10 minutes to Mars DataFrame#
This is a short introduction to Mars DataFrame which is originated from 10 minutes to pandas.
Customarily, we import as follows:
In [1]: import mars
In [2]: import mars.tensor as mt
In [3]: import mars.dataframe as md
Now create a new default session.
In [4]: mars.new_session()
Out[4]: <mars.deploy.oscar.session.SyncSession at 0x7f348d98a690>
Object creation#
Creating a Series by passing a list of values, letting it create
a default integer index:
In [5]: s = md.Series([1, 3, 5, mt.nan, 6, 8])
In [6]: s.execute()
Out[6]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
Creating a DataFrame by passing a Mars tensor, with a datetime index
and labeled columns:
In [7]: dates = md.date_range('20130101', periods=6)
In [8]: dates.execute()
Out[8]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [9]: df = md.DataFrame(mt.random.randn(6, 4), index=dates, columns=list('ABCD'))
In [10]: df.execute()
Out[10]:
A B C D
2013-01-01 -0.408528 -1.615864 -0.414608 -0.979481
2013-01-02 -0.490754 0.165001 0.953814 0.406053
2013-01-03 1.145544 0.437361 1.031433 0.605406
2013-01-04 -1.395899 0.031388 -0.313637 0.360562
2013-01-05 1.152373 -2.142283 0.454617 -1.062158
2013-01-06 2.122499 2.550671 -0.832217 -0.981746
Creating a DataFrame by passing a dict of objects that can be converted to series-like.
In [11]: df2 = md.DataFrame({'A': 1.,
....: 'B': md.Timestamp('20130102'),
....: 'C': md.Series(1, index=list(range(4)), dtype='float32'),
....: 'D': mt.array([3] * 4, dtype='int32'),
....: 'E': 'foo'})
....:
In [12]: df2.execute()
Out[12]:
A B C D E
0 1.0 2013-01-02 1.0 3 foo
1 1.0 2013-01-02 1.0 3 foo
2 1.0 2013-01-02 1.0 3 foo
3 1.0 2013-01-02 1.0 3 foo
The columns of the resulting DataFrame have different dtypes.
In [13]: df2.dtypes
Out[13]:
A float64
B datetime64[ns]
C float32
D int32
E object
dtype: object
Viewing data#
Here is how to view the top and bottom rows of the frame:
In [14]: df.head().execute()
Out[14]:
A B C D
2013-01-01 -0.408528 -1.615864 -0.414608 -0.979481
2013-01-02 -0.490754 0.165001 0.953814 0.406053
2013-01-03 1.145544 0.437361 1.031433 0.605406
2013-01-04 -1.395899 0.031388 -0.313637 0.360562
2013-01-05 1.152373 -2.142283 0.454617 -1.062158
In [15]: df.tail(3).execute()
Out[15]:
A B C D
2013-01-04 -1.395899 0.031388 -0.313637 0.360562
2013-01-05 1.152373 -2.142283 0.454617 -1.062158
2013-01-06 2.122499 2.550671 -0.832217 -0.981746
Display the index, columns:
In [16]: df.index.execute()
Out[16]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [17]: df.columns.execute()
Out[17]: Index(['A', 'B', 'C', 'D'], dtype='object')
DataFrame.to_tensor() gives a Mars tensor representation of the underlying data.
Note that this can be an expensive operation when your DataFrame has
columns with different data types, which comes down to a fundamental difference
between DataFrame and tensor: tensors have one dtype for the entire tensor,
while DataFrames have one dtype per column. When you call
DataFrame.to_tensor(), Mars DataFrame will find the tensor dtype that can hold all
of the dtypes in the DataFrame. This may end up being object, which requires
casting every value to a Python object.
For df, our DataFrame of all floating-point values,
DataFrame.to_tensor() is fast and doesn’t require copying data.
In [18]: df.to_tensor().execute()
Out[18]:
array([[-0.40852821, -1.61586429, -0.41460765, -0.97948149],
[-0.49075401, 0.16500056, 0.95381411, 0.40605319],
[ 1.14554439, 0.43736114, 1.03143266, 0.60540606],
[-1.39589861, 0.03138783, -0.31363672, 0.36056206],
[ 1.15237294, -2.14228337, 0.45461671, -1.0621575 ],
[ 2.12249899, 2.55067103, -0.83221736, -0.98174604]])
For df2, the DataFrame with multiple dtypes,
DataFrame.to_tensor() is relatively expensive.
