

Pandas DataFrame merge() Examples

Let’s look at some examples of merging two DataFrame objects.

1. Default Merging — inner join

import pandas as pd d1 = df1 = pd.DataFrame(d1) print('DataFrame 1:\n', df1) df2 = pd.DataFrame() print('DataFrame 2:\n', df2) df_merged = df1.merge(df2) print('Result:\n', df_merged) 

DataFrame 1: Name Country Role 0 Pankaj India CEO 1 Meghna India CTO 2 Lisa USA CTO DataFrame 2: ID Name 0 1 Pankaj 1 2 Anupam 2 3 Amit Result: Name Country Role ID 0 Pankaj India CEO 1 

2. Merging DataFrames with Left, Right, and Outer Join

print('Result Left Join:\n', df1.merge(df2, how='left')) print('Result Right Join:\n', df1.merge(df2, how='right')) print('Result Outer Join:\n', df1.merge(df2, how='outer')) 

Result Left Join: Name Country Role ID 0 Pankaj India CEO 1.0 1 Meghna India CTO NaN 2 Lisa USA CTO NaN Result Right Join: Name Country Role ID 0 Pankaj India CEO 1 1 Anupam NaN NaN 2 2 Amit NaN NaN 3 Result Outer Join: Name Country Role ID 0 Pankaj India CEO 1.0 1 Meghna India CTO NaN 2 Lisa USA CTO NaN 3 Anupam NaN NaN 2.0 4 Amit NaN NaN 3.0 

3. Merging DataFrame on Specific Columns

import pandas as pd d1 = df1 = pd.DataFrame(d1) df2 = pd.DataFrame() print(df1.merge(df2, on='ID')) print(df1.merge(df2, on='Name')) 

 Name_x ID Country Role Name_y 0 Pankaj 1 India CEO Pankaj 1 Meghna 2 India CTO Anupam 2 Lisa 3 USA CTO Amit Name ID_x Country Role ID_y 0 Pankaj 1 India CEO 1 

4. Specify Left and Right Columns for Merging DataFrame Objects

import pandas as pd d1 = df1 = pd.DataFrame(d1) df2 = pd.DataFrame() print(df1.merge(df2)) print(df1.merge(df2, left_on='ID1', right_on='ID2')) 

 Name ID1 Country Role ID2 0 Pankaj 1 India CEO 1 Name_x ID1 Country Role ID2 Name_y 0 Pankaj 1 India CEO 1 Pankaj 1 Meghna 2 India CTO 2 Anupam 2 Lisa 3 USA CTO 3 Amit 

5. Using Index as the Join Keys for Merging DataFrames

import pandas as pd d1 = df1 = pd.DataFrame(d1) df2 = pd.DataFrame() df_merged = df1.merge(df2) print('Result Default Merge:\n', df_merged) df_merged = df1.merge(df2, left_index=True, right_index=True) print('\nResult Index Merge:\n', df_merged) 

Result Default Merge: Name Country Role ID 0 Pankaj India CEO 1 Result Index Merge: Name_x Country Role ID Name_y 0 Pankaj India CEO 1 Pankaj 1 Meghna India CTO 2 Anupam 2 Lisa USA CTO 3 Amit 

References

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