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  1.473387 -0.438770  0.008015 -1.560330
2013-01-02 -1.400198 -0.100956 -2.200239  1.202693
2013-01-03  0.789467 -0.060208  0.994953  0.946702
2013-01-04 -0.120857  1.560768 -0.145777  1.279450
2013-01-05  0.461089 -0.826550  0.217422 -1.084699
2013-01-06  0.247145 -0.594301  0.305897  0.598113

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  1.473387 -0.438770  0.008015 -1.560330
2013-01-02 -1.400198 -0.100956 -2.200239  1.202693
2013-01-03  0.789467 -0.060208  0.994953  0.946702
2013-01-04 -0.120857  1.560768 -0.145777  1.279450
2013-01-05  0.461089 -0.826550  0.217422 -1.084699

In [13]: df.tail(3).execute()
Out[13]: 
                   A         B         C         D
2013-01-04 -0.120857  1.560768 -0.145777  1.279450
2013-01-05  0.461089 -0.826550  0.217422 -1.084699
2013-01-06  0.247145 -0.594301  0.305897  0.598113

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([[ 1.47338686, -0.43877022,  0.00801519, -1.56032973],
       [-1.40019783, -0.10095603, -2.20023857,  1.20269315],
       [ 0.78946704, -0.06020817,  0.99495349,  0.94670243],
       [-0.12085656,  1.56076755, -0.14577734,  1.27945001],
       [ 0.46108875, -0.8265497 ,  0.21742227, -1.08469897],
       [ 0.2471454 , -0.59430103,  0.30589662,  0.59811253]])

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   0.241672 -0.076670 -0.136621  0.230322
std    0.968766  0.853632  1.084541  1.235343
min   -1.400198 -0.826550 -2.200239 -1.560330
25%   -0.028856 -0.555418 -0.107329 -0.663996
50%    0.354117 -0.269863  0.112719  0.772407
75%    0.707372 -0.070395  0.283778  1.138695
max    1.473387  1.560768  0.994953  1.279450

Sorting by an axis:

In [19]: df.sort_index(axis=1, ascending=False).execute()
Out[19]: 
                   D         C         B         A
2013-01-01 -1.560330  0.008015 -0.438770  1.473387
2013-01-02  1.202693 -2.200239 -0.100956 -1.400198
2013-01-03  0.946702  0.994953 -0.060208  0.789467
2013-01-04  1.279450 -0.145777  1.560768 -0.120857
2013-01-05 -1.084699  0.217422 -0.826550  0.461089
2013-01-06  0.598113  0.305897 -0.594301  0.247145

Sorting by values:

In [20]: df.sort_values(by='B').execute()
Out[20]: 
                   A         B         C         D
2013-01-05  0.461089 -0.826550  0.217422 -1.084699
2013-01-06  0.247145 -0.594301  0.305897  0.598113
2013-01-01  1.473387 -0.438770  0.008015 -1.560330
2013-01-02 -1.400198 -0.100956 -2.200239  1.202693
2013-01-03  0.789467 -0.060208  0.994953  0.946702
2013-01-04 -0.120857  1.560768 -0.145777  1.279450

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    1.473387
2013-01-02   -1.400198
2013-01-03    0.789467
2013-01-04   -0.120857
2013-01-05    0.461089
2013-01-06    0.247145
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  1.473387 -0.438770  0.008015 -1.560330
2013-01-02 -1.400198 -0.100956 -2.200239  1.202693
2013-01-03  0.789467 -0.060208  0.994953  0.946702

In [23]: df['20130102':'20130104'].execute()
Out[23]: 
                   A         B         C         D
2013-01-02 -1.400198 -0.100956 -2.200239  1.202693
2013-01-03  0.789467 -0.060208  0.994953  0.946702
2013-01-04 -0.120857  1.560768 -0.145777  1.279450

Selection by label

For getting a cross section using a label:

In [24]: df.loc['20130101'].execute()
Out[24]: 
A    1.473387
B   -0.438770
C    0.008015
D   -1.560330
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  1.473387 -0.438770
2013-01-02 -1.400198 -0.100956
2013-01-03  0.789467 -0.060208
2013-01-04 -0.120857  1.560768
2013-01-05  0.461089 -0.826550
2013-01-06  0.247145 -0.594301

Showing label slicing, both endpoints are included:

In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[26]: 
                   A         B
2013-01-02 -1.400198 -0.100956
2013-01-03  0.789467 -0.060208
2013-01-04 -0.120857  1.560768

Reduction in the dimensions of the returned object:

In [27]: df.loc['20130102', ['A', 'B']].execute()
Out[27]: 
A   -1.400198
B   -0.100956
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [28]: df.loc['20130101', 'A'].execute()
Out[28]: 1.4733868640543755

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

In [29]: df.at['20130101', 'A'].execute()
Out[29]: 1.4733868640543755

Selection by position

Select via the position of the passed integers:

In [30]: df.iloc[3].execute()
Out[30]: 
A   -0.120857
B    1.560768
C   -0.145777
D    1.279450
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 -0.120857  1.560768
2013-01-05  0.461089 -0.826550

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 -1.400198 -2.200239
2013-01-03  0.789467  0.994953
2013-01-05  0.461089  0.217422

