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SQL-style merge routines
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groupby & merge; we are always performing a left-by type operation
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|
Parameters
|
----------
|
by: field to group
|
left: DataFrame
|
right: DataFrame
|
merge_pieces: function for merging
|
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d« t ||| fd« \ } } | S | |« } | S ) a)
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Perform a merge for ordered data with optional filling/interpolation.
|
|
Designed for ordered data like time series data. Optionally
|
perform group-wise merge (see examples).
|
|
Parameters
|
----------
|
left : DataFrame or named Series
|
right : DataFrame or named Series
|
on : label or list
|
Field names to join on. Must be found in both DataFrames.
|
left_on : label or list, or array-like
|
Field names to join on in left DataFrame. Can be a vector or list of
|
vectors of the length of the DataFrame to use a particular vector as
|
the join key instead of columns.
|
right_on : label or list, or array-like
|
Field names to join on in right DataFrame or vector/list of vectors per
|
left_on docs.
|
left_by : column name or list of column names
|
Group left DataFrame by group columns and merge piece by piece with
|
right DataFrame. Must be None if either left or right are a Series.
|
right_by : column name or list of column names
|
Group right DataFrame by group columns and merge piece by piece with
|
left DataFrame. Must be None if either left or right are a Series.
|
fill_method : {'ffill', None}, default None
|
Interpolation method for data.
|
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.
|
|
how : {'left', 'right', 'outer', 'inner'}, default 'outer'
|
* left: use only keys from left frame (SQL: left outer join)
|
* right: use only keys from right frame (SQL: right outer join)
|
* outer: use union of keys from both frames (SQL: full outer join)
|
* inner: use intersection of keys from both frames (SQL: inner join).
|
|
Returns
|
-------
|
DataFrame
|
The merged DataFrame output type will be the same as
|
'left', if it is a subclass of DataFrame.
|
|
See Also
|
--------
|
merge : Merge with a database-style join.
|
merge_asof : Merge on nearest keys.
|
|
Examples
|
--------
|
>>> from pandas import merge_ordered
|
>>> df1 = pd.DataFrame(
|
... {
|
... "key": ["a", "c", "e", "a", "c", "e"],
|
... "lvalue": [1, 2, 3, 1, 2, 3],
|
... "group": ["a", "a", "a", "b", "b", "b"]
|
... }
|
... )
|
>>> df1
|
key lvalue group
|
0 a 1 a
|
1 c 2 a
|
2 e 3 a
|
3 a 1 b
|
4 c 2 b
|
5 e 3 b
|
|
>>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
|
>>> df2
|
key rvalue
|
0 b 1
|
1 c 2
|
2 d 3
|
|
>>> merge_ordered(df1, df2, fill_method="ffill", left_by="group")
|
key lvalue group rvalue
|
0 a 1 a NaN
|
1 b 1 a 1.0
|
2 c 2 a 2.0
|
3 d 2 a 3.0
|
4 e 3 a 3.0
|
5 a 1 b NaN
|
6 b 1 b 1.0
|
7 c 2 b 2.0
|
8 d 2 b 3.0
|
9 e 3 b 3.0
|
c
|
óJ t | |¬« }|j « S )N)rH rI rJ rN Ú fill_methodrR )Ú _OrderedMergerW ) ÚxÚyr\ r rR rI rH rJ rN s r] Ú_mergerz merge_ordered.<locals>._merger s3 ø ä Ø Ø ØØØ Ø Ø#Øô
|
ð }}Ð r_ z*Can only group either left or right framesr z not found in left columnsc ó | |« S rm rd ©r r r s r] ú<lambda>z merge_ordered.<locals>.<lambda>© s ø É'ÐRSÐUVË- r_ z not found in right columnsc ó || « S rm rd r s r] r z merge_ordered.<locals>.<lambda>± s ø ±¸¸1³ r_ )ÚreturnrA ) Ú
|
ValueErrorrw Ústrr Ú
|
differencero r
r r )rX rY rH rI rJ Úleft_byÚright_byr rN rR Úcheckr Ú_r s ``` ``` @r] Ú merge_orderedrª + s þ ÷P ò ð ÐxÐ 3ÜÐEÓFÐFØÐÜ gsÔ #Ø iGÜG ×'Ñ'¨¯ © Ó5Ü u:?ÜeWÐ$>Ð?Ó@Ð @Ü& w°°eÓ=WÓX ð Mð
|
Ð Ü h¤Ô $Ø zHÜH ×(Ñ(¨¯ © Ó7Ü u:?ÜeWÐ$?Ð@ÓAÐ AÜ&Ø eTÓ#=ó
|
ð
|
Mñ uÓ%Ø Mr_ c óV t | ||||||||| |
|
d| | | ¬« }|j « S )ax"
|
Perform a merge by key distance.
|
|
This is similar to a left-join except that we match on nearest
|
key rather than equal keys. Both DataFrames must be sorted by the key.
|
|
For each row in the left DataFrame:
|
|
- A "backward" search selects the last row in the right DataFrame whose
|
'on' key is less than or equal to the left's key.
|
|
- A "forward" search selects the first row in the right DataFrame whose
|
'on' key is greater than or equal to the left's key.
|
|
- A "nearest" search selects the row in the right DataFrame whose 'on'
|
key is closest in absolute distance to the left's key.
