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 -0.924740  0.770940  0.848022  0.000714
2013-01-02 -0.115180 -1.186477  1.455773 -1.009835
2013-01-03  0.648884 -1.742358 -2.110255  0.699699
2013-01-04 -0.896207 -0.995048  0.680549 -1.133533
2013-01-05 -0.546100  0.971047 -0.431097 -0.960006
2013-01-06 -0.373037  1.217092 -1.576501 -1.003959

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 -0.924740  0.770940  0.848022  0.000714
2013-01-02 -0.115180 -1.186477  1.455773 -1.009835
2013-01-03  0.648884 -1.742358 -2.110255  0.699699
2013-01-04 -0.896207 -0.995048  0.680549 -1.133533
2013-01-05 -0.546100  0.971047 -0.431097 -0.960006

In [13]: df.tail(3).execute()
Out[13]: 
                   A         B         C         D
2013-01-04 -0.896207 -0.995048  0.680549 -1.133533
2013-01-05 -0.546100  0.971047 -0.431097 -0.960006
2013-01-06 -0.373037  1.217092 -1.576501 -1.003959

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([[-9.24739857e-01,  7.70939607e-01,  8.48021684e-01,
         7.13706225e-04],
       [-1.15179745e-01, -1.18647693e+00,  1.45577294e+00,
        -1.00983483e+00],
       [ 6.48883680e-01, -1.74235772e+00, -2.11025488e+00,
         6.99698627e-01],
       [-8.96206921e-01, -9.95047578e-01,  6.80548829e-01,
        -1.13353310e+00],
       [-5.46100357e-01,  9.71047404e-01, -4.31096994e-01,
        -9.60005508e-01],
       [-3.73036572e-01,  1.21709221e+00, -1.57650131e+00,
        -1.00395859e+00]])

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  -0.367730 -0.160801 -0.188918 -0.567820
std    0.586290  1.288186  1.429111  0.746896
min   -0.924740 -1.742358 -2.110255 -1.133533
25%   -0.808680 -1.138620 -1.290150 -1.008366
50%   -0.459568 -0.112054  0.124726 -0.981982
75%   -0.179644  0.921020  0.806153 -0.239466
max    0.648884  1.217092  1.455773  0.699699

Sorting by an axis:

In [19]: df.sort_index(axis=1, ascending=False).execute()
Out[19]: 
                   D         C         B         A
2013-01-01  0.000714  0.848022  0.770940 -0.924740
2013-01-02 -1.009835  1.455773 -1.186477 -0.115180
2013-01-03  0.699699 -2.110255 -1.742358  0.648884
2013-01-04 -1.133533  0.680549 -0.995048 -0.896207
2013-01-05 -0.960006 -0.431097  0.971047 -0.546100
2013-01-06 -1.003959 -1.576501  1.217092 -0.373037

Sorting by values:

In [20]: df.sort_values(by='B').execute()
Out[20]: 
                   A         B         C         D
2013-01-03  0.648884 -1.742358 -2.110255  0.699699
2013-01-02 -0.115180 -1.186477  1.455773 -1.009835
2013-01-04 -0.896207 -0.995048  0.680549 -1.133533
2013-01-01 -0.924740  0.770940  0.848022  0.000714
2013-01-05 -0.546100  0.971047 -0.431097 -0.960006
2013-01-06 -0.373037  1.217092 -1.576501 -1.003959

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   -0.924740
2013-01-02   -0.115180
2013-01-03    0.648884
2013-01-04   -0.896207
2013-01-05   -0.546100
2013-01-06   -0.373037
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 -0.924740  0.770940  0.848022  0.000714
2013-01-02 -0.115180 -1.186477  1.455773 -1.009835
2013-01-03  0.648884 -1.742358 -2.110255  0.699699

In [23]: df['20130102':'20130104'].execute()
Out[23]: 
                   A         B         C         D
2013-01-02 -0.115180 -1.186477  1.455773 -1.009835
2013-01-03  0.648884 -1.742358 -2.110255  0.699699
2013-01-04 -0.896207 -0.995048  0.680549 -1.133533

Selection by label

For getting a cross section using a label:

In [24]: df.loc['20130101'].execute()
Out[24]: 
A   -0.924740
B    0.770940
C    0.848022
D    0.000714
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 -0.924740  0.770940
2013-01-02 -0.115180 -1.186477
2013-01-03  0.648884 -1.742358
2013-01-04 -0.896207 -0.995048
2013-01-05 -0.546100  0.971047
2013-01-06 -0.373037  1.217092

Showing label slicing, both endpoints are included:

In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[26]: 
                   A         B
2013-01-02 -0.115180 -1.186477
2013-01-03  0.648884 -1.742358
2013-01-04 -0.896207 -0.995048

Reduction in the dimensions of the returned object:

In [27]: df.loc['20130102', ['A', 'B']].execute()
Out[27]: 
A   -0.115180
B   -1.186477
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [28]: df.loc['20130101', 'A'].execute()
Out[28]: -0.9247398569374877

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

In [29]: df.at['20130101', 'A'].execute()
Out[29]: -0.9247398569374877

Selection by position

Select via the position of the passed integers:

In [30]: df.iloc[3].execute()
Out[30]: 
A   -0.896207
B   -0.995048
C    0.680549
D   -1.133533
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 -0.896207 -0.995048
2013-01-05 -0.546100  0.971047

