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 0x7f420c176e50>
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 -0.325072 -0.841215 -2.938900 -0.451186
2013-01-02 -0.069804 0.416590 0.717899 -1.160404
2013-01-03 0.719863 1.334628 -0.180214 -1.094916
2013-01-04 -0.803795 1.707443 -0.223362 0.248133
2013-01-05 2.337252 -0.178261 0.768658 -1.321603
2013-01-06 -0.003333 0.472268 -0.404336 0.528030
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 -0.325072 -0.841215 -2.938900 -0.451186
2013-01-02 -0.069804 0.416590 0.717899 -1.160404
2013-01-03 0.719863 1.334628 -0.180214 -1.094916
2013-01-04 -0.803795 1.707443 -0.223362 0.248133
2013-01-05 2.337252 -0.178261 0.768658 -1.321603
In [15]: df.tail(3).execute()
Out[15]:
A B C D
2013-01-04 -0.803795 1.707443 -0.223362 0.248133
2013-01-05 2.337252 -0.178261 0.768658 -1.321603
2013-01-06 -0.003333 0.472268 -0.404336 0.528030
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([[-0.32507172, -0.84121472, -2.93889954, -0.45118569],
[-0.06980403, 0.4165897 , 0.71789866, -1.16040404],
[ 0.7198634 , 1.33462799, -0.18021438, -1.09491597],
[-0.80379459, 1.70744348, -0.22336171, 0.24813313],
[ 2.33725152, -0.17826103, 0.7686577 , -1.32160335],
[-0.00333293, 0.47226789, -0.40433562, 0.52803039]])
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 0.309185 0.485242 -0.376709 -0.541991
std 1.110317 0.940196 1.351868 0.784021
min -0.803795 -0.841215 -2.938900 -1.321603
25% -0.261255 -0.029548 -0.359092 -1.144032
50% -0.036568 0.444429 -0.201788 -0.773051
75% 0.539064 1.119038 0.493370 0.073303
max 2.337252 1.707443 0.768658 0.528030
Sorting by an axis:
In [21]: df.sort_index(axis=1, ascending=False).execute()
Out[21]:
D C B A
2013-01-01 -0.451186 -2.938900 -0.841215 -0.325072
2013-01-02 -1.160404 0.717899 0.416590 -0.069804
2013-01-03 -1.094916 -0.180214 1.334628 0.719863
2013-01-04 0.248133 -0.223362 1.707443 -0.803795
2013-01-05 -1.321603 0.768658 -0.178261 2.337252
2013-01-06 0.528030 -0.404336 0.472268 -0.003333
Sorting by values:
In [22]: df.sort_values(by='B').execute()
Out[22]:
A B C D
2013-01-01 -0.325072 -0.841215 -2.938900 -0.451186
2013-01-05 2.337252 -0.178261 0.768658 -1.321603
2013-01-02 -0.069804 0.416590 0.717899 -1.160404
2013-01-06 -0.003333 0.472268 -0.404336 0.528030
2013-01-03 0.719863 1.334628 -0.180214 -1.094916
2013-01-04 -0.803795 1.707443 -0.223362 0.248133
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 -0.325072
2013-01-02 -0.069804
2013-01-03 0.719863
2013-01-04 -0.803795
2013-01-05 2.337252
2013-01-06 -0.003333
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 -0.325072 -0.841215 -2.938900 -0.451186
2013-01-02 -0.069804 0.416590 0.717899 -1.160404
2013-01-03 0.719863 1.334628 -0.180214 -1.094916
In [25]: df['20130102':'20130104'].execute()
Out[25]:
A B C D
2013-01-02 -0.069804 0.416590 0.717899 -1.160404
2013-01-03 0.719863 1.334628 -0.180214 -1.094916
2013-01-04 -0.803795 1.707443 -0.223362 0.248133
Selection by label#
For getting a cross section using a label:
In [26]: df.loc['20130101'].execute()
Out[26]:
A -0.325072
B -0.841215
C -2.938900
D -0.451186
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 -0.325072 -0.841215
2013-01-02 -0.069804 0.416590
2013-01-03 0.719863 1.334628
2013-01-04 -0.803795 1.707443
2013-01-05 2.337252 -0.178261
2013-01-06 -0.003333 0.472268
Showing label slicing, both endpoints are included:
In [28]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[28]:
A B
2013-01-02 -0.069804 0.416590
2013-01-03 0.719863 1.334628
2013-01-04 -0.803795 1.707443
Reduction in the dimensions of the returned object:
In [29]: df.loc['20130102', ['A', 'B']].execute()
Out[29]:
A -0.069804
B 0.416590
Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [30]: df.loc['20130101', 'A'].execute()
Out[30]: -0.3250717215964995
For getting fast access to a scalar (equivalent to the prior method):
In [31]: df.at['20130101', 'A'].execute()
Out[31]: -0.3250717215964995
Selection by position#
Select via the position of the passed integers:
In [32]: df.iloc[3].execute()
Out[32]:
A -0.803795
B 1.707443
C -0.223362
D 0.248133
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 -0.803795 1.707443