|
|
Optionally match on equivalent keys with 'by' before searching with 'on'.
|
|
Parameters
|
----------
|
left : DataFrame or named Series
|
right : DataFrame or named Series
|
on : label
|
Field name to join on. Must be found in both DataFrames.
|
The data MUST be ordered. Furthermore this must be a numeric column,
|
such as datetimelike, integer, or float. On or left_on/right_on
|
must be given.
|
left_on : label
|
Field name to join on in left DataFrame.
|
right_on : label
|
Field name to join on in right DataFrame.
|
left_index : bool
|
Use the index of the left DataFrame as the join key.
|
right_index : bool
|
Use the index of the right DataFrame as the join key.
|
by : column name or list of column names
|
Match on these columns before performing merge operation.
|
left_by : column name
|
Field names to match on in the left DataFrame.
|
right_by : column name
|
Field names to match on in the right DataFrame.
|
suffixes : 2-length sequence (tuple, list, ...)
|
Suffix to apply to overlapping column names in the left and right
|
side, respectively.
|
tolerance : int or Timedelta, optional, default None
|
Select asof tolerance within this range; must be compatible
|
with the merge index.
|
allow_exact_matches : bool, default True
|
|
- If True, allow matching with the same 'on' value
|
(i.e. less-than-or-equal-to / greater-than-or-equal-to)
|
- If False, don't match the same 'on' value
|
(i.e., strictly less-than / strictly greater-than).
|
|
direction : 'backward' (default), 'forward', or 'nearest'
|
Whether to search for prior, subsequent, or closest matches.
|
|
Returns
|
-------
|
DataFrame
|
|
See Also
|
--------
|
merge : Merge with a database-style join.
|
merge_ordered : Merge with optional filling/interpolation.
|
|
Examples
|
--------
|
>>> left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
|
>>> left
|
a left_val
|
0 1 a
|
1 5 b
|
2 10 c
|
|
>>> right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
|
>>> right
|
a right_val
|
0 1 1
|
1 2 2
|
2 3 3
|
3 6 6
|
4 7 7
|
|
>>> pd.merge_asof(left, right, on="a")
|
a left_val right_val
|
0 1 a 1
|
1 5 b 3
|
2 10 c 7
|
|
>>> pd.merge_asof(left, right, on="a", allow_exact_matches=False)
|
a left_val right_val
|
0 1 a NaN
|
1 5 b 3.0
|
2 10 c 7.0
|
|
>>> pd.merge_asof(left, right, on="a", direction="forward")
|
a left_val right_val
|
0 1 a 1.0
|
1 5 b 6.0
|
2 10 c NaN
|
|
>>> pd.merge_asof(left, right, on="a", direction="nearest")
|
a left_val right_val
|
0 1 a 1
|
1 5 b 6
|
2 10 c 7
|
|
We can use indexed DataFrames as well.
|
|
>>> left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
|
>>> left
|
left_val
|
1 a
|
5 b
|
10 c
|
|
>>> right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
|
>>> right
|
right_val
|
1 1
|
2 2
|
3 3
|
6 6
|
7 7
|
|
>>> pd.merge_asof(left, right, left_index=True, right_index=True)
|
left_val right_val
|
1 a 1
|
5 b 3
|
10 c 7
|
|
Here is a real-world times-series example
|
|
>>> quotes = pd.DataFrame(
|
... {
|
... "time": [
|
... pd.Timestamp("2016-05-25 13:30:00.023"),
|
... pd.Timestamp("2016-05-25 13:30:00.023"),
|
... pd.Timestamp("2016-05-25 13:30:00.030"),
|
... pd.Timestamp("2016-05-25 13:30:00.041"),
|
... pd.Timestamp("2016-05-25 13:30:00.048"),
|
... pd.Timestamp("2016-05-25 13:30:00.049"),
|
... pd.Timestamp("2016-05-25 13:30:00.072"),
|
... pd.Timestamp("2016-05-25 13:30:00.075")
|
... ],
|
... "ticker": [
|
... "GOOG",
|
... "MSFT",
|
... "MSFT",
|
... "MSFT",
|
... "GOOG",
|
... "AAPL",
|
... "GOOG",
|
... "MSFT"
|
... ],
|
... "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
|
... "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]
|
... }
|
... )
|
>>> quotes
|
time ticker bid ask
|
0 2016-05-25 13:30:00.023 GOOG 720.50 720.93
|
1 2016-05-25 13:30:00.023 MSFT 51.95 51.96
|
2 2016-05-25 13:30:00.030 MSFT 51.97 51.98
|
3 2016-05-25 13:30:00.041 MSFT 51.99 52.00
|
4 2016-05-25 13:30:00.048 GOOG 720.50 720.93
|
5 2016-05-25 13:30:00.049 AAPL 97.99 98.01
|
6 2016-05-25 13:30:00.072 GOOG 720.50 720.88
|
7 2016-05-25 13:30:00.075 MSFT 52.01 52.03
|
|
>>> trades = pd.DataFrame(
|
... {
|
... "time": [
|
... pd.Timestamp("2016-05-25 13:30:00.023"),
|
... pd.Timestamp("2016-05-25 13:30:00.038"),
|
... pd.Timestamp("2016-05-25 13:30:00.048"),
|
... pd.Timestamp("2016-05-25 13:30:00.048"),
|
... pd.Timestamp("2016-05-25 13:30:00.048")
|
... ],
|
... "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
