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
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:>
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>
Getting data in/out¶
CSV¶
In [75]: df.to_csv('foo.csv').execute()
Out[75]:
Empty DataFrame
Columns: []
Index: []
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]