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.tensor as mt

In [2]: import mars.dataframe as md

Object creation

Creating a Series by passing a list of values, letting it create a default integer index:

In [3]: s = md.Series([1, 3, 5, mt.nan, 6, 8])

In [4]: s.execute()
Out[4]: 
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 [5]: dates = md.date_range('20130101', periods=6)

In [6]: dates.execute()
Out[6]: 
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 [7]: df = md.DataFrame(mt.random.randn(6, 4), index=dates, columns=list('ABCD'))

In [8]: df.execute()
Out[8]: 
                   A         B         C         D
2013-01-01  1.931407  0.690645 -0.356246 -1.871824
2013-01-02 -0.715638  1.199600  0.890146 -0.353785
2013-01-03  0.120903  1.217065  0.779198  0.945284
2013-01-04 -0.424614 -1.611170  0.236505  0.784191
2013-01-05  0.565586  1.038352 -0.118088 -1.297175
2013-01-06  1.134355 -0.184953 -0.922892  1.322544

Creating a DataFrame by passing a dict of objects that can be converted to series-like.

In [9]: 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 [10]: df2.execute()
Out[10]: 
     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 [11]: df2.dtypes
Out[11]: 
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 [12]: df.head().execute()
Out[12]: 
                   A         B         C         D
2013-01-01  1.931407  0.690645 -0.356246 -1.871824
2013-01-02 -0.715638  1.199600  0.890146 -0.353785
2013-01-03  0.120903  1.217065  0.779198  0.945284
2013-01-04 -0.424614 -1.611170  0.236505  0.784191
2013-01-05  0.565586  1.038352 -0.118088 -1.297175

In [13]: df.tail(3).execute()
Out[13]: 
                   A         B         C         D
2013-01-04 -0.424614 -1.611170  0.236505  0.784191
2013-01-05  0.565586  1.038352 -0.118088 -1.297175
2013-01-06  1.134355 -0.184953 -0.922892  1.322544

Display the index, columns:

In [14]: df.index.execute()
Out[14]: 
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 [15]: df.columns.execute()
Out[15]: 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 [16]: df.to_tensor().execute()
Out[16]: 
array([[ 1.93140749,  0.69064467, -0.3562465 , -1.87182408],
       [-0.71563757,  1.19960013,  0.89014555, -0.35378519],
       [ 0.12090274,  1.2170653 ,  0.77919784,  0.94528373],
       [-0.42461393, -1.61116961,  0.23650504,  0.78419148],
       [ 0.5655862 ,  1.03835244, -0.11808809, -1.2971749 ],
       [ 1.13435483, -0.18495251, -0.9228917 ,  1.32254439]])

For df2, the DataFrame with multiple dtypes, DataFrame.to_tensor() is relatively expensive.

In [17]: df2.to_tensor().execute()
Out[17]: 
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 [18]: df.describe().execute()
Out[18]: 
              A         B         C         D
count  6.000000  6.000000  6.000000  6.000000
mean   0.435333  0.391590  0.084770 -0.078461
std    0.990651  1.112247  0.693529  1.306312
min   -0.715638 -1.611170 -0.922892 -1.871824
25%   -0.288235  0.033947 -0.296707 -1.061327
50%    0.343244  0.864499  0.059208  0.215203
75%    0.992163  1.159288  0.643525  0.905011
max    1.931407  1.217065  0.890146  1.322544

Sorting by an axis:

In [19]: df.sort_index(axis=1, ascending=False).execute()
Out[19]: 
                   D         C         B         A
2013-01-01 -1.871824 -0.356246  0.690645  1.931407
2013-01-02 -0.353785  0.890146  1.199600 -0.715638
2013-01-03  0.945284  0.779198  1.217065  0.120903
2013-01-04  0.784191  0.236505 -1.611170 -0.424614
2013-01-05 -1.297175 -0.118088  1.038352  0.565586
2013-01-06  1.322544 -0.922892 -0.184953  1.134355

Sorting by values:

In [20]: df.sort_values(by='B').execute()
Out[20]: 
                   A         B         C         D
2013-01-04 -0.424614 -1.611170  0.236505  0.784191
2013-01-06  1.134355 -0.184953 -0.922892  1.322544
2013-01-01  1.931407  0.690645 -0.356246 -1.871824
2013-01-05  0.565586  1.038352 -0.118088 -1.297175
2013-01-02 -0.715638  1.199600  0.890146 -0.353785
2013-01-03  0.120903  1.217065  0.779198  0.945284

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 [21]: df['A'].execute()
Out[21]: 
2013-01-01    1.931407
2013-01-02   -0.715638
2013-01-03    0.120903
2013-01-04   -0.424614
2013-01-05    0.565586
2013-01-06    1.134355
Freq: D, Name: A, dtype: float64

Selecting via [], which slices the rows.

In [22]: df[0:3].execute()
Out[22]: 
                   A         B         C         D
2013-01-01  1.931407  0.690645 -0.356246 -1.871824
2013-01-02 -0.715638  1.199600  0.890146 -0.353785
2013-01-03  0.120903  1.217065  0.779198  0.945284

In [23]: df['20130102':'20130104'].execute()
Out[23]: 
                   A         B         C         D
2013-01-02 -0.715638  1.199600  0.890146 -0.353785
2013-01-03  0.120903  1.217065  0.779198  0.945284
2013-01-04 -0.424614 -1.611170  0.236505  0.784191

Selection by label

For getting a cross section using a label:

In [24]: df.loc['20130101'].execute()
Out[24]: 
A    1.931407
B    0.690645
C   -0.356246
D   -1.871824
Name: 2013-01-01 00:00:00, dtype: float64

Selecting on a multi-axis by label:

In [25]: df.loc[:, ['A', 'B']].execute()
Out[25]: 
                   A         B
2013-01-01  1.931407  0.690645
2013-01-02 -0.715638  1.199600
2013-01-03  0.120903  1.217065
2013-01-04 -0.424614 -1.611170
2013-01-05  0.565586  1.038352
2013-01-06  1.134355 -0.184953

Showing label slicing, both endpoints are included:

In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[26]: 
                   A         B
2013-01-02 -0.715638  1.199600
2013-01-03  0.120903  1.217065
2013-01-04 -0.424614 -1.611170

Reduction in the dimensions of the returned object:

In [27]: df.loc['20130102', ['A', 'B']].execute()
Out[27]: 
A   -0.715638
B    1.199600
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [28]: df.loc['20130101', 'A'].execute()
Out[28]: 1.9314074910209367

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

In [29]: df.at['20130101', 'A'].execute()
Out[29]: 1.9314074910209367

Selection by position

Select via the position of the passed integers:

In [30]: df.iloc[3].execute()
Out[30]: 
A   -0.424614
B   -1.611170
C    0.236505
D    0.784191
Name: 2013-01-04 00:00:00, dtype: float64

By integer slices, acting similar to numpy/python:

In [31]: df.iloc[3:5, 0:2].execute()
Out[31]: 
                   A         B
2013-01-04 -0.424614 -1.611170
2013-01-05  0.565586  1.038352

By lists of integer position locations, similar to the numpy/python style:

In [32]: df.iloc[[1, 2, 4], [0, 2]].execute()
Out[32]: 
                   A         C
2013-01-02 -0.715638  0.890146
2013-01-03  0.120903  0.779198
2013-01-05  0.565586 -0.118088

For slicing rows explicitly:

In [33]: df.iloc[1:3, :].execute()
Out[33]: 
                   A         B         C         D
2013-01-02 -0.715638  1.199600  0.890146 -0.353785
2013-01-03  0.120903  1.217065  0.779198  0.945284

For slicing columns explicitly:

In [34]: df.iloc[:, 1:3].execute()
Out[34]: 
                   B         C
2013-01-01  0.690645 -0.356246
2013-01-02  1.199600  0.890146
2013-01-03  1.217065  0.779198
2013-01-04 -1.611170  0.236505
2013-01-05  1.038352 -0.118088
2013-01-06 -0.184953 -0.922892

For getting a value explicitly:

In [35]: df.iloc[1, 1].execute()
Out[35]: 1.1996001268725445

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

In [36]: df.iat[1, 1].execute()
Out[36]: 1.1996001268725445

Boolean indexing

Using a single column’s values to select data.