|
... "price": [51.95, 51.95, 720.77, 720.92, 98.0],
|
... "quantity": [75, 155, 100, 100, 100]
|
... }
|
... )
|
>>> trades
|
time ticker price quantity
|
0 2016-05-25 13:30:00.023 MSFT 51.95 75
|
1 2016-05-25 13:30:00.038 MSFT 51.95 155
|
2 2016-05-25 13:30:00.048 GOOG 720.77 100
|
3 2016-05-25 13:30:00.048 GOOG 720.92 100
|
4 2016-05-25 13:30:00.048 AAPL 98.00 100
|
|
By default we are taking the asof of the quotes
|
|
>>> pd.merge_asof(trades, quotes, on="time", by="ticker")
|
time ticker price quantity bid ask
|
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
|
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
|
2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93
|
3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93
|
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
|
|
We only asof within 2ms between the quote time and the trade time
|
|
>>> pd.merge_asof(
|
... trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms")
|
... )
|
time ticker price quantity bid ask
|
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
|
1 2016-05-25 13:30:00.038 MSFT 51.95 155 NaN NaN
|
2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93
|
3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93
|
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
|
|
We only asof within 10ms between the quote time and the trade time
|
and we exclude exact matches on time. However *prior* data will
|
propagate forward
|
|
>>> pd.merge_asof(
|
... trades,
|
... quotes,
|
... on="time",
|
... by="ticker",
|
... tolerance=pd.Timedelta("10ms"),
|
... allow_exact_matches=False
|
... )
|
time ticker price quantity bid ask
|
0 2016-05-25 13:30:00.023 MSFT 51.95 75 NaN NaN
|
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
|
2 2016-05-25 13:30:00.048 GOOG 720.77 100 NaN NaN
|
3 2016-05-25 13:30:00.048 GOOG 720.92 100 NaN NaN
|
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
|
Úasof) rH rI rJ rK rL r r¦ r§ rN rR Ú toleranceÚallow_exact_matchesÚ direction)Ú
|
_AsOfMergerW )rX rY rH rI rJ rK rL r r¦ r§ rN r r® r¯ r\ s r] Ú
|
merge_asofr± ¸ sJ ôv
|
Ø Ø Ø ØØØ Ø Ø ØØØØ ØØ /Øô
|
Bð" ==?Ðr_ c óH e Zd ZU dZdZded< ded< ded< ded < d
|
ed < d
|
ed < d
|
ed < ded< d
|
ed< ded< ded< ded< ded< ded< d, d-dZ d.d Zd/d Ze d0d « Z
|
d1d2d Z e e d3d « « Z e d4d!« Ze d5d"« Ze d6d#« Ze d7d$« Z d8d%Ze d9d&« Ze d: d;d'« Ze d<d(« Ze d=d)« Zd* Ze d>d+« Zy)?rV z
|
Perform a database (SQL) merge operation between two DataFrame or Series
|
objects using either columns as keys or their row indexes
|
r^ úJoinHow | Literal['asof']rR úIndexLabel | NonerH z!Sequence[Hashable | AnyArrayLike]rI rJ ÚboolrK rL rM r rN rQ ú
|
str | boolrO ú
|
str | NonerP zlist[Hashable]Ú
|
join_namesúlist[ArrayLike]Úright_join_keysÚleft_join_keysNc ó2 t |« } t |« }| x| _ | _ |x| _ | _ || _ t j |« | _ |
|
| _ | xs |dk( | _
|
|| _ || _ | | _ t |« st dt! |« « t |« st dt! |« « | j" j$ |j" j$ k7 r<d| j" j$ d|j" j$ d}t' |« | j) ||« \ | _ | _ | j/ « \ | _ | _ | _ }}|r | j j7 |« | _ |r | j j7 |« | _ | j9 | j0 | j2 « | j; | j0 « | j= « | | j? | « y y )NÚouterz/left_index parameter must be of type bool, not z0right_index parameter must be of type bool, not z0Not allowed to merge between different levels. (z levels on the left, z on the right)) rT rX Ú orig_leftrY Ú
|
orig_rightrR ÚcomÚmaybe_make_listrH rN rM rK rL rO r! r£ Útypero Únlevelsr Ú_validate_left_right_onrI rJ Ú_get_merge_keysr» rº r¸ Ú_drop_labels_or_levelsÚ _maybe_require_matching_dtypesÚ_validate_toleranceÚ_maybe_coerce_merge_keysÚ_validate_validate_kwd)ÚselfrX rY rR rH rI rJ rK rL rM rN rO rP Ú_leftÚ_rightÚmsgÚ left_dropÚ
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right_drops r] Ú__init__z_MergeOperation.__init__á sö ô " $Ó'Ü" 5Ó)Ø%*Ð* DNØ'-Ð-
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T_Øä×%Ñ% bÓ)à ØÒ*C 7N à$Ø&Ôà"äzÔ"Ü ØAÄ$ÀzÓBRÐASÐTóð ô {Ô#Ü ØBÄ4È ÓCTÐBUÐVóð ð
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Ð Ø × 'Ñ '¨Õ 1ð r_ c ó y rm rd )rË r» rº s r] rÇ z._MergeOperation._maybe_require_matching_dtypes/ s ð r_ c ó y rm rd )rË r» s r] rÈ z#_MergeOperation._validate_tolerance5 s à r_ c ó| | j dd }| j dd }t | j j | j j | j « \ }}|Tt |t |« « s?|j j ||ddddd¬« } |j | | j ¬« }||_ |Tt |t |« « s?|j j ||ddddd¬« }
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j ¬« }||_ ddl m } ||_ ||_ | ||gd|¬ « } | S )
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z?
