.. currentmodule:: pandas
.. ipython:: python
:suppress:
import numpy as np
np.random.seed(123456)
import pandas as pd
pd.options.display.max_rows=15
randn = np.random.randn
np.set_printoptions(precision=4, suppress=True)
import matplotlib.pyplot as plt
plt.close('all')
import pandas.util.doctools as doctools
p = doctools.TablePlotter()
pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
The concat function (in the main pandas namespace) does all of the heavy
lifting of performing concatenation operations along an axis while performing
optional set logic (union or intersection) of the indexes (if any) on the other
axes. Note that I say "if any" because there is only a single possible axis of
concatenation for Series.
Before diving into all of the details of concat and what it can do, here is
a simple example:
.. ipython:: python
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
'B': ['B4', 'B5', 'B6', 'B7'],
'C': ['C4', 'C5', 'C6', 'C7'],
'D': ['D4', 'D5', 'D6', 'D7']},
index=[4, 5, 6, 7])
df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
'B': ['B8', 'B9', 'B10', 'B11'],
'C': ['C8', 'C9', 'C10', 'C11'],
'D': ['D8', 'D9', 'D10', 'D11']},
index=[8, 9, 10, 11])
frames = [df1, df2, df3]
result = pd.concat(frames)
.. ipython:: python
:suppress:
@savefig merging_concat_basic.png
p.plot(frames, result,
labels=['df1', 'df2', 'df3'], vertical=True);
plt.close('all');
Like its sibling function on ndarrays, numpy.concatenate, pandas.concat
takes a list or dict of homogeneously-typed objects and concatenates them with
some configurable handling of "what to do with the other axes":
pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
keys=None, levels=None, names=None, verify_integrity=False,
copy=True)
objs: a sequence or mapping of Series, DataFrame, or Panel objects. If a dict is passed, the sorted keys will be used as the keys argument, unless it is passed, in which case the values will be selected (see below). Any None objects will be dropped silently unless they are all None in which case a ValueError will be raised.axis: {0, 1, ...}, default 0. The axis to concatenate along.join: {'inner', 'outer'}, default 'outer'. How to handle indexes on other axis(es). Outer for union and inner for intersection.ignore_index: boolean, default False. If True, do not use the index values on the concatenation axis. The resulting axis will be labeled 0, ..., n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the index values on the other axes are still respected in the join.join_axes: list of Index objects. Specific indexes to use for the other n - 1 axes instead of performing inner/outer set logic.keys: sequence, default None. Construct hierarchical index using the passed keys as the outermost level. If multiple levels passed, should contain tuples.levels: list of sequences, default None. Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys.names: list, default None. Names for the levels in the resulting hierarchical index.verify_integrity: boolean, default False. Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation.copy: boolean, default True. If False, do not copy data unnecessarily.
Without a little bit of context and example many of these arguments don't make
much sense. Let's take the above example. Suppose we wanted to associate
specific keys with each of the pieces of the chopped up DataFrame. We can do
this using the keys argument:
.. ipython:: python result = pd.concat(frames, keys=['x', 'y', 'z'])
.. ipython:: python
:suppress:
@savefig merging_concat_keys.png
p.plot(frames, result,
labels=['df1', 'df2', 'df3'], vertical=True)
plt.close('all');
As you can see (if you've read the rest of the documentation), the resulting object's index has a :ref:`hierarchical index <advanced.hierarchical>`. This means that we can now do stuff like select out each chunk by key:
.. ipython:: python result.loc['y']
It's not a stretch to see how this can be very useful. More detail on this functionality below.
Note
It is worth noting however, that concat (and therefore append) makes
a full copy of the data, and that constantly reusing this function can
create a significant performance hit. If you need to use the operation over
several datasets, use a list comprehension.