In [19]: df2.to_tensor().execute()
Out[19]:
array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'foo']],
dtype=object)
Note
DataFrame.to_tensor() does not include the index or column
labels in the output.
describe() shows a quick statistic summary of your data:
In [20]: df.describe().execute()
Out[20]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.354206 -0.095621 0.146567 -0.275227
std 1.322780 1.665590 0.776435 0.807253
min -1.395899 -2.142283 -0.832217 -1.062158
25% -0.470198 -1.204051 -0.389365 -0.981180
50% 0.368508 0.098194 0.070490 -0.309460
75% 1.150666 0.369271 0.829015 0.394680
max 2.122499 2.550671 1.031433 0.605406
Sorting by an axis:
In [21]: df.sort_index(axis=1, ascending=False).execute()
Out[21]:
D C B A
2013-01-01 -0.979481 -0.414608 -1.615864 -0.408528
2013-01-02 0.406053 0.953814 0.165001 -0.490754
2013-01-03 0.605406 1.031433 0.437361 1.145544
2013-01-04 0.360562 -0.313637 0.031388 -1.395899
2013-01-05 -1.062158 0.454617 -2.142283 1.152373
2013-01-06 -0.981746 -0.832217 2.550671 2.122499
Sorting by values:
In [22]: df.sort_values(by='B').execute()
Out[22]:
A B C D
2013-01-05 1.152373 -2.142283 0.454617 -1.062158
2013-01-01 -0.408528 -1.615864 -0.414608 -0.979481
2013-01-04 -1.395899 0.031388 -0.313637 0.360562
2013-01-02 -0.490754 0.165001 0.953814 0.406053
2013-01-03 1.145544 0.437361 1.031433 0.605406
2013-01-06 2.122499 2.550671 -0.832217 -0.981746
Selection#
Note
While standard Python / Numpy expressions for selecting and setting are
intuitive and come in handy for interactive work, for production code, we
recommend the optimized DataFrame data access methods, .at, .iat,
.loc and .iloc.
Getting#
Selecting a single column, which yields a Series,
equivalent to df.A:
In [23]: df['A'].execute()
Out[23]:
2013-01-01 -0.408528
2013-01-02 -0.490754
2013-01-03 1.145544
2013-01-04 -1.395899
2013-01-05 1.152373
2013-01-06 2.122499
Freq: D, Name: A, dtype: float64
Selecting via [], which slices the rows.
In [24]: df[0:3].execute()
Out[24]:
A B C D
2013-01-01 -0.408528 -1.615864 -0.414608 -0.979481
2013-01-02 -0.490754 0.165001 0.953814 0.406053
2013-01-03 1.145544 0.437361 1.031433 0.605406
In [25]: df['20130102':'20130104'].execute()
Out[25]:
A B C D
2013-01-02 -0.490754 0.165001 0.953814 0.406053
2013-01-03 1.145544 0.437361 1.031433 0.605406
2013-01-04 -1.395899 0.031388 -0.313637 0.360562
Selection by label#
For getting a cross section using a label:
In [26]: df.loc['20130101'].execute()
Out[26]:
A -0.408528
B -1.615864
C -0.414608
D -0.979481
Name: 2013-01-01 00:00:00, dtype: float64
Selecting on a multi-axis by label:
In [27]: df.loc[:, ['A', 'B']].execute()
Out[27]:
A B
2013-01-01 -0.408528 -1.615864
2013-01-02 -0.490754 0.165001
2013-01-03 1.145544 0.437361
2013-01-04 -1.395899 0.031388
2013-01-05 1.152373 -2.142283
2013-01-06 2.122499 2.550671
Showing label slicing, both endpoints are included:
In [28]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[28]:
A B
2013-01-02 -0.490754 0.165001
2013-01-03 1.145544 0.437361
2013-01-04 -1.395899 0.031388
Reduction in the dimensions of the returned object:
In [29]: df.loc['20130102', ['A', 'B']].execute()
Out[29]:
A -0.490754
B 0.165001
Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [30]: df.loc['20130101', 'A'].execute()
Out[30]: -0.4085282147445123
For getting fast access to a scalar (equivalent to the prior method):
In [31]: df.at['20130101', 'A'].execute()
Out[31]: -0.4085282147445123
Selection by position#
Select via the position of the passed integers:
In [32]: df.iloc[3].execute()
Out[32]:
A -1.395899
B 0.031388
C -0.313637
D 0.360562
Name: 2013-01-04 00:00:00, dtype: float64
By integer slices, acting similar to numpy/python:
In [33]: df.iloc[3:5, 0:2].execute()
Out[33]:
A B
2013-01-04 -1.395899 0.031388
2013-01-05 1.152373 -2.142283
By lists of integer position locations, similar to the numpy/python style:
In [34]: df.iloc[[1, 2, 4], [0, 2]].execute()
Out[34]:
A C
2013-01-02 -0.490754 0.953814
2013-01-03 1.145544 1.031433
2013-01-05 1.152373 0.454617
For slicing rows explicitly:
In [35]: df.iloc[1:3, :].execute()
Out[35]:
A B C D
2013-01-02 -0.490754 0.165001 0.953814 0.406053
2013-01-03 1.145544 0.437361 1.031433 0.605406
For slicing columns explicitly:
In [36]: df.iloc[:, 1:3].execute()
Out[36]:
B C
2013-01-01 -1.615864 -0.414608
2013-01-02 0.165001 0.953814
2013-01-03 0.437361 1.031433
2013-01-04 0.031388 -0.313637
2013-01-05 -2.142283 0.454617
2013-01-06 2.550671 -0.832217
For getting a value explicitly:
In [37]: df.iloc[1, 1].execute()
Out[37]: 0.16500056492327506
For getting fast access to a scalar (equivalent to the prior method):
In [38]: df.iat[1, 1].execute()
Out[38]: 0.16500056492327506
Boolean indexing#
Using a single column’s values to select data.