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reindex along index and concat along columns.
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join_indexÚ left_indexerÚ right_indexerrQ rX rY ÚllabelsÚrlabelsÚlmgrÚrmgrru r s r] Ú_reindex_and_concatz#_MergeOperation._reindex_and_concat9 sE ð yy|Ø
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1 ä5Ø II× Ñ $§*¡*×"7Ñ"7¸¿ ¹ ó
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Ñð Ð #Ô,<¸\Ì3ÈtË9Ô,Uð 99×,Ñ,ØØ ØØØ Ø Ø !ð -ó Dð ×-Ñ-¨d¸¿¹Ð-ÓCDØ
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à Ð $Ô-=Ø 3u:ô.
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ð ::×-Ñ-ØØ ØØØ Ø Ø !ð .ó Dð ×/Ñ/°¸4¿9¹9Ð/ÓEEØ å!à Ø Ùu ¨A°DÔ9Ø r_ c ó´ | j r3| j | j | j « \ | _ | _ | j « \ }}}| j ||||¬« }|j | | j ¬« }| j r| j |« }| j |||« | j |« |j | d¬« S )NrS )Úmethodr^ ) rO Ú_indicator_pre_mergerX rY Ú_get_join_inforæ Ú __finalize__Ú _merge_typeÚ_indicator_post_mergeÚ_maybe_add_join_keysÚ_maybe_restore_index_levels)rË rQ rß rà rá r s r] rW z_MergeOperation.get_resultr sÉ Ø >>Ø$(×$=Ñ$=¸d¿i¹iÈÏÉÓ$TÑ !DItzà26×2EÑ2EÓ2GÑ/
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L -à×)Ñ)Ø m¸$ð *ó
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ð ×$Ñ$ T°$×2BÑ2BÐ$ÓCà >>Ø×/Ñ/°Ó7Fà ×!Ñ! &¨,¸ ÔFà ×(Ñ(¨Ô0à×"Ñ" 4°Ð"Ó8Ð8r_ c ó¸ t | j t « r | j S t | j t « r| j rdS d S t d« )NÚ_mergez<indicator option can only accept boolean or string arguments)rw rO r¤ rµ r£ ©rË s r] Ú_indicator_namez _MergeOperation._indicator_name sK ô dnn¤cÔ *Ø>>Ð !Ü ¬Ô -Ø #~~8Ð 7°4Ð 7ä ØNóð r_ c óh |j j |j « }dD ] }||v st d| « | j |v r t d« |j « }|j « }d|d< |d j d« |d< d|d< |d j d« |d< ||fS ) N)Ú_left_indicatorÚ_right_indicatorzECannot use `indicator=True` option when data contains a column named z:Cannot use name of an existing column for indicator columnrb rõ Úint8é rö )ro Úunionr£ ró rQ Úastype)rË rX rY ro Úis r] ré z$_MergeOperation._indicator_pre_merge sÞ ð ,,×$Ñ$ U§]¡]Ó3à8ò AØG|Ü ð4Ø45°3ð8óð ð ð × Ñ 7Ñ *Ü ØLóð ð yy{Ø
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à"#Ð Ñ Ø"&Ð'8Ñ"9×"@Ñ"@ÀÓ"HÐ Ñ à$%Ð Ñ!Ø$)Ð*<Ñ$=×$DÑ$DÀVÓ$LÐ Ñ!àU{Ðr_ c óD |d j d« |d< |d j d« |d< t |d |d z g d¢¬« || j < || j j j g d¢« || j < |j ddgd¬« }|S ) Nrõ r rö )rb rø é )Ú
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categories)Ú left_onlyÚ
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right_onlyÚbothrb )Úlabelsrs )Úfillnar3 ró ÚcatÚrename_categoriesÚdrop)rË r s r] rí z%_MergeOperation._indicator_post_merge® s» à$*Ð+<Ñ$=×$DÑ$DÀQÓ$GÐ Ñ!Ø%+Ð,>Ñ%?×%FÑ%FÀqÓ%IÐ!Ñ"ä'2Ø Ð%Ñ &¨Ð0BÑ)CÑ CÚ ô(
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t×#Ñ#Ñ$ð (.Ø × Ñ ñ(
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ç
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#× Ñ Ò CÓDð t×#Ñ#Ñ$ð Ð%6Ð8JÐ$KÐRSÓTØ r_ c ól g }t | j | j | j « D ]n \ }}}| j j |« s#| j j |« s?||k( sE||j j vs^|j |« p |r|j |d¬« yy)a
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Restore index levels specified as `on` parameters
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Here we check for cases where `self.left_on` and `self.right_on` pairs
|
each reference an index level in their respective DataFrames. The
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joined columns corresponding to these pairs are then restored to the
|
index of `result`.