frames = [ process_your_file(f) for f in files ] result = pd.concat(frames)
When gluing together multiple DataFrames (or Panels or...), for example, you have a choice of how to handle the other axes (other than the one being concatenated). This can be done in three ways:
- Take the (sorted) union of them all,
join='outer'. This is the default option as it results in zero information loss. - Take the intersection,
join='inner'. - Use a specific index (in the case of DataFrame) or indexes (in the case of
Panel or future higher dimensional objects), i.e. the
join_axesargument
Here is a example of each of these methods. First, the default join='outer'
behavior:
.. ipython:: python
df4 = pd.DataFrame({'B': ['B2', 'B3', 'B6', 'B7'],
'D': ['D2', 'D3', 'D6', 'D7'],
'F': ['F2', 'F3', 'F6', 'F7']},
index=[2, 3, 6, 7])
result = pd.concat([df1, df4], axis=1)
.. ipython:: python
:suppress:
@savefig merging_concat_axis1.png
p.plot([df1, df4], result,
labels=['df1', 'df4'], vertical=False);
plt.close('all');
Note that the row indexes have been unioned and sorted. Here is the same thing
with join='inner':
.. ipython:: python result = pd.concat([df1, df4], axis=1, join='inner')
.. ipython:: python
:suppress:
@savefig merging_concat_axis1_inner.png
p.plot([df1, df4], result,
labels=['df1', 'df4'], vertical=False);
plt.close('all');
Lastly, suppose we just wanted to reuse the exact index from the original DataFrame:
.. ipython:: python result = pd.concat([df1, df4], axis=1, join_axes=[df1.index])
.. ipython:: python
:suppress:
@savefig merging_concat_axis1_join_axes.png
p.plot([df1, df4], result,
labels=['df1', 'df4'], vertical=False);
plt.close('all');
A useful shortcut to concat are the append instance methods on Series
and DataFrame. These methods actually predated concat. They concatenate
along axis=0, namely the index:
.. ipython:: python result = df1.append(df2)
.. ipython:: python
:suppress:
@savefig merging_append1.png
p.plot([df1, df2], result,
labels=['df1', 'df2'], vertical=True);
plt.close('all');
In the case of DataFrame, the indexes must be disjoint but the columns do not need to be:
.. ipython:: python result = df1.append(df4)
.. ipython:: python
:suppress:
@savefig merging_append2.png
p.plot([df1, df4], result,
labels=['df1', 'df4'], vertical=True);
plt.close('all');
append may take multiple objects to concatenate:
.. ipython:: python result = df1.append([df2, df3])
.. ipython:: python
:suppress:
@savefig merging_append3.png
p.plot([df1, df2, df3], result,
labels=['df1', 'df2', 'df3'], vertical=True);
plt.close('all');
Note
Unlike list.append method, which appends to the original list and
returns nothing, append here does not modify df1 and
returns its copy with df2 appended.
For DataFrames which don't have a meaningful index, you may wish to append them and ignore the fact that they may have overlapping indexes:
To do this, use the ignore_index argument:
.. ipython:: python result = pd.concat([df1, df4], ignore_index=True)
.. ipython:: python
:suppress:
@savefig merging_concat_ignore_index.png
p.plot([df1, df4], result,
labels=['df1', 'df4'], vertical=True);
plt.close('all');
This is also a valid argument to DataFrame.append:
.. ipython:: python result = df1.append(df4, ignore_index=True)
.. ipython:: python
:suppress:
@savefig merging_append_ignore_index.png
p.plot([df1, df4], result,
labels=['df1', 'df4'], vertical=True);
plt.close('all');
You can concatenate a mix of Series and DataFrames. The Series will be transformed to DataFrames with the column name as the name of the Series.
.. ipython:: python s1 = pd.Series(['X0', 'X1', 'X2', 'X3'], name='X') result = pd.concat([df1, s1], axis=1)
.. ipython:: python
:suppress:
@savefig merging_concat_mixed_ndim.png
p.plot([df1, s1], result,
labels=['df1', 's1'], vertical=False);
plt.close('all');
If unnamed Series are passed they will be numbered consecutively.
.. ipython:: python s2 = pd.Series(['_0', '_1', '_2', '_3']) result = pd.concat([df1, s2, s2, s2], axis=1)
.. ipython:: python
:suppress:
@savefig merging_concat_unnamed_series.png
p.plot([df1, s2], result,
labels=['df1', 's2'], vertical=False);
plt.close('all');
Passing ignore_index=True will drop all name references.
