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  0.704246  1.794114 -2.133356  1.046581
2013-01-02 -0.372697  0.369636  2.124072 -0.647618
2013-01-03  1.206585  0.885519  1.800061  0.166788
2013-01-04  0.145012  1.696025  2.044533  0.435416
2013-01-05  1.348160 -0.280187 -1.036002  0.160458
2013-01-06 -1.172302 -0.133233 -0.432243  1.000946

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  0.704246  1.794114 -2.133356  1.046581
2013-01-02 -0.372697  0.369636  2.124072 -0.647618
2013-01-03  1.206585  0.885519  1.800061  0.166788
2013-01-04  0.145012  1.696025  2.044533  0.435416
2013-01-05  1.348160 -0.280187 -1.036002  0.160458

In [13]: df.tail(3).execute()
Out[13]: 
                   A         B         C         D
2013-01-04  0.145012  1.696025  2.044533  0.435416
2013-01-05  1.348160 -0.280187 -1.036002  0.160458
2013-01-06 -1.172302 -0.133233 -0.432243  1.000946

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([[ 0.70424561,  1.79411375, -2.13335627,  1.04658094],
       [-0.37269656,  0.36963576,  2.12407192, -0.64761789],
       [ 1.20658497,  0.88551919,  1.80006131,  0.16678843],
       [ 0.1450118 ,  1.69602542,  2.04453346,  0.43541608],
       [ 1.34815954, -0.28018688, -1.03600173,  0.16045793],
       [-1.17230185, -0.13323315, -0.4322434 ,  1.00094643]])

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   0.309834  0.721979  0.394511  0.360429
std    0.972377  0.892732  1.833550  0.629338
min   -1.172302 -0.280187 -2.133356 -0.647618
25%   -0.243269 -0.007516 -0.885062  0.162041
50%    0.424629  0.627577  0.683909  0.301102
75%    1.081000  1.493399  1.983415  0.859564
max    1.348160  1.794114  2.124072  1.046581

Sorting by an axis:

In [19]: df.sort_index(axis=1, ascending=False).execute()
Out[19]: 
                   D         C         B         A
2013-01-01  1.046581 -2.133356  1.794114  0.704246
2013-01-02 -0.647618  2.124072  0.369636 -0.372697
2013-01-03  0.166788  1.800061  0.885519  1.206585
2013-01-04  0.435416  2.044533  1.696025  0.145012
2013-01-05  0.160458 -1.036002 -0.280187  1.348160
2013-01-06  1.000946 -0.432243 -0.133233 -1.172302

Sorting by values:

In [20]: df.sort_values(by='B').execute()
Out[20]: 
                   A         B         C         D
2013-01-05  1.348160 -0.280187 -1.036002  0.160458
2013-01-06 -1.172302 -0.133233 -0.432243  1.000946
2013-01-02 -0.372697  0.369636  2.124072 -0.647618
2013-01-03  1.206585  0.885519  1.800061  0.166788
2013-01-04  0.145012  1.696025  2.044533  0.435416
2013-01-01  0.704246  1.794114 -2.133356  1.046581

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    0.704246
2013-01-02   -0.372697
2013-01-03    1.206585
2013-01-04    0.145012
2013-01-05    1.348160
2013-01-06   -1.172302
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  0.704246  1.794114 -2.133356  1.046581
2013-01-02 -0.372697  0.369636  2.124072 -0.647618
2013-01-03  1.206585  0.885519  1.800061  0.166788

In [23]: df['20130102':'20130104'].execute()
Out[23]: 
                   A         B         C         D
2013-01-02 -0.372697  0.369636  2.124072 -0.647618
2013-01-03  1.206585  0.885519  1.800061  0.166788
2013-01-04  0.145012  1.696025  2.044533  0.435416

Selection by label

For getting a cross section using a label:

In [24]: df.loc['20130101'].execute()
Out[24]: 
A    0.704246
B    1.794114
C   -2.133356
D    1.046581
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  0.704246  1.794114
2013-01-02 -0.372697  0.369636
2013-01-03  1.206585  0.885519
2013-01-04  0.145012  1.696025
2013-01-05  1.348160 -0.280187
2013-01-06 -1.172302 -0.133233

Showing label slicing, both endpoints are included:

In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute()
Out[26]: 
                   A         B
2013-01-02 -0.372697  0.369636
2013-01-03  1.206585  0.885519
2013-01-04  0.145012  1.696025

Reduction in the dimensions of the returned object:

In [27]: df.loc['20130102', ['A', 'B']].execute()
Out[27]: 
A   -0.372697
B    0.369636
Name: 2013-01-02 00:00:00, dtype: float64

For getting a scalar value:

In [28]: df.loc['20130101', 'A'].execute()
Out[28]: 0.7042456149386982

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

In [29]: df.at['20130101', 'A'].execute()
Out[29]: 0.7042456149386982

Selection by position

Select via the position of the passed integers:

In [30]: df.iloc[3].execute()
Out[30]: 
A    0.145012
B    1.696025
C    2.044533
D    0.435416
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  0.145012  1.696025
2013-01-05  1.348160 -0.280187

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 -0.372697  2.124072
2013-01-03  1.206585  1.800061
2013-01-05  1.348160 -1.036002