|
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**Note:** This method has side effects. It modifies `result` in-place
|
|
Parameters
|
----------
|
result: DataFrame
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merge result
|
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Returns
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-------
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None
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T©ÚinplaceN) Úzipr¸ rI rJ r¾ Ú_is_level_referencer¿ r Únamesr Ú set_index)rË r Únames_to_restoreÚnameÚleft_keyÚ right_keys r] rï z+_MergeOperation._maybe_restore_index_levels¾ s« ð* ÐÜ),Ø OOT\\¨4¯=©=ó*
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ò .Ñ %D(Ið ×2Ñ2°8Õ<ð OO×7Ñ7Ø õð Ó)Ø § ¡ × 2Ñ 2Ò2à ×'Ñ'¨Õ-ð# .ñ& Ø × Ñ Ð -°tÐ Õ <ð r_ c ó d }d }t d | j D « « sJ t | j | j | j
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k( r |j }|j- |« r#|j/ |||j0 ¬ « ||< *|j3 |« rt5 |j0 t6 « r_||_ |j0 j: D cg c]$ }||k7 r|j0 j= |« n|& }}|j? |d¬ « ´||_ ||_ Ä|jA ||xs d | |« à y c c}w )Nc 3 ó< K | ] }t |t « y wrm )rw Ú_known©rp r s r] rr z7_MergeOperation._maybe_add_join_keys.<locals>.<genexpr>ô s è ø ÒF¨Q:a¤×(ÑFùs )NNFéÿÿÿÿT©Ú extract_numpy©Ú
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fill_value©ÚdtyperQ ÚMÚO)r r r Úkey_)!rz r» r
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r¸ rI rJ Ú enumerateÚ _should_fillrX Úanyrº r Ú_valuesrY r< r1 ÚalgosÚtake_ndr4 Úwherer ÚkindÚ_is_label_referenceÚ_constructor_slicedr r rw r5 r r Úget_level_valuesr Úinsert)rË r rà rá Úleft_has_missingÚright_has_missingÚkeysrû r ÚlnameÚrnameÚ take_leftÚ
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take_rightÚlvalsÚlfillÚrvalsÚtakerÚrfillÚkey_colÚ result_dtypeÚ mask_leftÚ
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level_nameÚidx_lists r] rî z$_MergeOperation._maybe_add_join_keysê s) ð ÐØ ÐäÑF°$×2EÑ2EÔFÔFÐFÐFä4?? D§L¡L°$·-±-Ó@Ü'0°£ó g BÑ #AÑ#eUÜ uÔ-Øà$.Ñ !Izàv~Ø Ð+¨}Ñ/HØtyyÑ(Ø+Ð3ð $0Ð#7ñ !&à&2°bÑ&8×%=Ñ%=Ó%?ð -ñ ,Ø)-×)=Ñ)=¸aÑ)@Jà % d|× 1Ñ 1°T·Y±Y¸t±_×5JÑ5JÒ JØ,0¯I©I°d©O×,CÑ,C¡ à §¡Ñ+Ø,Ð4ð $1Ð#8ñ !&à&3°rÑ&9×%>Ñ%>Ó%@ð .ñ -Ø(,×(;Ñ(;¸AÑ(>Ià % d|× 1Ñ 1°T·Z±ZÀÑ5E×5KÑ5KÒ KØ-1¯Z©Z¸Ñ-=×-EÑ-E¡
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r_ c óR | j j }| j j }| j rJ| j r>| j
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dk( r| j |||d¬« }nvt | j « dkD r| j |||d¬« }nH||j « }n5|j |« }n#| t |« n
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t |« }t! |« }|||fS ) Nr¬ T©rR Úreturn_indexersrM rX rk rY r ©rR )rX r rY rK rL rR r rM Ú_left_join_on_indexr» rº r@ r
Ú_create_join_indexrQ r~ r> )rË Úleft_axÚright_axrß rà rá Úns r] rê z _MergeOperation._get_join_infoj sü ð ))//Ø::×#Ñ#à ??t× /Ò /°D·H±HÀÒ4FØ6=·l±lØdhh¸À4Ç9Á9ð 7Có 7Ñ 3J ¢mð × Ò $§(¡(¨fÒ"4Ü6IØ 4×#6Ñ#6¸T¿Y¹Yô7Ñ 3J ¢mð __ §¡¨WÒ!4Ü6IØ' 4×#7Ñ#7¸d¿i¹iô7Ñ 3J ¢|ð -1×,CÑ,CÓ,EÑ )\=à× Ò Ütyy> AÒ%Ø!%×!8Ñ!8Ø Ø Ø$Ø #ð "9ó "Jð #Ð*Ø!)§ ¡ £ Jà!)§ ¡ ¨}Ó!=JØ Ø88vÒ%à!%×!8Ñ!8Ø Ø Ø$Ø "ð "9ó "Jô _ qÒ(Ø!%×!8Ñ!8Ø Ø Ø%Ø "ð "9ó "Jð "Ð)Ø!(§ ¡ £ Jà!(§ ¡ ¨lÓ!;Jà$0Ð$8CL¼cÀ,Ó>OÜ *¨1Ó -
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à<¨ Ð6Ð6r_ c ó | j |dfv r\t |t « sL|dk( }t j |« r2t |j d¬« }|j t |g« « }||j « S |j |« S )aÝ
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Create a join index by rearranging one index to match another
|
|
Parameters
|
----------
|
index : Index
|
index being rearranged
|
other_index : Index
|
used to supply values not found in index
|
indexer : np.ndarray[np.intp] or None
|
how to rearrange index
|
how : str
|
Replacement is only necessary if indexer based on other_index.