.. ipython:: python result = pd.concat([df1, s1], axis=1, ignore_index=True)
.. ipython:: python
:suppress:
@savefig merging_concat_series_ignore_index.png
p.plot([df1, s1], result,
labels=['df1', 's1'], vertical=False);
plt.close('all');
A fairly common use of the keys argument is to override the column names when creating a new DataFrame based on existing Series.
Notice how the default behaviour consists on letting the resulting DataFrame inherits the parent Series' name, when these existed.
.. ipython:: python s3 = pd.Series([0, 1, 2, 3], name='foo') s4 = pd.Series([0, 1, 2, 3]) s5 = pd.Series([0, 1, 4, 5]) pd.concat([s3, s4, s5], axis=1)
Through the keys argument we can override the existing column names.
.. ipython:: python pd.concat([s3, s4, s5], axis=1, keys=['red','blue','yellow'])
Let's consider now a variation on the very first example presented:
.. ipython:: python result = pd.concat(frames, keys=['x', 'y', 'z'])
.. ipython:: python
:suppress:
@savefig merging_concat_group_keys2.png
p.plot(frames, result,
labels=['df1', 'df2', 'df3'], vertical=True);
plt.close('all');
You can also pass a dict to concat in which case the dict keys will be used
for the keys argument (unless other keys are specified):
.. ipython:: python
pieces = {'x': df1, 'y': df2, 'z': df3}
result = pd.concat(pieces)
.. ipython:: python
:suppress:
@savefig merging_concat_dict.png
p.plot([df1, df2, df3], result,
labels=['df1', 'df2', 'df3'], vertical=True);
plt.close('all');
.. ipython:: python result = pd.concat(pieces, keys=['z', 'y'])
.. ipython:: python
:suppress:
@savefig merging_concat_dict_keys.png
p.plot([df1, df2, df3], result,
labels=['df1', 'df2', 'df3'], vertical=True);
plt.close('all');
The MultiIndex created has levels that are constructed from the passed keys and the index of the DataFrame pieces:
.. ipython:: python result.index.levels
If you wish to specify other levels (as will occasionally be the case), you can
do so using the levels argument:
.. ipython:: python
result = pd.concat(pieces, keys=['x', 'y', 'z'],
levels=[['z', 'y', 'x', 'w']],
names=['group_key'])
.. ipython:: python
:suppress:
@savefig merging_concat_dict_keys_names.png
p.plot([df1, df2, df3], result,
labels=['df1', 'df2', 'df3'], vertical=True);
plt.close('all');
.. ipython:: python result.index.levels
Yes, this is fairly esoteric, but is actually necessary for implementing things like GroupBy where the order of a categorical variable is meaningful.
While not especially efficient (since a new object must be created), you can
append a single row to a DataFrame by passing a Series or dict to append,
which returns a new DataFrame as above.
.. ipython:: python s2 = pd.Series(['X0', 'X1', 'X2', 'X3'], index=['A', 'B', 'C', 'D']) result = df1.append(s2, ignore_index=True)
.. ipython:: python
:suppress:
@savefig merging_append_series_as_row.png
p.plot([df1, s2], result,
labels=['df1', 's2'], vertical=True);
plt.close('all');
You should use ignore_index with this method to instruct DataFrame to
discard its index. If you wish to preserve the index, you should construct an
appropriately-indexed DataFrame and append or concatenate those objects.
You can also pass a list of dicts or Series:
.. ipython:: python
dicts = [{'A': 1, 'B': 2, 'C': 3, 'X': 4},
{'A': 5, 'B': 6, 'C': 7, 'Y': 8}]
result = df1.append(dicts, ignore_index=True)
.. ipython:: python
:suppress:
@savefig merging_append_dits.png
p.plot([df1, pd.DataFrame(dicts)], result,
labels=['df1', 'dicts'], vertical=True);
plt.close('all');
pandas has full-featured, high performance in-memory join operations
idiomatically very similar to relational databases like SQL. These methods
perform significantly better (in some cases well over an order of magnitude
better) than other open source implementations (like base::merge.data.frame
in R). The reason for this is careful algorithmic design and internal layout of
the data in DataFrame.
See the :ref:`cookbook<cookbook.merge>` for some advanced strategies.
