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 0x7fccc68bd9d0>

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 -1.882108  0.960855 -0.009459  1.016304
2013-01-02 -1.049234  0.304553  0.530838 -0.043343
2013-01-03  0.213860 -1.431571  1.205769  0.203414
2013-01-04 -0.580846 -0.552984  0.018739 -1.755353
2013-01-05  0.361730 -0.987899  0.100992  2.175861
2013-01-06 -0.498805  0.270995 -0.047999  2.116362

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 -1.882108  0.960855 -0.009459  1.016304
2013-01-02 -1.049234  0.304553  0.530838 -0.043343
2013-01-03  0.213860 -1.431571  1.205769  0.203414
2013-01-04 -0.580846 -0.552984  0.018739 -1.755353
2013-01-05  0.361730 -0.987899  0.100992  2.175861

In [15]: df.tail(3).execute()
Out[15]: 
                   A         B         C         D
2013-01-04 -0.580846 -0.552984  0.018739 -1.755353
2013-01-05  0.361730 -0.987899  0.100992  2.175861
2013-01-06 -0.498805  0.270995 -0.047999  2.116362

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([[-1.8821078 ,  0.96085483, -0.00945897,  1.01630381],
       [-1.04923352,  0.30455254,  0.53083836, -0.04334307],
       [ 0.21385999, -1.43157104,  1.20576944,  0.20341422],
       [-0.58084625, -0.55298387,  0.01873937, -1.75535283],
       [ 0.36172962, -0.9878991 ,  0.10099177,  2.1758611 ],
       [-0.49880457,  0.27099466, -0.04799914,  2.11636246]])

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  -0.572567 -0.239342  0.299813  0.618874
std    0.829336  0.902979  0.491789  1.488135
min   -1.882108 -1.431571 -0.047999 -1.755353
25%   -0.932137 -0.879170 -0.002409  0.018346
50%   -0.539825 -0.140995  0.059866  0.609859
75%    0.035694  0.296163  0.423377  1.841348
max    0.361730  0.960855  1.205769  2.175861

Sorting by an axis:

In [21]: df.sort_index(axis=1, ascending=False).execute()
Out[21]: 
                   D         C         B         A
2013-01-01  1.016304 -0.009459  0.960855 -1.882108
2013-01-02 -0.043343  0.530838  0.304553 -1.049234
2013-01-03  0.203414  1.205769 -1.431571  0.213860
2013-01-04 -1.755353  0.018739 -0.552984 -0.580846
2013-01-05  2.175861  0.100992 -0.987899  0.361730
2013-01-06  2.116362 -0.047999  0.270995 -0.498805

Sorting by values:

In [22]: df.sort_values(by='B').execute()
Out[22]: 
                   A         B         C         D
2013-01-03  0.213860 -1.431571  1.205769  0.203414
2013-01-05  0.361730 -0.987899  0.100992  2.175861
2013-01-04 -0.580846 -0.552984  0.018739 -1.755353
2013-01-06 -0.498805  0.270995 -0.047999  2.116362
2013-01-02 -1.049234  0.304553  0.530838 -0.043343
2013-01-01 -1.882108  0.960855 -0.009459  1.016304

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   -1.882108
2013-01-02   -1.049234
2013-01-03    0.213860
2013-01-04   -0.580846
2013-01-05    0.361730
2013-01-06   -0.498805
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 -1.882108  0.960855 -0.009459  1.016304
2013-01-02 -1.049234  0.304553  0.530838 -0.043343
2013-01-03  0.213860 -1.431571  1.205769  0.203414

In [25]: df['20130102':'20130104'].execute()
Out[25]: 
                   A         B         C         D
2013-01-02 -1.049234  0.304553  0.530838 -0.043343
2013-01-03  0.213860 -1.431571  1.205769  0.203414
2013-01-04 -0.580846 -0.552984  0.018739 -1.755353

Selection by label#

For getting a cross section using a label:

In [26]: df.loc['20130101'].execute()
Out[26]: 
A   -1.882108
B    0.960855
C   -0.009459
D    1.016304
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 -1.882108  0.960855
2013-01-02 -1.049234  0.304553
2013-01-03  0.213860 -1.431571
2013-01-04 -0.580846 -0.552984
2013-01-05  0.361730 -0.987899
2013-01-06 -0.498805  0.270995

Showing label slicing, both endpoints are included:

In [28]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[28]: 
                   A         B
2013-01-02 -1.049234  0.304553
2013-01-03  0.213860 -1.431571
2013-01-04 -0.580846 -0.552984

Reduction in the dimensions of the returned object:

In [29]: df.loc['20130102', ['A', 'B']].execute()
Out[29]: 
A   -1.049234
B    0.304553
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [30]: df.loc['20130101', 'A'].execute()
Out[30]: -1.8821077980771692

For getting fast access to a scalar (equivalent to the prior method):

In [31]: df.at['20130101', 'A'].execute()
Out[31]: -1.8821077980771692

Selection by position#

Select via the position of the passed integers:

In [32]: df.iloc[3].execute()
Out[32]: 
A   -0.580846
B   -0.552984
C    0.018739
D   -1.755353
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 -0.580846 -0.552984
2013-01-05  0.361730 -0.987899

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 -1.049234  0.530838
2013-01-03  0.213860  1.205769
2013-01-05  0.361730  0.100992

For slicing rows explicitly:

In [35]: df.iloc[1:3, :].execute()
Out[35]: 
                   A         B         C         D
2013-01-02 -1.049234  0.304553  0.530838 -0.043343
2013-01-03  0.213860 -1.431571  1.205769  0.203414

