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 0x7fc32da65710>

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 -1.154713  1.126999 -0.121116  0.008886
2013-01-02  2.004474  0.625649 -0.072003 -0.244366
2013-01-03 -0.412276 -0.360237  0.186695  0.554216
2013-01-04 -2.901208  0.215376 -1.698960  1.459582
2013-01-05  0.738152  0.339431  1.192081  0.970703
2013-01-06 -0.853461 -0.799830  0.320724 -0.995249

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 -1.154713  1.126999 -0.121116  0.008886
2013-01-02  2.004474  0.625649 -0.072003 -0.244366
2013-01-03 -0.412276 -0.360237  0.186695  0.554216
2013-01-04 -2.901208  0.215376 -1.698960  1.459582
2013-01-05  0.738152  0.339431  1.192081  0.970703

In [15]: df.tail(3).execute()
Out[15]: 
                   A         B         C         D
2013-01-04 -2.901208  0.215376 -1.698960  1.459582
2013-01-05  0.738152  0.339431  1.192081  0.970703
2013-01-06 -0.853461 -0.799830  0.320724 -0.995249

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([[-1.15471313,  1.12699877, -0.12111647,  0.00888647],
       [ 2.00447359,  0.6256494 , -0.07200269, -0.24436604],
       [-0.41227607, -0.36023747,  0.18669517,  0.55421638],
       [-2.90120832,  0.21537632, -1.69896028,  1.45958154],
       [ 0.73815229,  0.33943131,  1.19208077,  0.97070281],
       [-0.85346062, -0.79983024,  0.32072424, -0.99524853]])

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  -0.429839  0.191231 -0.032097  0.292295
std    1.679461  0.688948  0.944343  0.885154
min   -2.901208 -0.799830 -1.698960 -0.995249
25%   -1.079400 -0.216334 -0.108838 -0.181053
50%   -0.632868  0.277404  0.057346  0.281551
75%    0.450545  0.554095  0.287217  0.866581
max    2.004474  1.126999  1.192081  1.459582

Sorting by an axis:

In [21]: df.sort_index(axis=1, ascending=False).execute()
Out[21]: 
                   D         C         B         A
2013-01-01  0.008886 -0.121116  1.126999 -1.154713
2013-01-02 -0.244366 -0.072003  0.625649  2.004474
2013-01-03  0.554216  0.186695 -0.360237 -0.412276
2013-01-04  1.459582 -1.698960  0.215376 -2.901208
2013-01-05  0.970703  1.192081  0.339431  0.738152
2013-01-06 -0.995249  0.320724 -0.799830 -0.853461

Sorting by values:

In [22]: df.sort_values(by='B').execute()
Out[22]: 
                   A         B         C         D
2013-01-06 -0.853461 -0.799830  0.320724 -0.995249
2013-01-03 -0.412276 -0.360237  0.186695  0.554216
2013-01-04 -2.901208  0.215376 -1.698960  1.459582
2013-01-05  0.738152  0.339431  1.192081  0.970703
2013-01-02  2.004474  0.625649 -0.072003 -0.244366
2013-01-01 -1.154713  1.126999 -0.121116  0.008886

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   -1.154713
2013-01-02    2.004474
2013-01-03   -0.412276
2013-01-04   -2.901208
2013-01-05    0.738152
2013-01-06   -0.853461
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 -1.154713  1.126999 -0.121116  0.008886
2013-01-02  2.004474  0.625649 -0.072003 -0.244366
2013-01-03 -0.412276 -0.360237  0.186695  0.554216

In [25]: df['20130102':'20130104'].execute()
Out[25]: 
                   A         B         C         D
2013-01-02  2.004474  0.625649 -0.072003 -0.244366
2013-01-03 -0.412276 -0.360237  0.186695  0.554216
2013-01-04 -2.901208  0.215376 -1.698960  1.459582

Selection by label#

For getting a cross section using a label:

In [26]: df.loc['20130101'].execute()
Out[26]: 
A   -1.154713
B    1.126999
C   -0.121116
D    0.008886
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 -1.154713  1.126999
2013-01-02  2.004474  0.625649
2013-01-03 -0.412276 -0.360237
2013-01-04 -2.901208  0.215376
2013-01-05  0.738152  0.339431
2013-01-06 -0.853461 -0.799830

Showing label slicing, both endpoints are included:

In [28]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[28]: 
                   A         B
2013-01-02  2.004474  0.625649
2013-01-03 -0.412276 -0.360237
2013-01-04 -2.901208  0.215376

Reduction in the dimensions of the returned object:

In [29]: df.loc['20130102', ['A', 'B']].execute()
Out[29]: 
A    2.004474
B    0.625649
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [30]: df.loc['20130101', 'A'].execute()
Out[30]: -1.1547131314038468

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

In [31]: df.at['20130101', 'A'].execute()
Out[31]: -1.1547131314038468

Selection by position#

Select via the position of the passed integers:

In [32]: df.iloc[3].execute()
Out[32]: 
A   -2.901208
B    0.215376
C   -1.698960
D    1.459582
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 -2.901208  0.215376
2013-01-05  0.738152  0.339431