Users who are familiar with SQL but new to pandas might be interested in a :ref:`comparison with SQL<compare_with_sql.join>`.
pandas provides a single function, merge, as the entry point for all
standard database join operations between DataFrame objects:
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=True,
suffixes=('_x', '_y'), copy=True, indicator=False)
left: A DataFrame objectright: Another DataFrame objecton: Columns (names) to join on. Must be found in both the left and right DataFrame objects. If not passed andleft_indexandright_indexareFalse, the intersection of the columns in the DataFrames will be inferred to be the join keysleft_on: Columns from the left DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrameright_on: Columns from the right DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrameleft_index: IfTrue, use the index (row labels) from the left DataFrame as its join key(s). In the case of a DataFrame with a MultiIndex (hierarchical), the number of levels must match the number of join keys from the right DataFrameright_index: Same usage asleft_indexfor the right DataFramehow: One of'left','right','outer','inner'. Defaults toinner. See below for more detailed description of each methodsort: Sort the result DataFrame by the join keys in lexicographical order. Defaults toTrue, setting toFalsewill improve performance substantially in many casessuffixes: A tuple of string suffixes to apply to overlapping columns. Defaults to('_x', '_y').copy: Always copy data (defaultTrue) from the passed DataFrame objects, even when reindexing is not necessary. Cannot be avoided in many cases but may improve performance / memory usage. The cases where copying can be avoided are somewhat pathological but this option is provided nonetheless.indicator: Add a column to the output DataFrame called_mergewith information on the source of each row._mergeis Categorical-type and takes on a value ofleft_onlyfor observations whose merge key only appears in'left'DataFrame,right_onlyfor observations whose merge key only appears in'right'DataFrame, andbothif the observation's merge key is found in both... versionadded:: 0.17.0
The return type will be the same as left. If left is a DataFrame
and right is a subclass of DataFrame, the return type will still be
DataFrame.
merge is a function in the pandas namespace, and it is also available as a
DataFrame instance method, with the calling DataFrame being implicitly
considered the left object in the join.
The related DataFrame.join method, uses merge internally for the
index-on-index (by default) and column(s)-on-index join. If you are joining on
index only, you may wish to use DataFrame.join to save yourself some typing.
Experienced users of relational databases like SQL will be familiar with the terminology used to describe join operations between two SQL-table like structures (DataFrame objects). There are several cases to consider which are very important to understand:
- one-to-one joins: for example when joining two DataFrame objects on their indexes (which must contain unique values)
- many-to-one joins: for example when joining an index (unique) to one or more columns in a DataFrame
- many-to-many joins: joining columns on columns.
Note
When joining columns on columns (potentially a many-to-many join), any indexes on the passed DataFrame objects will be discarded.
It is worth spending some time understanding the result of the many-to-many join case. In SQL / standard relational algebra, if a key combination appears more than once in both tables, the resulting table will have the Cartesian product of the associated data. Here is a very basic example with one unique key combination:
.. ipython:: python
left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
result = pd.merge(left, right, on='key')
.. ipython:: python
:suppress:
@savefig merging_merge_on_key.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
Here is a more complicated example with multiple join keys:
.. ipython:: python
left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
'key2': ['K0', 'K0', 'K0', 'K0'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
result = pd.merge(left, right, on=['key1', 'key2'])
.. ipython:: python
:suppress:
@savefig merging_merge_on_key_multiple.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
The how argument to merge specifies how to determine which keys are to
be included in the resulting table. If a key combination does not appear in
either the left or right tables, the values in the joined table will be
NA. Here is a summary of the how options and their SQL equivalent names:
| Merge method | SQL Join Name | Description |
|---|---|---|
left |
LEFT OUTER JOIN |
Use keys from left frame only |
right |
RIGHT OUTER JOIN |
Use keys from right frame only |
outer |
FULL OUTER JOIN |
Use union of keys from both frames |
inner |
INNER JOIN |
Use intersection of keys from both frames |
.. ipython:: python result = pd.merge(left, right, how='left', on=['key1', 'key2'])
.. ipython:: python
:suppress:
@savefig merging_merge_on_key_left.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
.. ipython:: python result = pd.merge(left, right, how='right', on=['key1', 'key2'])
.. ipython:: python
:suppress:
@savefig merging_merge_on_key_right.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
.. ipython:: python result = pd.merge(left, right, how='outer', on=['key1', 'key2'])
.. ipython:: python
:suppress:
@savefig merging_merge_on_key_outer.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
.. ipython:: python result = pd.merge(left, right, how='inner', on=['key1', 'key2'])
.. ipython:: python
:suppress:
@savefig merging_merge_on_key_inner.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
Here is another example with duplicate join keys in DataFrames:
.. ipython:: python
left = pd.DataFrame({'A' : [1,2], 'B' : [2, 2]})
right = pd.DataFrame({'A' : [4,5,6], 'B': [2,2,2]})
result = pd.merge(left, right, on='B', how='outer')
.. ipython:: python
:suppress:
@savefig merging_merge_on_key_dup.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
Warning
Joining / merging on duplicate keys can cause a returned frame that is the multiplication of the row dimensions, may result in memory overflow. It is the user' s responsibility to manage duplicate values in keys before joining large DataFrames.
