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  0.996172  0.297444 -0.025738 -0.707780
2013-01-02 -0.555496 -0.354014 -0.745561  0.865680
2013-01-03 -0.133414 -1.006552  0.902451  1.514278
2013-01-04 -0.513947  0.558884 -1.549307 -0.590955
2013-01-05 -0.469396  0.269355  2.267890  0.660135
2013-01-06 -1.645099 -1.831922 -0.549775 -0.474557

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  0.996172  0.297444 -0.025738 -0.707780
2013-01-02 -0.555496 -0.354014 -0.745561  0.865680
2013-01-03 -0.133414 -1.006552  0.902451  1.514278
2013-01-04 -0.513947  0.558884 -1.549307 -0.590955
2013-01-05 -0.469396  0.269355  2.267890  0.660135

In [13]: df.tail(3).execute()
Out[13]: 
                   A         B         C         D
2013-01-04 -0.513947  0.558884 -1.549307 -0.590955
2013-01-05 -0.469396  0.269355  2.267890  0.660135
2013-01-06 -1.645099 -1.831922 -0.549775 -0.474557

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([[ 0.99617173,  0.29744392, -0.02573815, -0.70777961],
       [-0.55549562, -0.35401445, -0.74556121,  0.86567991],
       [-0.13341362, -1.0065516 ,  0.90245066,  1.51427841],
       [-0.51394657,  0.55888406, -1.54930722, -0.59095507],
       [-0.46939609,  0.2693546 ,  2.26789014,  0.66013511],
       [-1.64509948, -1.83192221, -0.54977473, -0.47455679]])

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  -0.386863 -0.344468  0.049993  0.211134
std    0.849892  0.922588  1.356604  0.925866
min   -1.645099 -1.831922 -1.549307 -0.707780
25%   -0.545108 -0.843417 -0.696615 -0.561856
50%   -0.491671 -0.042330 -0.287756  0.092789
75%   -0.217409  0.290422  0.670403  0.814294
max    0.996172  0.558884  2.267890  1.514278

Sorting by an axis:

In [19]: df.sort_index(axis=1, ascending=False).execute()
Out[19]: 
                   D         C         B         A
2013-01-01 -0.707780 -0.025738  0.297444  0.996172
2013-01-02  0.865680 -0.745561 -0.354014 -0.555496
2013-01-03  1.514278  0.902451 -1.006552 -0.133414
2013-01-04 -0.590955 -1.549307  0.558884 -0.513947
2013-01-05  0.660135  2.267890  0.269355 -0.469396
2013-01-06 -0.474557 -0.549775 -1.831922 -1.645099

Sorting by values:

In [20]: df.sort_values(by='B').execute()
Out[20]: 
                   A         B         C         D
2013-01-06 -1.645099 -1.831922 -0.549775 -0.474557
2013-01-03 -0.133414 -1.006552  0.902451  1.514278
2013-01-02 -0.555496 -0.354014 -0.745561  0.865680
2013-01-05 -0.469396  0.269355  2.267890  0.660135
2013-01-01  0.996172  0.297444 -0.025738 -0.707780
2013-01-04 -0.513947  0.558884 -1.549307 -0.590955

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    0.996172
2013-01-02   -0.555496
2013-01-03   -0.133414
2013-01-04   -0.513947
2013-01-05   -0.469396
2013-01-06   -1.645099
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  0.996172  0.297444 -0.025738 -0.707780
2013-01-02 -0.555496 -0.354014 -0.745561  0.865680
2013-01-03 -0.133414 -1.006552  0.902451  1.514278

In [23]: df['20130102':'20130104'].execute()
Out[23]: 
                   A         B         C         D
2013-01-02 -0.555496 -0.354014 -0.745561  0.865680
2013-01-03 -0.133414 -1.006552  0.902451  1.514278
2013-01-04 -0.513947  0.558884 -1.549307 -0.590955

Selection by label

For getting a cross section using a label:

In [24]: df.loc['20130101'].execute()
Out[24]: 
A    0.996172
B    0.297444
C   -0.025738
D   -0.707780
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  0.996172  0.297444
2013-01-02 -0.555496 -0.354014
2013-01-03 -0.133414 -1.006552
2013-01-04 -0.513947  0.558884
2013-01-05 -0.469396  0.269355
2013-01-06 -1.645099 -1.831922

Showing label slicing, both endpoints are included:

In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[26]: 
                   A         B
2013-01-02 -0.555496 -0.354014
2013-01-03 -0.133414 -1.006552
2013-01-04 -0.513947  0.558884

Reduction in the dimensions of the returned object:

In [27]: df.loc['20130102', ['A', 'B']].execute()
Out[27]: 
A   -0.555496
B   -0.354014
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [28]: df.loc['20130101', 'A'].execute()
Out[28]: 0.9961717326498453

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

In [29]: df.at['20130101', 'A'].execute()
Out[29]: 0.9961717326498453

Selection by position

Select via the position of the passed integers:

In [30]: df.iloc[3].execute()
Out[30]: 
A   -0.513947
B    0.558884
C   -1.549307
D   -0.590955
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 -0.513947  0.558884
2013-01-05 -0.469396  0.269355