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  2.004474 -0.072003
2013-01-03 -0.412276  0.186695
2013-01-05  0.738152  1.192081

For slicing rows explicitly:

In [35]: df.iloc[1:3, :].execute()
Out[35]: 
                   A         B         C         D
2013-01-02  2.004474  0.625649 -0.072003 -0.244366
2013-01-03 -0.412276 -0.360237  0.186695  0.554216

For slicing columns explicitly:

In [36]: df.iloc[:, 1:3].execute()
Out[36]: 
                   B         C
2013-01-01  1.126999 -0.121116
2013-01-02  0.625649 -0.072003
2013-01-03 -0.360237  0.186695
2013-01-04  0.215376 -1.698960
2013-01-05  0.339431  1.192081
2013-01-06 -0.799830  0.320724

For getting a value explicitly:

In [37]: df.iloc[1, 1].execute()
Out[37]: 0.6256494008687327

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

In [38]: df.iat[1, 1].execute()
Out[38]: 0.6256494008687327

Boolean indexing#

Using a single column’s values to select data.

In [39]: df[df['A'] > 0].execute()
Out[39]: 
                   A         B         C         D
2013-01-02  2.004474  0.625649 -0.072003 -0.244366
2013-01-05  0.738152  0.339431  1.192081  0.970703

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  1.126999       NaN  0.008886
2013-01-02  2.004474  0.625649       NaN       NaN
2013-01-03       NaN       NaN  0.186695  0.554216
2013-01-04       NaN  0.215376       NaN  1.459582
2013-01-05  0.738152  0.339431  1.192081  0.970703
2013-01-06       NaN       NaN  0.320724       NaN

Operations#

Stats#

Operations in general exclude missing data.

Performing a descriptive statistic:

In [41]: df.mean().execute()
Out[41]: 
A   -0.429839
B    0.191231
C   -0.032097
D    0.292295
dtype: float64

Same operation on the other axis:

In [42]: df.mean(1).execute()
Out[42]: 
2013-01-01   -0.034986
2013-01-02    0.578439
2013-01-03   -0.007900
2013-01-04   -0.731303
2013-01-05    0.810092
2013-01-06   -0.581954
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 -1.412276 -1.360237 -0.813305 -0.445784
2013-01-04 -5.901208 -2.784624 -4.698960 -1.540418
2013-01-05 -4.261848 -4.660569 -3.807919 -4.029297
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    4.905682
B    1.926829
C    2.891041
D    2.454830
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 -0.953826  1.545058  0.175901  0.262698
1  0.715620  1.370578 -0.737252 -1.272021
2  0.536494  1.190122  1.342157 -0.626725
3  0.640357 -0.984611 -0.717763 -1.598003
4  0.930090  0.989741 -1.535904  1.381829
5 -0.229707  1.417013 -0.236877  0.344851
6  0.325114 -0.368508  1.013749  0.069613
7  2.055862 -1.085238  0.276277 -0.081811
8 -1.876217 -1.058380 -0.409820 -1.029904
9  0.386319 -1.906937  0.075222  0.120449

# 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 -0.953826  1.545058  0.175901  0.262698
1  0.715620  1.370578 -0.737252 -1.272021
2  0.536494  1.190122  1.342157 -0.626725
3  0.640357 -0.984611 -0.717763 -1.598003
4  0.930090  0.989741 -1.535904  1.381829
5 -0.229707  1.417013 -0.236877  0.344851
6  0.325114 -0.368508  1.013749  0.069613
7  2.055862 -1.085238  0.276277 -0.081811
8 -1.876217 -1.058380 -0.409820 -1.029904
9  0.386319 -1.906937  0.075222  0.120449

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 -0.565032 -1.607176
1  bar    one  1.308391  0.990335
2  foo    two -1.252675  0.415938
3  bar  three -1.685600 -1.520058
4  foo    two  1.587265 -1.453618
5  bar    two -1.505516  2.071624
6  foo    one -0.258125  1.156111
7  foo  three -0.572976 -0.295718

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

In [65]: df.groupby('A').sum().execute()
Out[65]: 
            C         D
A                      
bar -1.882725  1.541902
foo -1.061543 -1.784464

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    1.308391  0.990335
    three -1.685600 -1.520058
    two   -1.505516  2.071624
foo one   -0.823157 -0.451065
    three -0.572976 -0.295718
    two    0.334590 -1.037681

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 0x7fc331c70090>
../../_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  -0.446503  -1.246108   2.302081  0.185348
1    2000-01-02  -1.426874  -2.512547   3.064252  0.129371
2    2000-01-03  -0.899882  -2.238935   1.691353  0.952471
3    2000-01-04   0.296918  -2.522751   1.665118  1.918964
4    2000-01-05   0.748298  -2.625772   1.079004  0.972642
..          ...        ...        ...        ...       ...
995  2002-09-22  36.721154  15.031687  25.888191  4.402686
996  2002-09-23  35.773785  15.712163  26.392070  4.865123
997  2002-09-24  36.220666  15.497736  28.012235  6.078988
998  2002-09-25  35.427897  14.468245  27.108731  6.307152
999  2002-09-26  36.541143  13.555077  27.968725  5.945080

[1000 rows x 5 columns]