.. versionadded:: 0.17.0
merge now accepts the argument indicator. If True, a Categorical-type column called _merge will be added to the output object that takes on values:
Observation Origin _mergevalueMerge key only in 'left'frameleft_onlyMerge key only in 'right'frameright_onlyMerge key in both frames both
.. ipython:: python
df1 = pd.DataFrame({'col1': [0, 1], 'col_left':['a', 'b']})
df2 = pd.DataFrame({'col1': [1, 2, 2],'col_right':[2, 2, 2]})
pd.merge(df1, df2, on='col1', how='outer', indicator=True)
The indicator argument will also accept string arguments, in which case the indicator function will use the value of the passed string as the name for the indicator column.
.. ipython:: python pd.merge(df1, df2, on='col1', how='outer', indicator='indicator_column')
.. versionadded:: 0.19.0
Merging will preserve the dtype of the join keys.
.. ipython:: python
left = pd.DataFrame({'key': [1], 'v1': [10]})
left
right = pd.DataFrame({'key': [1, 2], 'v1': [20, 30]})
right
We are able to preserve the join keys
.. ipython:: python pd.merge(left, right, how='outer') pd.merge(left, right, how='outer').dtypes
Of course if you have missing values that are introduced, then the resulting dtype will be upcast.
.. ipython:: python pd.merge(left, right, how='outer', on='key') pd.merge(left, right, how='outer', on='key').dtypes
.. versionadded:: 0.20.0
Merging will preserve category dtypes of the mergands.
The left frame.
.. ipython:: python
X = pd.Series(np.random.choice(['foo', 'bar'], size=(10,)))
X = X.astype('category', categories=['foo', 'bar'])
left = DataFrame({'X': X,
'Y': np.random.choice(['one', 'two', 'three'], size=(10,))})
left
left.dtypes
The right frame.
.. ipython:: python
right = DataFrame({'X': Series(['foo', 'bar']).astype('category', categories=['foo', 'bar']),
'Z': [1, 2]})
right
right.dtypes
The merged result
.. ipython:: python result = pd.merge(left, right, how='outer') result result.dtypes
Note
The category dtypes must be exactly the same, meaning the same categories and the ordered attribute.
Otherwise the result will coerce to object dtype.
Note
Merging on category dtypes that are the same can be quite performant compared to object dtype merging.