2013-01-05 2.337252 -0.178261
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 -0.069804 0.717899
2013-01-03 0.719863 -0.180214
2013-01-05 2.337252 0.768658
For slicing rows explicitly:
In [35]: df.iloc[1:3, :].execute()
Out[35]:
A B C D
2013-01-02 -0.069804 0.416590 0.717899 -1.160404
2013-01-03 0.719863 1.334628 -0.180214 -1.094916
For slicing columns explicitly:
In [36]: df.iloc[:, 1:3].execute()
Out[36]:
B C
2013-01-01 -0.841215 -2.938900
2013-01-02 0.416590 0.717899
2013-01-03 1.334628 -0.180214
2013-01-04 1.707443 -0.223362
2013-01-05 -0.178261 0.768658
2013-01-06 0.472268 -0.404336
For getting a value explicitly:
In [37]: df.iloc[1, 1].execute()
Out[37]: 0.416589697239865
For getting fast access to a scalar (equivalent to the prior method):
In [38]: df.iat[1, 1].execute()
Out[38]: 0.416589697239865
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 0.719863 1.334628 -0.180214 -1.094916
2013-01-05 2.337252 -0.178261 0.768658 -1.321603
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 0.416590 0.717899 NaN
2013-01-03 0.719863 1.334628 NaN NaN
2013-01-04 NaN 1.707443 NaN 0.248133
2013-01-05 2.337252 NaN 0.768658 NaN
2013-01-06 NaN 0.472268 NaN 0.528030
Operations#
Stats#
Operations in general exclude missing data.
Performing a descriptive statistic:
In [41]: df.mean().execute()
Out[41]:
A 0.309185
B 0.485242
C -0.376709
D -0.541991
dtype: float64
Same operation on the other axis:
In [42]: df.mean(1).execute()
Out[42]:
2013-01-01 -1.139093
2013-01-02 -0.023930
2013-01-03 0.194840
2013-01-04 0.232105
2013-01-05 0.401511
2013-01-06 0.148157
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 -0.280137 0.334628 -1.180214 -2.094916
2013-01-04 -3.803795 -1.292557 -3.223362 -2.751867
2013-01-05 -2.662748 -5.178261 -4.231342 -6.321603
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 3.141046
B 2.548658
C 3.707557
D 1.849634
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 -0.111168 -0.610583 0.944961 0.223291
1 0.871924 0.227515 0.045617 -0.238502
2 -0.231006 0.047014 -0.171016 0.196644
3 -0.565967 1.921934 1.829373 -1.526664
4 -0.466027 0.940561 -0.046651 -0.799098
5 1.439973 0.055666 -0.326876 0.080001
6 1.806012 0.146856 1.157681 -1.441006
7 0.895919 0.842091 0.314105 0.879768
8 1.128261 -0.498494 1.050752 -1.233651
9 0.905677 0.354411 0.034853 -0.311034
# 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 -0.111168 -0.610583 0.944961 0.223291
1 0.871924 0.227515 0.045617 -0.238502
2 -0.231006 0.047014 -0.171016 0.196644
3 -0.565967 1.921934 1.829373 -1.526664
4 -0.466027 0.940561 -0.046651 -0.799098
5 1.439973 0.055666 -0.326876 0.080001
6 1.806012 0.146856 1.157681 -1.441006
7 0.895919 0.842091 0.314105 0.879768
8 1.128261 -0.498494 1.050752 -1.233651
9 0.905677 0.354411 0.034853 -0.311034
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 0.135101 0.588976
1 bar one 1.010377 1.776787
2 foo two -0.150991 0.347054
3 bar three 0.854944 -0.564876
4 foo two 0.382478 0.676850
5 bar two 1.289967 1.699737
6 foo one 1.087797 -0.538354
7 foo three -0.386876 0.054309
Grouping and then applying the sum() function to the resulting
groups.
In [65]: df.groupby('A').sum().execute()
Out[65]:
C D
A
bar 3.155288 2.911648
foo 1.067509 1.128835
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 1.010377 1.776787
three 0.854944 -0.564876
two 1.289967 1.699737
foo one 1.222897 0.050622
three -0.386876 0.054309
two 0.231488 1.023904
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 0x7f420f608f10>
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 -1.709990 -0.503212 1.080806 0.210275
1 2000-01-02 -2.039664 -1.015497 0.232424 -0.487611
2 2000-01-03 -2.992595 0.597305 1.044619 -2.042844
3 2000-01-04 -3.660657 2.636786 0.147946 -1.866936
4 2000-01-05 -4.155412 1.724421 0.436242 -1.675198
.. ... ... ... ... ...
995 2002-09-22 30.655625 33.853597 -27.704002 -12.458389
996 2002-09-23 32.730193 34.456705 -27.188329 -12.418444
997 2002-09-24 32.100591 34.892548 -27.246512 -12.208398
998 2002-09-25 30.923473 36.485384 -27.548312 -13.093134
999 2002-09-26 30.587739 36.645095 -27.500490 -12.112284
[1000 rows x 5 columns]