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
Creating a Series by passing a list of values, letting it create a default integer index:
Series
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:
DataFrame
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 1.380039 -0.303651 0.344097 1.020950 2013-01-02 -0.683558 0.947133 -1.996983 -1.215373 2013-01-03 1.208587 0.539496 -0.206538 -0.089361 2013-01-04 -1.856555 0.776693 -0.152847 -0.712563 2013-01-05 -0.828458 -0.096248 0.764279 0.217016 2013-01-06 0.080176 -0.973207 1.474233 -0.528768
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
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 1.380039 -0.303651 0.344097 1.020950 2013-01-02 -0.683558 0.947133 -1.996983 -1.215373 2013-01-03 1.208587 0.539496 -0.206538 -0.089361 2013-01-04 -1.856555 0.776693 -0.152847 -0.712563 2013-01-05 -0.828458 -0.096248 0.764279 0.217016 In [13]: df.tail(3).execute() Out[13]: A B C D 2013-01-04 -1.856555 0.776693 -0.152847 -0.712563 2013-01-05 -0.828458 -0.096248 0.764279 0.217016 2013-01-06 0.080176 -0.973207 1.474233 -0.528768
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.
DataFrame.to_tensor()
object
For df, our DataFrame of all floating-point values, DataFrame.to_tensor() is fast and doesn’t require copying data.
df
In [16]: df.to_tensor().execute() Out[16]: array([[ 1.38003874, -0.30365081, 0.3440968 , 1.02094996], [-0.68355761, 0.94713257, -1.99698258, -1.21537257], [ 1.20858735, 0.5394963 , -0.20653791, -0.0893615 ], [-1.85655497, 0.77669312, -0.15284695, -0.71256303], [-0.82845757, -0.09624842, 0.76427906, 0.21701598], [ 0.08017614, -0.97320724, 1.47423281, -0.52876787]])
For df2, the DataFrame with multiple dtypes, DataFrame.to_tensor() is relatively expensive.
df2
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:
describe()
In [18]: df.describe().execute() Out[18]: A B C D count 6.000000 6.000000 6.000000 6.000000 mean -0.116628 0.148369 0.037707 -0.218017 std 1.256216 0.735920 1.176547 0.784437 min -1.856555 -0.973207 -1.996983 -1.215373 25% -0.792233 -0.251800 -0.193115 -0.666614 50% -0.301691 0.221624 0.095625 -0.309065 75% 0.926485 0.717394 0.659233 0.140422 max 1.380039 0.947133 1.474233 1.020950
Sorting by an axis:
In [19]: df.sort_index(axis=1, ascending=False).execute() Out[19]: D C B A 2013-01-01 1.020950 0.344097 -0.303651 1.380039 2013-01-02 -1.215373 -1.996983 0.947133 -0.683558 2013-01-03 -0.089361 -0.206538 0.539496 1.208587 2013-01-04 -0.712563 -0.152847 0.776693 -1.856555 2013-01-05 0.217016 0.764279 -0.096248 -0.828458 2013-01-06 -0.528768 1.474233 -0.973207 0.080176
Sorting by values:
In [20]: df.sort_values(by='B').execute() Out[20]: A B C D 2013-01-06 0.080176 -0.973207 1.474233 -0.528768 2013-01-01 1.380039 -0.303651 0.344097 1.020950 2013-01-05 -0.828458 -0.096248 0.764279 0.217016 2013-01-03 1.208587 0.539496 -0.206538 -0.089361 2013-01-04 -1.856555 0.776693 -0.152847 -0.712563 2013-01-02 -0.683558 0.947133 -1.996983 -1.215373
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.