DataFrame.join is a convenient method for combining the columns of two
potentially differently-indexed DataFrames into a single result DataFrame. Here
is a very basic example:
.. ipython:: python
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=['K0', 'K1', 'K2'])
right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
'D': ['D0', 'D2', 'D3']},
index=['K0', 'K2', 'K3'])
result = left.join(right)
.. ipython:: python
:suppress:
@savefig merging_join.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
.. ipython:: python result = left.join(right, how='outer')
.. ipython:: python
:suppress:
@savefig merging_join_outer.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
.. ipython:: python result = left.join(right, how='inner')
.. ipython:: python
:suppress:
@savefig merging_join_inner.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
The data alignment here is on the indexes (row labels). This same behavior can
be achieved using merge plus additional arguments instructing it to use the
indexes:
.. ipython:: python result = pd.merge(left, right, left_index=True, right_index=True, how='outer')
.. ipython:: python
:suppress:
@savefig merging_merge_index_outer.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
.. ipython:: python result = pd.merge(left, right, left_index=True, right_index=True, how='inner');
.. ipython:: python
:suppress:
@savefig merging_merge_index_inner.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
join takes an optional on argument which may be a column or multiple
column names, which specifies that the passed DataFrame is to be aligned on
that column in the DataFrame. These two function calls are completely
equivalent:
left.join(right, on=key_or_keys)
pd.merge(left, right, left_on=key_or_keys, right_index=True,
how='left', sort=False)
Obviously you can choose whichever form you find more convenient. For
many-to-one joins (where one of the DataFrame's is already indexed by the join
key), using join may be more convenient. Here is a simple example:
.. ipython:: python
left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'key': ['K0', 'K1', 'K0', 'K1']})
right = pd.DataFrame({'C': ['C0', 'C1'],
'D': ['D0', 'D1']},
index=['K0', 'K1'])
result = left.join(right, on='key')
.. ipython:: python
:suppress:
@savefig merging_join_key_columns.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
.. ipython:: python
result = pd.merge(left, right, left_on='key', right_index=True,
how='left', sort=False);
.. ipython:: python
:suppress:
@savefig merging_merge_key_columns.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
To join on multiple keys, the passed DataFrame must have a MultiIndex:
.. ipython:: python
left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'key1': ['K0', 'K0', 'K1', 'K2'],
'key2': ['K0', 'K1', 'K0', 'K1']})
index = pd.MultiIndex.from_tuples([('K0', 'K0'), ('K1', 'K0'),
('K2', 'K0'), ('K2', 'K1')])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index)
Now this can be joined by passing the two key column names:
.. ipython:: python result = left.join(right, on=['key1', 'key2'])
.. ipython:: python
:suppress:
@savefig merging_join_multikeys.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
The default for DataFrame.join is to perform a left join (essentially a
"VLOOKUP" operation, for Excel users), which uses only the keys found in the
calling DataFrame. Other join types, for example inner join, can be just as
easily performed:
.. ipython:: python result = left.join(right, on=['key1', 'key2'], how='inner')
.. ipython:: python
:suppress:
@savefig merging_join_multikeys_inner.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
As you can see, this drops any rows where there was no match.
.. versionadded:: 0.14.0
You can join a singly-indexed DataFrame with a level of a multi-indexed DataFrame.
The level will match on the name of the index of the singly-indexed frame against
a level name of the multi-indexed frame.
.. ipython:: python
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=pd.Index(['K0', 'K1', 'K2'], name='key'))
index = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),
('K2', 'Y2'), ('K2', 'Y3')],
names=['key', 'Y'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=index)
result = left.join(right, how='inner')
.. ipython:: python
:suppress:
@savefig merging_join_multiindex_inner.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
This is equivalent but less verbose and more memory efficient / faster than this.
.. ipython:: python
result = pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner').set_index(['key','Y'])
.. ipython:: python
:suppress:
@savefig merging_merge_multiindex_alternative.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
This is not Implemented via join at-the-moment, however it can be done using the following.
.. ipython:: python
index = pd.MultiIndex.from_tuples([('K0', 'X0'), ('K0', 'X1'),
('K1', 'X2')],
names=['key', 'X'])
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=index)
result = pd.merge(left.reset_index(), right.reset_index(),
on=['key'], how='inner').set_index(['key','X','Y'])
.. ipython:: python
:suppress:
@savefig merging_merge_two_multiindex.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
The merge suffixes argument takes a tuple of list of strings to append to
overlapping column names in the input DataFrames to disambiguate the result
columns:
.. ipython:: python
left = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'v': [1, 2, 3]})
right = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'v': [4, 5, 6]})
result = pd.merge(left, right, on='k')
.. ipython:: python
:suppress:
@savefig merging_merge_overlapped.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
.. ipython:: python result = pd.merge(left, right, on='k', suffixes=['_l', '_r'])
.. ipython:: python
:suppress:
@savefig merging_merge_overlapped_suffix.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
DataFrame.join has lsuffix and rsuffix arguments which behave
similarly.
.. ipython:: python
left = left.set_index('k')
right = right.set_index('k')
result = left.join(right, lsuffix='_l', rsuffix='_r')
.. ipython:: python
:suppress:
@savefig merging_merge_overlapped_multi_suffix.png
p.plot([left, right], result,
labels=['left', 'right'], vertical=False);
plt.close('all');
A list or tuple of DataFrames can also be passed to DataFrame.join to join
them together on their indexes. The same is true for Panel.join.