|
|
Returns
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-------
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Index
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r½ r F)Úcompat) rR rw r5 Únpr" r1 r r r4 rQ r~ )rË r Ú other_indexÚindexerrR Úmaskr s r] rF z"_MergeOperation._create_join_index© sx ð4 88W~Ñ %¬j¸ÄjÔ.Qð b=DÜvvd|Ü /°· ± ÀEÔ J
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t t | « } | 2|j j | « « |j | « É|j j j « |j j j « || « sqt t | « } | !|j j | « « n%|j j j « |8|| k( r3|j | « n!t t | « } |j | « |Ct t |« }|j j |« « |j |« ô|j j j « |j j j « @ nÕt | j « rV| j D ] }
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Returns
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left_keys, right_keys, join_names, left_drop, right_drop
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c óV t | t « xr t | « t « k( S rm ©rw r r
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« 5 |j) |j « } d d d « t/ j2 |« } | k( } | | j5 « s$t7 j8 d t: t= « ¬ « :t? j@ |d¬ « t? j@ |d¬ « k( r³l|rtC |j « stC |j « r|rn|rt |j « st |j « r_|r]t? j@ |d¬ « }t? j@ |d¬ « }g d¢}g d¢}||v r||v rn||v r||vs
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||v r||vrtE |« tG |j « r tG |j « s tE |« tG |j « s tG |j « r tE |« t |j tH « r%t |j tH « s tE |« t |j tH « s%t |j tH « r tE |« t |j tH « rt |j tH « s2|j j dk( r|j j dk( rw|j j dk( r$|j j dk( r tE |« |j j dk( r$|j j dk( r tE |« t |j « rt |j « r || jJ jL v rv|r$t t |« jN j ntP }| jJ jS « | _% | jJ | j) |« | jJ |< || jT jL v sÅ|r$t t |« jN j ntP }| jT jS « | _* | jT | j) |« | jT |< = y # 1 sw Y xY w# 1 sw Y pxY w)NTr zYou are trying to merge on ú and z columns for key 'z2'. If you wish to proceed you should use pd.concatFr Úignore)ÚinvalidzjYou are merging on int and float columns where the float values are not equal to their int representation.)Ú
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4Ó ñku @÷B5ñ 5ú÷(5ñ 5ús Ì6 a+Ï. a8á+a5 á8b c
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ó² t j |« }t j |« }| j | || j r| j rd\ }}nV| j r t d« | j r t d« | j j }| j j }|j |« }t |« dk( r+t d| d| d| j d| j « |j |d ¬
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« j r |j |d ¬
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« j st d t |« « |x}}n[| j B|| t d « | j s | j r t d « | j x}}n |
| j r t d« | j s | t d« t |« }| j rÄt |« | j j j k7 r t! d« d g|z }n|| j r t d« | j s | t d« t |« }| j r=t |« | j j j k7 r t! d« d g|z }t |« t |« k7 r t! d« ||fS )N)rd rd z&Must pass right_on or right_index=Truez$Must pass left_on or left_index=Truer z>No common columns to perform merge on. Merge options: left_on=z , right_on=z , left_index=z, right_index=rc rD zData columns not unique: zSCan only pass argument "on" OR "left_on" and "right_on", not a combination of both.zYCan only pass argument "on" OR "left_index" and "right_index", not a combination of both.z:Can only pass argument "left_on" OR "left_index" not both.z&Must pass "right_on" OR "right_index".zDlen(left_on) must equal the number of levels in the index of "right"z<Can only pass argument "right_on" OR "right_index" not both.z$Must pass "left_on" OR "left_index".zDlen(right_on) must equal the number of levels in the index of "left"z%len(right_on) must equal len(left_on))rÀ rÁ rH rK rL r rX ro rY Ú intersectionr
r Ú is_uniqueÚreprr rà r£ )rË rI rJ Ú left_colsÚ
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right_colsÚ common_colsrI s r] rÄ z'_MergeOperation._validate_left_right_on sÕ Ü×%Ñ% gÓ.Ü×&Ñ& xÓ0ð 77?w°8Ñ3CØ 4×#3Ò#3Ø$*Ñ!Ø Ü Ð!IÓJÐJØ×!Ò!Ü Ð!GÓHÐHð !II× -Ñ - Ø !ZZ× /Ñ /