In [37]: df[df['A'] > 0].execute()
Out[37]: 
                   A         B         C         D
2013-01-01  1.931407  0.690645 -0.356246 -1.871824
2013-01-03  0.120903  1.217065  0.779198  0.945284
2013-01-05  0.565586  1.038352 -0.118088 -1.297175
2013-01-06  1.134355 -0.184953 -0.922892  1.322544

Selecting values from a DataFrame where a boolean condition is met.

In [38]: df[df > 0].execute()
Out[38]: 
                   A         B         C         D
2013-01-01  1.931407  0.690645       NaN       NaN
2013-01-02       NaN  1.199600  0.890146       NaN
2013-01-03  0.120903  1.217065  0.779198  0.945284
2013-01-04       NaN       NaN  0.236505  0.784191
2013-01-05  0.565586  1.038352       NaN       NaN
2013-01-06  1.134355       NaN       NaN  1.322544

Operations

Stats

Operations in general exclude missing data.

Performing a descriptive statistic:

In [39]: df.mean().execute()
Out[39]: 
A    0.435333
B    0.391590
C    0.084770
D   -0.078461
dtype: float64

Same operation on the other axis:

In [40]: df.mean(1).execute()
Out[40]: 
2013-01-01    0.098495
2013-01-02    0.255081
2013-01-03    0.765612
2013-01-04   -0.253772
2013-01-05    0.047169
2013-01-06    0.337264
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 [41]: s = md.Series([1, 3, 5, mt.nan, 6, 8], index=dates).shift(2)

In [42]: s.execute()
Out[42]: 
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 [43]: df.sub(s, axis='index').execute()
Out[43]: 
                   A         B         C         D
2013-01-01       NaN       NaN       NaN       NaN
2013-01-02       NaN       NaN       NaN       NaN
2013-01-03 -0.879097  0.217065 -0.220802 -0.054716
2013-01-04 -3.424614 -4.611170 -2.763495 -2.215809
2013-01-05 -4.434414 -3.961648 -5.118088 -6.297175
2013-01-06       NaN       NaN       NaN       NaN

Apply

Applying functions to the data:

In [44]: df.apply(lambda x: x.max() - x.min()).execute()
Out[44]: 
A    2.647045
B    2.828235
C    1.813037
D    3.194368
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 [45]: s = md.Series(['A', 'B', 'C', 'Aaba', 'Baca', mt.nan, 'CABA', 'dog', 'cat'])

In [46]: s.str.lower().execute()
Out[46]: 
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 [47]: df = md.DataFrame(mt.random.randn(10, 4))

In [48]: df.execute()
Out[48]: 
          0         1         2         3
0 -1.249995 -0.377896  0.881990  0.428890
1 -1.131498  1.340801 -1.052318  0.201938
2 -1.200298  0.822057  0.136660  0.798779
3  0.627171  0.395426 -2.056140  1.325629
4  0.140962  0.480946  0.446364  0.638284
5  1.165917 -0.227402  0.468240  0.987829
6  0.679503  0.403699  0.176963  0.391028
7  2.011149  0.692399  1.290789  1.937144
8  1.911354  0.609072 -0.111463  0.134656
9  2.196636  0.242138  0.588012 -1.585527

# break it into pieces
In [49]: pieces = [df[:3], df[3:7], df[7:]]

In [50]: md.concat(pieces).execute()
Out[50]: 
          0         1         2         3
0 -1.249995 -0.377896  0.881990  0.428890
1 -1.131498  1.340801 -1.052318  0.201938
2 -1.200298  0.822057  0.136660  0.798779
3  0.627171  0.395426 -2.056140  1.325629
4  0.140962  0.480946  0.446364  0.638284
5  1.165917 -0.227402  0.468240  0.987829
6  0.679503  0.403699  0.176963  0.391028
7  2.011149  0.692399  1.290789  1.937144
8  1.911354  0.609072 -0.111463  0.134656
9  2.196636  0.242138  0.588012 -1.585527