.. ipython:: python
right2 = pd.DataFrame({'v': [7, 8, 9]}, index=['K1', 'K1', 'K2'])
result = left.join([right, right2])
.. ipython:: python
:suppress:
@savefig merging_join_multi_df.png
p.plot([left, right, right2], result,
labels=['left', 'right', 'right2'], vertical=False);
plt.close('all');
Another fairly common situation is to have two like-indexed (or similarly indexed) Series or DataFrame objects and wanting to "patch" values in one object from values for matching indices in the other. Here is an example:
.. ipython:: python
df1 = pd.DataFrame([[np.nan, 3., 5.], [-4.6, np.nan, np.nan],
[np.nan, 7., np.nan]])
df2 = pd.DataFrame([[-42.6, np.nan, -8.2], [-5., 1.6, 4]],
index=[1, 2])
For this, use the combine_first method:
.. ipython:: python result = df1.combine_first(df2)
.. ipython:: python
:suppress:
@savefig merging_combine_first.png
p.plot([df1, df2], result,
labels=['df1', 'df2'], vertical=False);
plt.close('all');
Note that this method only takes values from the right DataFrame if they are
missing in the left DataFrame. A related method, update, alters non-NA
values inplace:
.. ipython:: python :suppress: df1_copy = df1.copy()
.. ipython:: python df1.update(df2)
.. ipython:: python
:suppress:
@savefig merging_update.png
p.plot([df1_copy, df2], df1,
labels=['df1', 'df2'], vertical=False);
plt.close('all');
A :func:`merge_ordered` function allows combining time series and other
ordered data. In particular it has an optional fill_method keyword to
fill/interpolate missing data:
.. ipython:: python
left = pd.DataFrame({'k': ['K0', 'K1', 'K1', 'K2'],
'lv': [1, 2, 3, 4],
's': ['a', 'b', 'c', 'd']})
right = pd.DataFrame({'k': ['K1', 'K2', 'K4'],
'rv': [1, 2, 3]})
pd.merge_ordered(left, right, fill_method='ffill', left_by='s')
.. versionadded:: 0.19.0
A :func:`merge_asof` is similar to an ordered left-join except that we match on nearest key rather than equal keys. For each row in the left DataFrame, we select the last row in the right DataFrame whose on key is less than the left's key. Both DataFrames must be sorted by the key.
Optionally an asof merge can perform a group-wise merge. This matches the by key equally,
in addition to the nearest match on the on key.
For example; we might have trades and quotes and we want to asof merge them.
.. ipython:: python
trades = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.038',
'20160525 13:30:00.048',
'20160525 13:30:00.048',
'20160525 13:30:00.048']),
'ticker': ['MSFT', 'MSFT',
'GOOG', 'GOOG', 'AAPL'],
'price': [51.95, 51.95,
720.77, 720.92, 98.00],
'quantity': [75, 155,
100, 100, 100]},
columns=['time', 'ticker', 'price', 'quantity'])
quotes = pd.DataFrame({
'time': pd.to_datetime(['20160525 13:30:00.023',
'20160525 13:30:00.023',
'20160525 13:30:00.030',
'20160525 13:30:00.041',
'20160525 13:30:00.048',
'20160525 13:30:00.049',
'20160525 13:30:00.072',
'20160525 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]},
columns=['time', 'ticker', 'bid', 'ask'])
.. ipython:: python trades quotes
By default we are taking the asof of the quotes.
.. ipython:: python
pd.merge_asof(trades, quotes,
on='time',
by='ticker')
We only asof within 2ms betwen the quote time and the trade time.
.. ipython:: python
pd.merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=pd.Timedelta('2ms'))
We only asof within 10ms betwen the quote time and the trade time and we exclude exact matches on time.
Note that though we exclude the exact matches (of the quotes), prior quotes DO propogate to that point
in time.
.. ipython:: python
pd.merge_asof(trades, quotes,
on='time',
by='ticker',
tolerance=pd.Timedelta('10ms'),
allow_exact_matches=False)