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
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:>
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>
Getting data in/out¶
CSV¶
In [77]: df.to_csv('foo.csv').execute()
Out[77]:
Empty DataFrame
Columns: []
Index: []
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]