For slicing columns explicitly:

In [36]: df.iloc[:, 1:3].execute()
Out[36]: 
                   B         C
2013-01-01  0.960855 -0.009459
2013-01-02  0.304553  0.530838
2013-01-03 -1.431571  1.205769
2013-01-04 -0.552984  0.018739
2013-01-05 -0.987899  0.100992
2013-01-06  0.270995 -0.047999

For getting a value explicitly:

In [37]: df.iloc[1, 1].execute()
Out[37]: 0.30455253590692033

For getting fast access to a scalar (equivalent to the prior method):

In [38]: df.iat[1, 1].execute()
Out[38]: 0.30455253590692033

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  0.21386 -1.431571  1.205769  0.203414
2013-01-05  0.36173 -0.987899  0.100992  2.175861

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  0.960855       NaN  1.016304
2013-01-02      NaN  0.304553  0.530838       NaN
2013-01-03  0.21386       NaN  1.205769  0.203414
2013-01-04      NaN       NaN  0.018739       NaN
2013-01-05  0.36173       NaN  0.100992  2.175861
2013-01-06      NaN  0.270995       NaN  2.116362

Operations#

Stats#

Operations in general exclude missing data.

Performing a descriptive statistic:

In [41]: df.mean().execute()
Out[41]: 
A   -0.572567
B   -0.239342
C    0.299813
D    0.618874
dtype: float64

Same operation on the other axis:

In [42]: df.mean(1).execute()
Out[42]: 
2013-01-01    0.021398
2013-01-02   -0.064296
2013-01-03    0.047868
2013-01-04   -0.717611
2013-01-05    0.412671
2013-01-06    0.460138
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 -0.786140 -2.431571  0.205769 -0.796586
2013-01-04 -3.580846 -3.552984 -2.981261 -4.755353
2013-01-05 -4.638270 -5.987899 -4.899008 -2.824139
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    2.243837
B    2.392426
C    1.253769
D    3.931214
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  0.987942 -0.929619 -0.066542 -2.142220
1  0.612161  1.606244 -1.309438  0.076113
2 -0.419658 -0.289739 -1.657454 -0.115085
3  1.953100  0.686447 -1.513060  0.598228
4  0.134242 -0.313226  1.966364 -0.069673
5  0.456217  0.279538  0.440514 -0.220514
6 -0.479430 -0.648801 -0.841180 -0.892234
7 -0.662661 -0.059356  0.375136  0.202591
8  1.602243  0.110080 -0.699141 -1.101087
9  0.673317  0.431241 -0.048495  0.201615

# 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  0.987942 -0.929619 -0.066542 -2.142220
1  0.612161  1.606244 -1.309438  0.076113
2 -0.419658 -0.289739 -1.657454 -0.115085
3  1.953100  0.686447 -1.513060  0.598228
4  0.134242 -0.313226  1.966364 -0.069673
5  0.456217  0.279538  0.440514 -0.220514
6 -0.479430 -0.648801 -0.841180 -0.892234
7 -0.662661 -0.059356  0.375136  0.202591
8  1.602243  0.110080 -0.699141 -1.101087
9  0.673317  0.431241 -0.048495  0.201615

Note

Adding a column to a DataFrame is relatively fast. However, adding a row requires a copy, and may be expensive. We recommend passing a pre-built list of records to the DataFrame constructor instead of building a DataFrame by iteratively appending records to it.

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  1.299919  0.249839
1  bar    one  0.641778  0.430571
2  foo    two  2.248535 -2.493165
3  bar  three  0.486686 -0.901611
4  foo    two  0.614228  0.297693
5  bar    two  0.993868  0.086885
6  foo    one -0.271946 -0.922745
7  foo  three -1.713409  1.831023

Grouping and then applying the sum() function to the resulting groups.

In [65]: df.groupby('A').sum().execute()
Out[65]: 
            C         D
A                      
bar  2.122332 -0.384155
foo  2.177327 -1.037355

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    0.641778  0.430571
    three  0.486686 -0.901611
    two    0.993868  0.086885
foo one    1.027973 -0.672906
    three -1.713409  1.831023
    two    2.862763 -2.195472

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:>
../../_images/series_plot_basic.png

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 0x7fccc6a99650>
../../_images/frame_plot_basic.png

Getting data in/out#

CSV#

In [77]: df.to_csv('foo.csv').execute()
Out[77]: 
Empty DataFrame
Columns: []
Index: []

Reading from a csv file.

In [78]: md.read_csv('foo.csv').execute()
Out[78]: 
     Unnamed: 0          A          B          C          D
0    2000-01-01  -0.657630  -1.628396  -0.521589  -0.628873
1    2000-01-02  -0.618936  -2.408207   0.374808  -3.626958
2    2000-01-03   0.738739  -2.872245   1.326673  -3.479289
3    2000-01-04   1.906156  -2.158316   0.062554  -3.765099
4    2000-01-05   2.149986  -2.311702   0.637701  -3.184967
..          ...        ...        ...        ...        ...
995  2002-09-22  21.214772 -42.538924 -42.710949  32.937965
996  2002-09-23  20.857961 -42.768397 -42.648203  31.736822
997  2002-09-24  21.377999 -42.700526 -41.447758  32.803241
998  2002-09-25  22.125392 -43.409634 -41.340038  32.609549
999  2002-09-26  21.517450 -43.961246 -40.317388  32.710258

[1000 rows x 5 columns]