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 0x7f77e1c21490>

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  1.076422 -0.063295  1.290941 -1.218255
2013-01-02 -1.028042  0.161895  0.046537  0.780091
2013-01-03  0.272705  0.461037  0.175947  1.614700
2013-01-04  0.356858 -1.031220 -1.168594  0.048108
2013-01-05 -0.176852  0.444718  1.477015 -1.103744
2013-01-06  0.496114  0.753061  2.028959  0.887982

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  1.076422 -0.063295  1.290941 -1.218255
2013-01-02 -1.028042  0.161895  0.046537  0.780091
2013-01-03  0.272705  0.461037  0.175947  1.614700
2013-01-04  0.356858 -1.031220 -1.168594  0.048108
2013-01-05 -0.176852  0.444718  1.477015 -1.103744

In [15]: df.tail(3).execute()
Out[15]: 
                   A         B         C         D
2013-01-04  0.356858 -1.031220 -1.168594  0.048108
2013-01-05 -0.176852  0.444718  1.477015 -1.103744
2013-01-06  0.496114  0.753061  2.028959  0.887982

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([[ 1.07642204, -0.06329531,  1.29094118, -1.21825506],
       [-1.02804208,  0.16189489,  0.04653676,  0.7800906 ],
       [ 0.27270535,  0.4610367 ,  0.17594678,  1.61469985],
       [ 0.35685839, -1.03122042, -1.16859393,  0.04810752],
       [-0.176852  ,  0.44471826,  1.47701543, -1.10374372],
       [ 0.49611353,  0.75306102,  2.02895894,  0.88798153]])

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   0.166201  0.121033  0.641801  0.168147
std    0.711188  0.629789  1.174058  1.143624
min   -1.028042 -1.031220 -1.168594 -1.218255
25%   -0.064463 -0.006998  0.078889 -0.815781
50%    0.314782  0.303307  0.733444  0.414099
75%    0.461300  0.456957  1.430497  0.861009
max    1.076422  0.753061  2.028959  1.614700

Sorting by an axis:

In [21]: df.sort_index(axis=1, ascending=False).execute()
Out[21]: 
                   D         C         B         A
2013-01-01 -1.218255  1.290941 -0.063295  1.076422
2013-01-02  0.780091  0.046537  0.161895 -1.028042
2013-01-03  1.614700  0.175947  0.461037  0.272705
2013-01-04  0.048108 -1.168594 -1.031220  0.356858
2013-01-05 -1.103744  1.477015  0.444718 -0.176852
2013-01-06  0.887982  2.028959  0.753061  0.496114

Sorting by values:

In [22]: df.sort_values(by='B').execute()
Out[22]: 
                   A         B         C         D
2013-01-04  0.356858 -1.031220 -1.168594  0.048108
2013-01-01  1.076422 -0.063295  1.290941 -1.218255
2013-01-02 -1.028042  0.161895  0.046537  0.780091
2013-01-05 -0.176852  0.444718  1.477015 -1.103744
2013-01-03  0.272705  0.461037  0.175947  1.614700
2013-01-06  0.496114  0.753061  2.028959  0.887982

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    1.076422
2013-01-02   -1.028042
2013-01-03    0.272705
2013-01-04    0.356858
2013-01-05   -0.176852
2013-01-06    0.496114
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  1.076422 -0.063295  1.290941 -1.218255
2013-01-02 -1.028042  0.161895  0.046537  0.780091
2013-01-03  0.272705  0.461037  0.175947  1.614700

In [25]: df['20130102':'20130104'].execute()
Out[25]: 
                   A         B         C         D
2013-01-02 -1.028042  0.161895  0.046537  0.780091
2013-01-03  0.272705  0.461037  0.175947  1.614700
2013-01-04  0.356858 -1.031220 -1.168594  0.048108

Selection by label

For getting a cross section using a label:

In [26]: df.loc['20130101'].execute()
Out[26]: 
A    1.076422
B   -0.063295
C    1.290941
D   -1.218255
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  1.076422 -0.063295
2013-01-02 -1.028042  0.161895
2013-01-03  0.272705  0.461037
2013-01-04  0.356858 -1.031220
2013-01-05 -0.176852  0.444718
2013-01-06  0.496114  0.753061

Showing label slicing, both endpoints are included:

In [28]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[28]: 
                   A         B
2013-01-02 -1.028042  0.161895
2013-01-03  0.272705  0.461037
2013-01-04  0.356858 -1.031220

Reduction in the dimensions of the returned object:

In [29]: df.loc['20130102', ['A', 'B']].execute()
Out[29]: 
A   -1.028042
B    0.161895
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [30]: df.loc['20130101', 'A'].execute()
Out[30]: 1.076422039673007

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

In [31]: df.at['20130101', 'A'].execute()
Out[31]: 1.076422039673007

Selection by position

Select via the position of the passed integers:

In [32]: df.iloc[3].execute()
Out[32]: 
A    0.356858
B   -1.031220
C   -1.168594
D    0.048108
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  0.356858 -1.031220
2013-01-05 -0.176852  0.444718

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 -1.028042  0.046537
2013-01-03  0.272705  0.175947
2013-01-05 -0.176852  1.477015

For slicing rows explicitly:

In [35]: df.iloc[1:3, :].execute()
Out[35]: 
                   A         B         C         D
2013-01-02 -1.028042  0.161895  0.046537  0.780091
2013-01-03  0.272705  0.461037  0.175947  1.614700

For slicing columns explicitly:

In [36]: df.iloc[:, 1:3].execute()
Out[36]: 
                   B         C
2013-01-01 -0.063295  1.290941
2013-01-02  0.161895  0.046537
2013-01-03  0.461037  0.175947
2013-01-04 -1.031220 -1.168594
2013-01-05  0.444718  1.477015
2013-01-06  0.753061  2.028959

For getting a value explicitly:

In [37]: df.iloc[1, 1].execute()
Out[37]: 0.1618948946987995

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

In [38]: df.iat[1, 1].execute()
Out[38]: 0.1618948946987995

Boolean indexing

Using a single column’s values to select data.

In [39]: df[df['A'] > 0].execute()
Out[39]: 
                   A         B         C         D
2013-01-01  1.076422 -0.063295  1.290941 -1.218255
2013-01-03  0.272705  0.461037  0.175947  1.614700
2013-01-04  0.356858 -1.031220 -1.168594  0.048108
2013-01-06  0.496114  0.753061  2.028959  0.887982

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  1.076422       NaN  1.290941       NaN
2013-01-02       NaN  0.161895  0.046537  0.780091
2013-01-03  0.272705  0.461037  0.175947  1.614700
2013-01-04  0.356858       NaN       NaN  0.048108
2013-01-05       NaN  0.444718  1.477015       NaN
2013-01-06  0.496114  0.753061  2.028959  0.887982

Operations

Stats

Operations in general exclude missing data.

Performing a descriptive statistic:

In [41]: df.mean().execute()
Out[41]: 
A    0.166201
B    0.121033
C    0.641801
D    0.168147
dtype: float64

Same operation on the other axis:

In [42]: df.mean(1).execute()
Out[42]: 
2013-01-01    0.271453
2013-01-02   -0.009880
2013-01-03    0.631097
2013-01-04   -0.448712
2013-01-05    0.160284
2013-01-06    1.041529
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 -0.727295 -0.538963 -0.824053  0.614700
2013-01-04 -2.643142 -4.031220 -4.168594 -2.951892
2013-01-05 -5.176852 -4.555282 -3.522985 -6.103744
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    2.104464
B    1.784281
C    3.197553
D    2.832955
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  0.703009  0.629147  0.070154 -0.599728
1  1.509314  2.037364  1.275207  0.354816
2 -1.212487  0.181120 -0.933633  0.356520
3 -0.097401  0.066019  0.614016 -0.340231
4 -0.772586 -0.516224  0.451034 -0.443006
5  1.568485 -1.888047 -1.663243 -0.312146
6  0.471689 -0.681341 -1.698202  0.046998
7  0.045164  0.810413 -0.077242 -1.148577
8  0.160981 -0.015128  0.374324  0.959274
9  1.462799  0.334684  1.225499  0.793457

# 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  0.703009  0.629147  0.070154 -0.599728
1  1.509314  2.037364  1.275207  0.354816
2 -1.212487  0.181120 -0.933633  0.356520
3 -0.097401  0.066019  0.614016 -0.340231
4 -0.772586 -0.516224  0.451034 -0.443006
5  1.568485 -1.888047 -1.663243 -0.312146
6  0.471689 -0.681341 -1.698202  0.046998
7  0.045164  0.810413 -0.077242 -1.148577
8  0.160981 -0.015128  0.374324  0.959274
9  1.462799  0.334684  1.225499  0.793457

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  0.185293 -2.357280
1  bar    one -1.151557 -1.608468
2  foo    two  0.746421 -0.722193
3  bar  three  0.054988 -0.832692
4  foo    two -1.388687 -2.106404
5  bar    two -0.443227 -0.265841
6  foo    one -0.450409 -0.808825
7  foo  three -0.584444 -1.292391

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

In [65]: df.groupby('A').sum().execute()
Out[65]: 
            C         D
A                      
bar -1.539796 -2.707001
foo -1.491827 -7.287094

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   -1.151557 -1.608468
    three  0.054988 -0.832692
    two   -0.443227 -0.265841
foo one   -0.265116 -3.166106
    three -0.584444 -1.292391
    two   -0.642266 -2.828597

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 0x7f77e5b09f90>
../../_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   0.304951   3.012175   1.136871  -0.181248
1    2000-01-02   0.359162   3.949068  -0.551410  -0.608089
2    2000-01-03   1.723525   3.775545  -0.637127  -1.206238
3    2000-01-04   1.702542   5.507166  -0.662413  -1.277900
4    2000-01-05   1.779222   6.277748   0.667923   0.101587
..          ...        ...        ...        ...        ...
995  2002-09-22 -20.010932 -50.015539 -59.465757 -53.747694
996  2002-09-23 -22.960616 -50.153695 -59.900121 -53.072363
997  2002-09-24 -23.118659 -49.747630 -60.751085 -55.023352
998  2002-09-25 -22.524879 -48.875720 -62.158760 -55.518531
999  2002-09-26 -20.767534 -47.089365 -60.293954 -55.280246

[1000 rows x 5 columns]