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 -0.115180  1.455773
2013-01-03  0.648884 -2.110255
2013-01-05 -0.546100 -0.431097

For slicing rows explicitly:

In [33]: df.iloc[1:3, :].execute()
Out[33]: 
                   A         B         C         D
2013-01-02 -0.115180 -1.186477  1.455773 -1.009835
2013-01-03  0.648884 -1.742358 -2.110255  0.699699

For slicing columns explicitly:

In [34]: df.iloc[:, 1:3].execute()
Out[34]: 
                   B         C
2013-01-01  0.770940  0.848022
2013-01-02 -1.186477  1.455773
2013-01-03 -1.742358 -2.110255
2013-01-04 -0.995048  0.680549
2013-01-05  0.971047 -0.431097
2013-01-06  1.217092 -1.576501

For getting a value explicitly:

In [35]: df.iloc[1, 1].execute()
Out[35]: -1.1864769305715868

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

In [36]: df.iat[1, 1].execute()
Out[36]: -1.1864769305715868

Boolean indexing

Using a single column’s values to select data.

In [37]: df[df['A'] > 0].execute()
Out[37]: 
                   A         B         C         D
2013-01-03  0.648884 -1.742358 -2.110255  0.699699

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       NaN  0.770940  0.848022  0.000714
2013-01-02       NaN       NaN  1.455773       NaN
2013-01-03  0.648884       NaN       NaN  0.699699
2013-01-04       NaN       NaN  0.680549       NaN
2013-01-05       NaN  0.971047       NaN       NaN
2013-01-06       NaN  1.217092       NaN       NaN

Operations

Stats

Operations in general exclude missing data.

Performing a descriptive statistic:

In [39]: df.mean().execute()
Out[39]: 
A   -0.367730
B   -0.160801
C   -0.188918
D   -0.567820
dtype: float64

Same operation on the other axis:

In [40]: df.mean(1).execute()
Out[40]: 
2013-01-01    0.173734
2013-01-02   -0.213930
2013-01-03   -0.626008
2013-01-04   -0.586060
2013-01-05   -0.241539
2013-01-06   -0.434101
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 -0.351116 -2.742358 -3.110255 -0.300301
2013-01-04 -3.896207 -3.995048 -2.319451 -4.133533
2013-01-05 -5.546100 -4.028953 -5.431097 -5.960006
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    1.573624
B    2.959450
C    3.566028
D    1.833232
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 -1.192854 -0.162735  0.850603 -0.503124
1  1.850383  0.167894 -0.793017 -0.214691
2 -0.473524  1.010253  0.632031 -0.425816
3 -0.682673  0.125081  1.528010 -2.036973
4  2.118904 -1.092768  0.935487 -1.521369
5 -0.443550  1.159571  1.237090  1.048793
6 -0.674023 -0.225085  0.594600 -0.483192
7  1.289236  2.228827 -2.402175  0.057371
8 -0.314895  2.010411 -0.432206  0.652077
9  1.480547  0.409850 -1.030584  0.328009

# 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 -1.192854 -0.162735  0.850603 -0.503124
1  1.850383  0.167894 -0.793017 -0.214691
2 -0.473524  1.010253  0.632031 -0.425816
3 -0.682673  0.125081  1.528010 -2.036973
4  2.118904 -1.092768  0.935487 -1.521369
5 -0.443550  1.159571  1.237090  1.048793
6 -0.674023 -0.225085  0.594600 -0.483192
7  1.289236  2.228827 -2.402175  0.057371
8 -0.314895  2.010411 -0.432206  0.652077
9  1.480547  0.409850 -1.030584  0.328009

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 -0.444180  1.652666
1  bar    one -0.053441  0.804783
2  foo    two  0.957405  0.073286
3  bar  three  0.019604  0.970040
4  foo    two  1.706177 -0.796002
5  bar    two -0.287955 -0.351346
6  foo    one  0.086541 -1.087024
7  foo  three  0.545263 -0.327195

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

In [63]: df.groupby('A').sum().execute()
Out[63]: 
            C         D
A                      
bar -0.321792  1.423477
foo  2.851206 -0.484268

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   -0.357639  0.565642
    two    2.663583 -0.722715
    three  0.545263 -0.327195
bar one   -0.053441  0.804783
    two   -0.287955 -0.351346
    three  0.019604  0.970040

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 0x7f35267a1b10>
../../_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  0.171792  -1.428496  -0.590932   0.212112
1    2000-01-02  1.606502  -1.694335   0.460951  -2.167250
2    2000-01-03  0.108290  -1.976547   0.753892  -1.838001
3    2000-01-04  1.045008  -1.335184  -0.878818  -0.033611
4    2000-01-05  1.395585  -2.295376  -2.711310  -0.185244
..          ...       ...        ...        ...        ...
995  2002-09-22  6.698322  92.609805 -30.091714 -26.088067
996  2002-09-23  5.041494  90.951700 -30.017777 -26.234808
997  2002-09-24  5.891865  92.485130 -28.675356 -25.813865
998  2002-09-25  6.164564  93.698065 -28.802962 -26.113199
999  2002-09-26  6.137191  92.348835 -28.728546 -27.055299

[1000 rows x 5 columns]