In [39]: df[df['A'] > 0].execute()
Out[39]:
A B C D
2013-01-03 1.145544 0.437361 1.031433 0.605406
2013-01-05 1.152373 -2.142283 0.454617 -1.062158
2013-01-06 2.122499 2.550671 -0.832217 -0.981746
Selecting values from a DataFrame where a boolean condition is met.
In [40]: df[df > 0].execute()
Out[40]:
A B C D
2013-01-01 NaN NaN NaN NaN
2013-01-02 NaN 0.165001 0.953814 0.406053
2013-01-03 1.145544 0.437361 1.031433 0.605406
2013-01-04 NaN 0.031388 NaN 0.360562
2013-01-05 1.152373 NaN 0.454617 NaN
2013-01-06 2.122499 2.550671 NaN NaN
Operations#
Stats#
Operations in general exclude missing data.
Performing a descriptive statistic:
In [41]: df.mean().execute()
Out[41]:
A 0.354206
B -0.095621
C 0.146567
D -0.275227
dtype: float64
Same operation on the other axis:
In [42]: df.mean(1).execute()
Out[42]:
2013-01-01 -0.854620
2013-01-02 0.258528
2013-01-03 0.804936
2013-01-04 -0.329396
2013-01-05 -0.399363
2013-01-06 0.714802
Freq: D, dtype: float64
Operating with objects that have different dimensionality and need alignment. In addition, Mars DataFrame automatically broadcasts along the specified dimension.
In [43]: s = md.Series([1, 3, 5, mt.nan, 6, 8], index=dates).shift(2)
In [44]: s.execute()
Out[44]:
2013-01-01 NaN
2013-01-02 NaN
2013-01-03 1.0
2013-01-04 3.0
2013-01-05 5.0
2013-01-06 NaN
Freq: D, dtype: float64
In [45]: df.sub(s, axis='index').execute()
Out[45]:
A B C D
2013-01-01 NaN NaN NaN NaN
2013-01-02 NaN NaN NaN NaN
2013-01-03 0.145544 -0.562639 0.031433 -0.394594
2013-01-04 -4.395899 -2.968612 -3.313637 -2.639438
2013-01-05 -3.847627 -7.142283 -4.545383 -6.062158
2013-01-06 NaN NaN NaN NaN
Apply#
Applying functions to the data:
In [46]: df.apply(lambda x: x.max() - x.min()).execute()
Out[46]:
A 3.518398
B 4.692954
C 1.863650
D 1.667564
dtype: float64
String Methods#
Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses regular expressions by default (and in some cases always uses them). See more at Vectorized String Methods.
In [47]: s = md.Series(['A', 'B', 'C', 'Aaba', 'Baca', mt.nan, 'CABA', 'dog', 'cat'])
In [48]: s.str.lower().execute()
Out[48]:
0 a
1 b
2 c
3 aaba
4 baca
5 NaN
6 caba
7 dog
8 cat
dtype: object
Merge#
Concat#
Mars DataFrame provides various facilities for easily combining together Series and DataFrame objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
Concatenating DataFrame objects together with concat():
In [49]: df = md.DataFrame(mt.random.randn(10, 4))
In [50]: df.execute()
Out[50]:
0 1 2 3
0 0.115075 0.884831 0.478299 0.416459
1 0.250367 0.826709 0.603764 0.811368
2 -1.710826 -0.622261 -0.583373 1.292714
3 0.215724 0.119656 1.232683 0.050237
4 -0.439354 -0.683713 0.005588 0.566589
5 -0.220359 -0.294231 -0.035997 0.134134
6 0.313627 -0.071105 0.176026 0.039439
7 -1.006887 1.401754 0.060687 -1.002063
8 -1.292104 0.252355 -0.904728 1.582943
9 1.014965 -0.848492 1.425587 0.562507
# break it into pieces
In [51]: pieces = [df[:3], df[3:7], df[7:]]
In [52]: md.concat(pieces).execute()
Out[52]:
0 1 2 3
0 0.115075 0.884831 0.478299 0.416459
1 0.250367 0.826709 0.603764 0.811368
2 -1.710826 -0.622261 -0.583373 1.292714
3 0.215724 0.119656 1.232683 0.050237
4 -0.439354 -0.683713 0.005588 0.566589
5 -0.220359 -0.294231 -0.035997 0.134134
6 0.313627 -0.071105 0.176026 0.039439
7 -1.006887 1.401754 0.060687 -1.002063
8 -1.292104 0.252355 -0.904728 1.582943
9 1.014965 -0.848492 1.425587 0.562507
Join#
SQL style merges. See the Database style joining section.