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Ø '× 4Ñ 4°ZÓ @ Ü{Ó# qÒ(Ü$ð2Ø29°ð ;$Ø$, :ð .&Ø&*§o¡oÐ%6ð 7'Ø'+×'7Ñ'7Ð&8ð :óð ð " {¸Ó@×JÒJØ%??¨;¸G?ÓD×NÒNä$Ð'@ÄÀkÓARÐ@SÐ%TÓUÐUØ%0Ð0(Ø WWÐ ØÐ" hÐ&:Ü ðAóð ð $×"2Ò"2Ü ðDóð ð "&§¡Ð (GhØ Ð ØÜ ØPóð ð ×#Ò#¨Ð(8Ü Ð!IÓJÐJÜG AØ× Ò Üw< 4§:¡:×#3Ñ#3×#;Ñ#;Ò;Ü$ð<óð ð !6 A:Ø Ð !Ø× Ò Ü ØRóð ð ?? w Ü Ð!GÓHÐHÜH AØÜx= D§I¡I§O¡O×$;Ñ$;Ò;Ü$ð;óð ð & 1*Ü x=C LÒ (Ü Ð DÓEÐ EàÐ Ð r_ c ó& | j r!| j j j }n)t j
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one_to_onez1:1zQMerge keys are not unique in either left or right dataset; not a one-to-one mergezAMerge keys are not unique in left dataset; not a one-to-one mergezBMerge keys are not unique in right dataset; not a one-to-one merge)Ú one_to_manyz1:mzBMerge keys are not unique in left dataset; not a one-to-many merge)Ú many_to_onezm:1zCMerge keys are not unique in right dataset; not a many-to-one merge)Ú many_to_manyzm:mú"z" is not a valid argument. Valid arguments are:
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- "1:1"
|
- "1:m"
|
- "m:1"
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- "m:m"
|
- "one_to_one"
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- "one_to_many"
|
- "many_to_one"
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- "many_to_many") rK r¾ r r r5 Ú from_arraysr» rL r¿ rº r r£ )rË rP Ú left_uniqueÚ right_uniques r] rÊ z&_MergeOperation._validate_validate_kwda s= ð ??Ø ..×.Ñ.×8Ñ8Kä$×0Ñ0°×1DÑ1DÓE×OÑOKà × Ò Ø ??×0Ñ0×:Ñ:Lä%×1Ñ1°$×2FÑ2FÓG×QÑQLð Ð,Ñ ,Ù ¡|Ü ð?óð ñ Ü ØWóð ñ Ü ØXóð ð ð
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Ð/Ñ /Ù Ü ØXóð ð ð
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Ð/Ñ /Ù Ü ð.óð ð ð Ð0Ñ 0Ø ô ØH:ð #ð #ó ð r_ )
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rc NNNFFT©Ú_xÚ_yFN)rX úDataFrame | SeriesrY r rR r³ rH ú IndexLabel | AnyArrayLike | NonerI r rJ r rK rµ rL rµ rM rµ rN r rO r¶ rP r· r¢ ÚNone©r» r¹ rº r¹ r¢ r ©r» r¹ r¢ r )
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rß r4 rà únpt.NDArray[np.intp] | Nonerá r rQ ú bool | Noner¢ rA ©T©rQ r r¢ rA )r¢ r· )rX rA rY rA r¢ ztuple[DataFrame, DataFrame])r rA r¢ rA )r rA r¢ r )r rA rà r rá r r¢ r )r¢ ú?tuple[npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None])r¢ zFtuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None])rX )
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r r4 rM r4 rN r rR r r¢ r4 )r¢ zWtuple[list[ArrayLike], list[ArrayLike], list[Hashable], list[Hashable], list[Hashable]])r¢ r )rP r¤ r¢ r )Ú__name__Ú
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__module__Ú __qualname__Ú__doc__rì Ú__annotations__rÑ rÇ rÈ r
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|
Parameters
|
----------
|
left_keys : list[ndarray, ExtensionArray, Index, Series]
|
right_keys : list[ndarray, ExtensionArray, Index, Series]
|
sort : bool, default False
|
how : {'inner', 'outer', 'left', 'right'}, default 'inner'
|
|
Returns
|
-------
|
np.ndarray[np.intp] or None
|
Indexer into the left_keys.
|
np.ndarray[np.intp] or None
|
Indexer into the right_keys.
|
z0left_keys and right_keys must be the same lengthr )rX rc )rY r½ T)rY rc )rX r½ Frb c 3 óF K | ] }t | | ¬ « yw©rk N)Ú_factorize_keys)rp rI rX rY rM s r] rr z$get_join_indexers.<locals>.<genexpr>É s- øè ø ò
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Ú_get_empty_indexerÚ _get_no_sort_one_missing_indexerr r
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Ú_get_join_keysry r4 Úis_monotonic_increasingr r Ú get_join_indexers_non_uniquer# r )rX rY rM rR Úleft_nÚright_nÚmappedÚzippedÚllabÚrlabÚshapeÚlkeyÚrkeyrX rY r© ÚlidxÚridxs ``` r] r? r? sÇ ú ô, y>SØó ò ð :à9ó:ð ô
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dð ÐÔ ,¨T´3°t³9Ô =ØØ ÐÔ ,¨T´3°u³:Ô >ØØ :Ðr_ c óJ t | ||¬« \ }}}|dk( r t j ||||¬« \ }}||fS |dk( r t j ||||¬« \ }}||fS |dk( r t j ||||¬« \ }}||fS |dk( rt j |||« \ }}fS )aW
|
Get join indexers for left and right.