For slicing rows explicitly:

In [33]: df.iloc[1:3, :].execute()
Out[33]: 
                   A         B         C         D
2013-01-02 -1.400198 -0.100956 -2.200239  1.202693
2013-01-03  0.789467 -0.060208  0.994953  0.946702

For slicing columns explicitly:

In [34]: df.iloc[:, 1:3].execute()
Out[34]: 
                   B         C
2013-01-01 -0.438770  0.008015
2013-01-02 -0.100956 -2.200239
2013-01-03 -0.060208  0.994953
2013-01-04  1.560768 -0.145777
2013-01-05 -0.826550  0.217422
2013-01-06 -0.594301  0.305897

For getting a value explicitly:

In [35]: df.iloc[1, 1].execute()
Out[35]: -0.10095603373532115

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

In [36]: df.iat[1, 1].execute()
Out[36]: -0.10095603373532115

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  1.473387 -0.438770  0.008015 -1.560330
2013-01-03  0.789467 -0.060208  0.994953  0.946702
2013-01-05  0.461089 -0.826550  0.217422 -1.084699
2013-01-06  0.247145 -0.594301  0.305897  0.598113

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  1.473387       NaN  0.008015       NaN
2013-01-02       NaN       NaN       NaN  1.202693
2013-01-03  0.789467       NaN  0.994953  0.946702
2013-01-04       NaN  1.560768       NaN  1.279450
2013-01-05  0.461089       NaN  0.217422       NaN
2013-01-06  0.247145       NaN  0.305897  0.598113

Operations

Stats

Operations in general exclude missing data.

Performing a descriptive statistic:

In [39]: df.mean().execute()
Out[39]: 
A    0.241672
B   -0.076670
C   -0.136621
D    0.230322
dtype: float64

Same operation on the other axis:

In [40]: df.mean(1).execute()
Out[40]: 
2013-01-01   -0.129424
2013-01-02   -0.624675
2013-01-03    0.667729
2013-01-04    0.643396
2013-01-05   -0.308184
2013-01-06    0.139213
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 -0.210533 -1.060208 -0.005047 -0.053298
2013-01-04 -3.120857 -1.439232 -3.145777 -1.720550
2013-01-05 -4.538911 -5.826550 -4.782578 -6.084699
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    2.873585
B    2.387317
C    3.195192
D    2.839780
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 -0.492399  1.591341  0.489138 -0.537219
1 -0.462851  0.249419  0.819305  0.386692
2  0.771851  1.309203  0.531806  0.680079
3 -0.152101 -0.680273  0.768000 -1.773337
4 -0.267961 -0.705051 -0.905153 -1.048523
5 -3.009908 -0.360819  0.780722  0.298824
6 -0.284410 -1.549198 -1.114532  1.460305
7  1.218042  0.988848  1.730204 -0.059872
8 -0.774641  0.277757  0.258700  0.012099
9  0.599905  0.292947 -1.276687 -1.157529

# 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 -0.492399  1.591341  0.489138 -0.537219
1 -0.462851  0.249419  0.819305  0.386692
2  0.771851  1.309203  0.531806  0.680079
3 -0.152101 -0.680273  0.768000 -1.773337
4 -0.267961 -0.705051 -0.905153 -1.048523
5 -3.009908 -0.360819  0.780722  0.298824
6 -0.284410 -1.549198 -1.114532  1.460305
7  1.218042  0.988848  1.730204 -0.059872
8 -0.774641  0.277757  0.258700  0.012099
9  0.599905  0.292947 -1.276687 -1.157529

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  0.942136 -1.799919
1  bar    one -1.865205 -0.540639
2  foo    two  0.259002  0.395910
3  bar  three -0.805246 -0.486076
4  foo    two -0.757582  0.201025
5  bar    two -0.192773 -0.565687
6  foo    one  0.370796 -0.024036
7  foo  three  0.208165  1.175249

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

In [63]: df.groupby('A').sum().execute()
Out[63]: 
            C         D
A                      
bar -2.863224 -1.592401
foo  1.022517 -0.051772

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    1.312931 -1.823954
    two   -0.498580  0.596934
    three  0.208165  1.175249
bar one   -1.865205 -0.540639
    two   -0.192773 -0.565687
    three -0.805246 -0.486076

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 0x7f9cec818e90>
../../_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  -0.125596   0.931804  -0.385178  -0.637361
1    2000-01-02  -2.439752   1.547093  -1.532035  -2.009570
2    2000-01-03  -3.570241   0.719254  -0.619744  -2.185976
3    2000-01-04  -3.122562  -0.140634  -0.817207  -2.412433
4    2000-01-05  -3.606291   1.129713   1.369832  -2.316546
..          ...        ...        ...        ...        ...
995  2002-09-22 -23.801909 -29.921077 -10.620415 -23.353655
996  2002-09-23 -22.906084 -30.210952  -9.438820 -22.831602
997  2002-09-24 -23.541994 -32.636398  -9.753185 -23.125812
998  2002-09-25 -23.375654 -32.101282 -11.378576 -22.697645
999  2002-09-26 -24.444503 -31.908815 -12.003691 -22.137871

[1000 rows x 5 columns]