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 -0.555496 -0.745561
2013-01-03 -0.133414  0.902451
2013-01-05 -0.469396  2.267890

For slicing rows explicitly:

In [33]: df.iloc[1:3, :].execute()
Out[33]: 
                   A         B         C         D
2013-01-02 -0.555496 -0.354014 -0.745561  0.865680
2013-01-03 -0.133414 -1.006552  0.902451  1.514278

For slicing columns explicitly:

In [34]: df.iloc[:, 1:3].execute()
Out[34]: 
                   B         C
2013-01-01  0.297444 -0.025738
2013-01-02 -0.354014 -0.745561
2013-01-03 -1.006552  0.902451
2013-01-04  0.558884 -1.549307
2013-01-05  0.269355  2.267890
2013-01-06 -1.831922 -0.549775

For getting a value explicitly:

In [35]: df.iloc[1, 1].execute()
Out[35]: -0.354014452391651

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

In [36]: df.iat[1, 1].execute()
Out[36]: -0.354014452391651

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  0.996172  0.297444 -0.025738 -0.70778

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  0.996172  0.297444       NaN       NaN
2013-01-02       NaN       NaN       NaN  0.865680
2013-01-03       NaN       NaN  0.902451  1.514278
2013-01-04       NaN  0.558884       NaN       NaN
2013-01-05       NaN  0.269355  2.267890  0.660135
2013-01-06       NaN       NaN       NaN       NaN

Operations

Stats

Operations in general exclude missing data.

Performing a descriptive statistic:

In [39]: df.mean().execute()
Out[39]: 
A   -0.386863
B   -0.344468
C    0.049993
D    0.211134
dtype: float64

Same operation on the other axis:

In [40]: df.mean(1).execute()
Out[40]: 
2013-01-01    0.140024
2013-01-02   -0.197348
2013-01-03    0.319191
2013-01-04   -0.523831
2013-01-05    0.681996
2013-01-06   -1.125338
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 -1.133414 -2.006552 -0.097549  0.514278
2013-01-04 -3.513947 -2.441116 -4.549307 -3.590955
2013-01-05 -5.469396 -4.730645 -2.732110 -4.339865
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    2.641271
B    2.390806
C    3.817197
D    2.222058
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  0.246649 -1.121654 -0.104328  0.469352
1 -0.458209 -1.367174 -0.499511 -0.424648
2  0.811784  0.539248  1.212989 -0.664290
3  0.784364  0.706993  0.607985 -0.030239
4 -0.270892 -0.440902 -1.959573 -0.267640
5 -0.420808  0.426049 -0.155517  0.677272
6  1.103542 -0.592715 -0.277834 -0.741513
7  0.807872 -0.432451  0.639677 -0.576744
8  1.270396  0.992311  0.388719  1.663674
9  1.017798  0.002464 -0.212461 -0.627949

# 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  0.246649 -1.121654 -0.104328  0.469352
1 -0.458209 -1.367174 -0.499511 -0.424648
2  0.811784  0.539248  1.212989 -0.664290
3  0.784364  0.706993  0.607985 -0.030239
4 -0.270892 -0.440902 -1.959573 -0.267640
5 -0.420808  0.426049 -0.155517  0.677272
6  1.103542 -0.592715 -0.277834 -0.741513
7  0.807872 -0.432451  0.639677 -0.576744
8  1.270396  0.992311  0.388719  1.663674
9  1.017798  0.002464 -0.212461 -0.627949

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  0.201133 -0.509286
1  bar    one -2.486497  0.819362
2  foo    two  1.592384 -0.388189
3  bar  three -0.417854  1.898835
4  foo    two  0.740800  1.337719
5  bar    two  1.446650 -1.069157
6  foo    one  0.221476  0.364358
7  foo  three -2.080049  0.660333

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

In [63]: df.groupby('A').sum().execute()
Out[63]: 
            C         D
A                      
bar -1.457701  1.649041
foo  0.675743  1.464934

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    0.422608 -0.144929
    two    2.333184  0.949530
    three -2.080049  0.660333
bar one   -2.486497  0.819362
    two    1.446650 -1.069157
    three -0.417854  1.898835

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]: <AxesSubplot:>
../../_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]: <AxesSubplot:>

In [74]: plt.legend(loc='best')
Out[74]: <matplotlib.legend.Legend at 0x7fdbe38ab610>
../../_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  -1.454651   0.225538   1.767234  -0.485952
1    2000-01-02  -0.546805   1.627389   0.665487  -1.909533
2    2000-01-03  -1.798785   1.505491   1.635632  -1.852234
3    2000-01-04  -1.562943   1.479468   0.569763  -2.766721
4    2000-01-05  -2.095900   1.169591   0.538246  -3.097193
..          ...        ...        ...        ...        ...
995  2002-09-22 -21.399585 -93.954730 -23.808535  19.700636
996  2002-09-23 -20.846456 -94.806360 -22.291834  21.326204
997  2002-09-24 -21.792252 -95.241904 -22.437903  20.599168
998  2002-09-25 -21.230538 -96.136098 -22.841493  19.696823
999  2002-09-26 -22.219553 -93.998680 -23.032008  19.539431

[1000 rows x 5 columns]