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 [51]: left = md.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})

In [52]: right = md.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})

In [53]: left.execute()
Out[53]: 
   key  lval
0  foo     1
1  foo     2

In [54]: right.execute()
Out[54]: 
   key  rval
0  foo     4
1  foo     5

In [55]: md.merge(left, right, on='key').execute()
Out[55]: 
   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 [56]: left = md.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})

In [57]: right = md.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})

In [58]: left.execute()
Out[58]: 
   key  lval
0  foo     1
1  bar     2

In [59]: right.execute()
Out[59]: 
   key  rval
0  foo     4
1  bar     5

In [60]: md.merge(left, right, on='key').execute()
Out[60]: 
   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 [61]: 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 [62]: df.execute()
Out[62]: 
     A      B         C         D
0  foo    one  1.710894  1.546415
1  bar    one  0.241495 -0.278982
2  foo    two -0.016772  0.110815
3  bar  three -1.186565 -1.336349
4  foo    two -1.033151  1.333253
5  bar    two -1.263034  0.594817
6  foo    one  0.869332 -0.483507
7  foo  three  2.206120  0.025061

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

In [63]: df.groupby('A').sum().execute()
Out[63]: 
            C         D
A                      
bar -2.208104 -1.020514
foo  3.736423  2.532036

Grouping by multiple columns forms a hierarchical index, and again we can apply the sum function.

In [64]: df.groupby(['A', 'B']).sum().execute()
Out[64]: 
                  C         D
A   B                        
foo one    2.580226  1.062908
    two   -1.049923  1.444068
    three  2.206120  0.025061
bar one    0.241495 -0.278982
    two   -1.263034  0.594817
    three -1.186565 -1.336349

Plotting

We use the standard convention for referencing the matplotlib API:

In [65]: import matplotlib.pyplot as plt

In [66]: plt.close('all')
In [67]: ts = md.Series(mt.random.randn(1000),
   ....:                index=md.date_range('1/1/2000', periods=1000))
   ....: 

In [68]: ts = ts.cumsum()

In [69]: ts.plot()
Out[69]: <matplotlib.axes._subplots.AxesSubplot at 0x7fc8e0633f98>
../../_images/series_plot_basic.png

On a DataFrame, the plot() method is a convenience to plot all of the columns with labels:

In [70]: df = md.DataFrame(mt.random.randn(1000, 4), index=ts.index,
   ....:                   columns=['A', 'B', 'C', 'D'])
   ....: 

In [71]: df = df.cumsum()

In [72]: plt.figure()
Out[72]: <Figure size 640x480 with 0 Axes>

In [73]: df.plot()
Out[73]: <matplotlib.axes._subplots.AxesSubplot at 0x7fc8e011e128>

In [74]: plt.legend(loc='best')
Out[74]: <matplotlib.legend.Legend at 0x7fc8e00db320>
../../_images/frame_plot_basic.png

Getting data in/out

CSV

In [75]: df.to_csv('foo.csv').execute()
Out[75]: 
Empty DataFrame
Columns: []
Index: []

Reading from a csv file.

In [76]: md.read_csv('foo.csv').execute()
Out[76]: 
     Unnamed: 0          A          B         C          D
0    2000-01-01   0.020635  -1.829055  0.804398  -0.362481
1    2000-01-02   0.119671  -3.021767  0.853802  -1.621981
2    2000-01-03  -1.828637  -2.215143 -0.609730  -0.799588
3    2000-01-04  -2.382725  -2.952165 -0.966116  -1.064462
4    2000-01-05  -3.046591  -2.646843 -1.700743  -1.218309
..          ...        ...        ...       ...        ...
995  2002-09-22  51.697014  12.498474 -0.093453  23.503771
996  2002-09-23  52.396735  12.992341 -1.932032  23.240207
997  2002-09-24  51.236871  14.046080 -1.572348  24.537271
998  2002-09-25  50.581787  13.398644 -2.347733  24.002623
999  2002-09-26  50.267412  12.670912 -2.124686  23.756498

[1000 rows x 5 columns]