In [53]: left = md.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
In [54]: right = md.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
In [55]: left.execute()
Out[55]:
key lval
0 foo 1
1 foo 2
In [56]: right.execute()
Out[56]:
key rval
0 foo 4
1 foo 5
In [57]: md.merge(left, right, on='key').execute()
Out[57]:
key lval rval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5
Another example that can be given is:
In [58]: left = md.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
In [59]: right = md.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
In [60]: left.execute()
Out[60]:
key lval
0 foo 1
1 bar 2
In [61]: right.execute()
Out[61]:
key rval
0 foo 4
1 bar 5
In [62]: md.merge(left, right, on='key').execute()
Out[62]:
key lval rval
0 foo 1 4
1 bar 2 5
Grouping#
By “group by” we are referring to a process involving one or more of the following steps:
Splitting the data into groups based on some criteria
Applying a function to each group independently
Combining the results into a data structure
In [63]: df = md.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
....: 'foo', 'bar', 'foo', 'foo'],
....: 'B': ['one', 'one', 'two', 'three',
....: 'two', 'two', 'one', 'three'],
....: 'C': mt.random.randn(8),
....: 'D': mt.random.randn(8)})
....:
In [64]: df.execute()
Out[64]:
A B C D
0 foo one -0.378475 -1.768423
1 bar one 0.646488 0.670867
2 foo two -0.799690 -0.994373
3 bar three -0.429681 -1.156063
4 foo two -1.411407 -0.895470
5 bar two 1.815013 1.745927
6 foo one 1.873860 0.868160
7 foo three 0.303657 -1.404396
Grouping and then applying the sum() function to the resulting
groups.
In [65]: df.groupby('A').sum().execute()
Out[65]:
C D
A
bar 2.031820 1.260731
foo -0.412055 -4.194502
Grouping by multiple columns forms a hierarchical index, and again we can apply the sum function.
In [66]: df.groupby(['A', 'B']).sum().execute()
Out[66]:
C D
A B
bar one 0.646488 0.670867
three -0.429681 -1.156063
two 1.815013 1.745927
foo one 1.495385 -0.900263
three 0.303657 -1.404396
two -2.211097 -1.889843
Plotting#
We use the standard convention for referencing the matplotlib API:
In [67]: import matplotlib.pyplot as plt
In [68]: plt.close('all')
In [69]: ts = md.Series(mt.random.randn(1000),
....: index=md.date_range('1/1/2000', periods=1000))
....:
In [70]: ts = ts.cumsum()
In [71]: ts.plot()
Out[71]: <AxesSubplot:>
On a DataFrame, the plot() method is a convenience to plot all
of the columns with labels:
In [72]: df = md.DataFrame(mt.random.randn(1000, 4), index=ts.index,
....: columns=['A', 'B', 'C', 'D'])
....:
In [73]: df = df.cumsum()
In [74]: plt.figure()
Out[74]: <Figure size 640x480 with 0 Axes>
In [75]: df.plot()
Out[75]: <AxesSubplot:>
In [76]: plt.legend(loc='best')
Out[76]: <matplotlib.legend.Legend at 0x7f3493af4ad0>
Getting data in/out#
CSV#
In [77]: df.to_csv('foo.csv').execute()
Out[77]:
Empty DataFrame
Columns: []
Index: []
In [78]: md.read_csv('foo.csv').execute()
Out[78]:
Unnamed: 0 A B C D
0 2000-01-01 -0.114199 1.435991 0.313251 -0.369235
1 2000-01-02 -0.436170 1.211855 -1.337571 -0.093541
2 2000-01-03 0.798615 3.331479 -0.737810 0.113401
3 2000-01-04 0.720034 4.373077 -1.629931 1.252530
4 2000-01-05 1.729162 4.733967 -2.373432 1.606382
.. ... ... ... ... ...
995 2002-09-22 -52.892286 4.551149 -31.389413 20.340402
996 2002-09-23 -53.597744 3.677439 -32.662184 20.555935
997 2002-09-24 -54.506083 2.744942 -31.397477 20.441688
998 2002-09-25 -53.458040 2.556464 -31.120635 20.500559
999 2002-09-26 -54.590758 4.216846 -29.428213 21.627531
[1000 rows x 5 columns]