|
|
Parameters
|
----------
|
left : ArrayLike
|
right : ArrayLike
|
sort : bool, default False
|
how : {'inner', 'outer', 'left', 'right'}, default 'inner'
|
|
Returns
|
-------
|
np.ndarray[np.intp]
|
Indexer into left.
|
np.ndarray[np.intp]
|
Indexer into right.
|
rk rX rY rc r½ )r« ÚlibjoinÚleft_outer_joinÚ
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inner_joinÚfull_outer_join) rX rY rM rR r¼ r½ Úcountr¾ r¿ s r] r´ r´ ë sÏ ô. (¨¨e¸$Ô?ÑD$Ø
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*this is an internal non-public method*
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|
Returns the levels, labels and names of a multi-index to multi-index join.
|
Depending on the type of join, this method restores the appropriate
|
dropped levels of the joined multi-index.
|
The method relies on lindexer, rindexer which hold the index positions of
|
left and right, where a join was feasible
|
|
Parameters
|
----------
|
left : MultiIndex
|
left index
|
right : MultiIndex
|
right index
|
dropped_level_names : str array
|
list of non-common level names
|
join_index : Index
|
the index of the join between the
|
common levels of left and right
|
lindexer : np.ndarray[np.intp]
|
left indexer
|
rindexer : np.ndarray[np.intp]
|
right indexer
|
|
Returns
|
-------
|
levels : list of Index
|
levels of combined multiindexes
|
labels : np.ndarray[np.intp]
|
labels of combined multiindexes
|
names : List[Hashable]
|
names of combined multiindex levels
|
|
c ó t | t « r| S t j | j g| j g¬« S )N)r )rw r5 r r# r )r s r] Ú_convert_to_multiindexz@restore_dropped_levels_multijoin.<locals>._convert_to_multiindex: s0 Ü eZÔ (ØLä ×)Ñ)¨5¯=©=¨/À%Ç*Á*À ÔNÐ Nr_ r r )r r4 r¢ r5 )rV rW r r r$ r% )rX rY Údropped_level_namesrß ÚlindexerÚrindexerrÈ Ú join_levelsÚ
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t+ |« d t+ |« d « | j ´t- | j « s| j g| _ t- | j « s| j g| _ t | j « t | j « k7 r t d « | j t/ |« z }| j t/ |« z }||fS )Nrb z can only asof on a key for leftz can only asof on a key for rightz left can only have one indexz right can only have one indexz(Can only pass by OR left_by and right_byzmissing left_byzmissing right_byr zIncompatible merge dtype, rb z$, both sides must have numeric dtypez,left_by and right_by must be the same length)ÚsuperrÄ r
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}n|d } |||d« y )Nc ó | j |j k7 r®t | j t « rWt |j t « r=d| dt | j « dt |j « d}t |« d| dt | j « dt |j « d}t |« y )Nzincompatible merge keys [z] rb z), both sides category, but not equal onesz, must be the same type)r rw r, r r )rX rY rû rÎ s r] Ú_check_dtype_matchzE_AsOfMerge._maybe_require_matching_dtypes.<locals>._check_dtype_match3 sÀ ØzzU[[Ò(Ü djjÔ*:Ô;Ä
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Return join indexers where all of one side is selected without sorting
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and none of the other side is selected.
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Parameters
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----------
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n : int
|
Length of indexers to create.
|
left_missing : bool
|
If True, the left indexer will contain only -1's.
|
If False, the right indexer will contain only -1's.
|
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Returns
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-------
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np.ndarray[np.intp]
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Left indexer
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np.ndarray[np.intp]
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Right indexer
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Encode left and right keys as enumerated types.
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|
This is used to get the join indexers to be used when merging DataFrames.
|
|
Parameters
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----------
|
lk : ndarray, ExtensionArray
|
Left key.
|
rk : ndarray, ExtensionArray
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Right key.
|
sort : bool, defaults to True
|
If True, the encoding is done such that the unique elements in the
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keys are sorted.
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|
Returns
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-------
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np.ndarray[np.intp]
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Left (resp. right if called with `key='right'`) labels, as enumerated type.
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np.ndarray[np.intp]
|
Right (resp. left if called with `key='right'`) labels, as enumerated type.
|
int
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Number of unique elements in union of left and right labels.
|
|
See Also
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--------
|
merge : Merge DataFrame or named Series objects
|
with a database-style join.
|
algorithms.factorize : Encode the object as an enumerated type
|
or categorical variable.
|
|
Examples
|
--------
|
>>> lk = np.array(["a", "c", "b"])
|
>>> rk = np.array(["a", "c"])
|
|
Here, the unique values are `'a', 'b', 'c'`. With the default
|
`sort=True`, the encoding will be `{0: 'a', 1: 'b', 2: 'c'}`:
|
|
>>> pd.core.reshape.merge._factorize_keys(lk, rk)
|
(array([0, 2, 1]), array([0, 2]), 3)
|
|
With the `sort=False`, the encoding will correspond to the order
|
in which the unique elements first appear: `{0: 'a', 1: 'c', 2: 'b'}`:
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(array([0, 1, 2]), array([0, 1]), 3)
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Rename the left and right indices.
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|
If there is overlap, and suffix is not None, add
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Parameters
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x : original column name
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suffix : str or None
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|
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-------
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x : renamed column name
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