For slicing rows explicitly:

In [33]: df.iloc[1:3, :].execute()
Out[33]: 
                   A         B         C         D
2013-01-02 -0.372697  0.369636  2.124072 -0.647618
2013-01-03  1.206585  0.885519  1.800061  0.166788

For slicing columns explicitly:

In [34]: df.iloc[:, 1:3].execute()
Out[34]: 
                   B         C
2013-01-01  1.794114 -2.133356
2013-01-02  0.369636  2.124072
2013-01-03  0.885519  1.800061
2013-01-04  1.696025  2.044533
2013-01-05 -0.280187 -1.036002
2013-01-06 -0.133233 -0.432243

For getting a value explicitly:

In [35]: df.iloc[1, 1].execute()
Out[35]: 0.369635758922324

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

In [36]: df.iat[1, 1].execute()
Out[36]: 0.369635758922324

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  0.704246  1.794114 -2.133356  1.046581
2013-01-03  1.206585  0.885519  1.800061  0.166788
2013-01-04  0.145012  1.696025  2.044533  0.435416
2013-01-05  1.348160 -0.280187 -1.036002  0.160458

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  0.704246  1.794114       NaN  1.046581
2013-01-02       NaN  0.369636  2.124072       NaN
2013-01-03  1.206585  0.885519  1.800061  0.166788
2013-01-04  0.145012  1.696025  2.044533  0.435416
2013-01-05  1.348160       NaN       NaN  0.160458
2013-01-06       NaN       NaN       NaN  1.000946

Operations

Stats

Operations in general exclude missing data.

Performing a descriptive statistic:

In [39]: df.mean().execute()
Out[39]: 
A    0.309834
B    0.721979
C    0.394511
D    0.360429
dtype: float64

Same operation on the other axis:

In [40]: df.mean(1).execute()
Out[40]: 
2013-01-01    0.352896
2013-01-02    0.368348
2013-01-03    1.014738
2013-01-04    1.080247
2013-01-05    0.048107
2013-01-06   -0.184208
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  0.206585 -0.114481  0.800061 -0.833212
2013-01-04 -2.854988 -1.303975 -0.955467 -2.564584
2013-01-05 -3.651840 -5.280187 -6.036002 -4.839542
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    2.520461
B    2.074301
C    4.257428
D    1.694199
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 -1.157724 -0.189187  1.146274 -0.711210
1 -0.181547 -1.216801 -1.538909  0.432104
2 -0.539534 -0.334773  0.145556  0.435950
3 -0.915676  0.223804  1.414803  0.175416
4 -2.361754 -0.443951 -0.628845 -0.950372
5 -0.495911  0.079019  0.703715  0.817838
6 -0.827810  0.058145  1.293824 -1.176750
7 -0.258268  0.250121  0.419353 -0.869956
8  1.020997  0.802142  0.778613 -0.677730
9 -0.877974 -0.193732  0.272148 -0.143335

# 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 -1.157724 -0.189187  1.146274 -0.711210
1 -0.181547 -1.216801 -1.538909  0.432104
2 -0.539534 -0.334773  0.145556  0.435950
3 -0.915676  0.223804  1.414803  0.175416
4 -2.361754 -0.443951 -0.628845 -0.950372
5 -0.495911  0.079019  0.703715  0.817838
6 -0.827810  0.058145  1.293824 -1.176750
7 -0.258268  0.250121  0.419353 -0.869956
8  1.020997  0.802142  0.778613 -0.677730
9 -0.877974 -0.193732  0.272148 -0.143335

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 -1.150165  0.638342
1  bar    one  2.093781 -0.430106
2  foo    two -0.675295 -0.155558
3  bar  three -0.202238 -0.482107
4  foo    two -0.276685  0.159493
5  bar    two -0.535216 -0.180477
6  foo    one -0.856825 -1.418332
7  foo  three  0.614378 -1.893399

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

In [63]: df.groupby('A').sum().execute()
Out[63]: 
            C         D
A                      
bar  1.356327 -1.092690
foo -2.344592 -2.669453

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   -2.006991 -0.779990
    two   -0.951980  0.003936
    three  0.614378 -1.893399
bar one    2.093781 -0.430106
    two   -0.535216 -0.180477
    three -0.202238 -0.482107

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 0x7faea4c3f5f8>
../../_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  -0.748777   0.483233   0.122023  -0.213653
1    2000-01-02  -0.004817  -1.316996   0.211360   1.611840
2    2000-01-03  -0.099389  -1.921624   0.467984   1.260091
3    2000-01-04  -1.366523  -1.938647   0.084058  -0.293603
4    2000-01-05  -0.855157  -1.611363   1.021367  -1.343343
..          ...        ...        ...        ...        ...
995  2002-09-22 -16.907445 -31.699841  19.598046 -23.200662
996  2002-09-23 -18.927061 -32.042826  19.080499 -23.425057
997  2002-09-24 -16.930066 -31.171650  18.712998 -23.804921
998  2002-09-25 -17.608424 -31.682987  19.901356 -24.486571
999  2002-09-26 -17.118967 -30.709751  18.736074 -25.050285

[1000 rows x 5 columns]