.at
.iat
.loc
.iloc
Selecting a single column, which yields a Series, equivalent to df.A:
df.A
In [21]: df['A'].execute() Out[21]: 2013-01-01 1.380039 2013-01-02 -0.683558 2013-01-03 1.208587 2013-01-04 -1.856555 2013-01-05 -0.828458 2013-01-06 0.080176 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 1.380039 -0.303651 0.344097 1.020950 2013-01-02 -0.683558 0.947133 -1.996983 -1.215373 2013-01-03 1.208587 0.539496 -0.206538 -0.089361 In [23]: df['20130102':'20130104'].execute() Out[23]: A B C D 2013-01-02 -0.683558 0.947133 -1.996983 -1.215373 2013-01-03 1.208587 0.539496 -0.206538 -0.089361 2013-01-04 -1.856555 0.776693 -0.152847 -0.712563
For getting a cross section using a label:
In [24]: df.loc['20130101'].execute() Out[24]: A 1.380039 B -0.303651 C 0.344097 D 1.020950 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 1.380039 -0.303651 2013-01-02 -0.683558 0.947133 2013-01-03 1.208587 0.539496 2013-01-04 -1.856555 0.776693 2013-01-05 -0.828458 -0.096248 2013-01-06 0.080176 -0.973207
Showing label slicing, both endpoints are included:
In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute() Out[26]: A B 2013-01-02 -0.683558 0.947133 2013-01-03 1.208587 0.539496 2013-01-04 -1.856555 0.776693
Reduction in the dimensions of the returned object:
In [27]: df.loc['20130102', ['A', 'B']].execute() Out[27]: A -0.683558 B 0.947133 Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [28]: df.loc['20130101', 'A'].execute() Out[28]: 1.3800387404592631
For getting fast access to a scalar (equivalent to the prior method):
In [29]: df.at['20130101', 'A'].execute() Out[29]: 1.3800387404592631
Select via the position of the passed integers:
In [30]: df.iloc[3].execute() Out[30]: A -1.856555 B 0.776693 C -0.152847 D -0.712563 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 -1.856555 0.776693 2013-01-05 -0.828458 -0.096248
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 -0.683558 -1.996983 2013-01-03 1.208587 -0.206538 2013-01-05 -0.828458 0.764279
For slicing rows explicitly:
In [33]: df.iloc[1:3, :].execute() Out[33]: A B C D 2013-01-02 -0.683558 0.947133 -1.996983 -1.215373 2013-01-03 1.208587 0.539496 -0.206538 -0.089361
For slicing columns explicitly:
In [34]: df.iloc[:, 1:3].execute() Out[34]: B C 2013-01-01 -0.303651 0.344097 2013-01-02 0.947133 -1.996983 2013-01-03 0.539496 -0.206538 2013-01-04 0.776693 -0.152847 2013-01-05 -0.096248 0.764279 2013-01-06 -0.973207 1.474233
For getting a value explicitly:
In [35]: df.iloc[1, 1].execute() Out[35]: 0.947132573716667
In [36]: df.iat[1, 1].execute() Out[36]: 0.947132573716667
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 1.380039 -0.303651 0.344097 1.020950 2013-01-03 1.208587 0.539496 -0.206538 -0.089361 2013-01-06 0.080176 -0.973207 1.474233 -0.528768
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 1.380039 NaN 0.344097 1.020950 2013-01-02 NaN 0.947133 NaN NaN 2013-01-03 1.208587 0.539496 NaN NaN 2013-01-04 NaN 0.776693 NaN NaN 2013-01-05 NaN NaN 0.764279 0.217016 2013-01-06 0.080176 NaN 1.474233 NaN
Operations in general exclude missing data.
Performing a descriptive statistic:
In [39]: df.mean().execute() Out[39]: A -0.116628 B 0.148369 C 0.037707 D -0.218017 dtype: float64
Same operation on the other axis:
In [40]: df.mean(1).execute() Out[40]: 2013-01-01 0.610359 2013-01-02 -0.737195 2013-01-03 0.363046 2013-01-04 -0.486318 2013-01-05 0.014147 2013-01-06 0.013108 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 0.208587 -0.460504 -1.206538 -1.089361 2013-01-04 -4.856555 -2.223307 -3.152847 -3.712563 2013-01-05 -5.828458 -5.096248 -4.235721 -4.782984 2013-01-06 NaN NaN NaN NaN
Applying functions to the data:
In [44]: df.apply(lambda x: x.max() - x.min()).execute() Out[44]: A 3.236594 B 1.920340 C 3.471215 D 2.236323 dtype: float64
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
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():
concat()
In [47]: df = md.DataFrame(mt.random.randn(10, 4)) In [48]: df.execute() Out[48]: 0 1 2 3 0 0.009272 -0.659995 -0.378903 -1.410914 1 -1.824559 1.399596 -0.953777 -1.109440 2 0.611343 0.584350 1.876943 -1.694674 3 -0.427432 -1.107903 -0.436457 0.290908 4 0.609468 0.096552 0.178083 1.152755 5 1.876762 0.334645 0.290916 0.525072 6 1.280286 1.981358 -2.052386 0.717608 7 0.768622 0.810061 0.151142 -0.855658 8 -0.816228 0.791419 -1.255221 0.401390 9 -1.781242 -1.562540 0.345979 0.392550 # 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 0.009272 -0.659995 -0.378903 -1.410914 1 -1.824559 1.399596 -0.953777 -1.109440 2 0.611343 0.584350 1.876943 -1.694674 3 -0.427432 -1.107903 -0.436457 0.290908 4 0.609468 0.096552 0.178083 1.152755 5 1.876762 0.334645 0.290916 0.525072 6 1.280286 1.981358 -2.052386 0.717608 7 0.768622 0.810061 0.151142 -0.855658 8 -0.816228 0.791419 -1.255221 0.401390 9 -1.781242 -1.562540 0.345979 0.392550
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.
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
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
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 1.214069 0.058317 1 bar one 0.348770 -0.747681 2 foo two 1.204847 -0.550463 3 bar three -0.750572 0.537047 4 foo two -1.489774 -1.829142 5 bar two -0.896199 -2.055427 6 foo one 0.702748 0.535140 7 foo three -0.781145 1.228572
Grouping and then applying the sum() function to the resulting groups.
sum()
In [63]: df.groupby('A').sum().execute() Out[63]: C D A bar -1.298001 -2.266061 foo 0.850746 -0.557578
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 1.916817 0.593457 two -0.284926 -2.379606 three -0.781145 1.228572 bar one 0.348770 -0.747681 two -0.896199 -2.055427 three -0.750572 0.537047
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:
plot()
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 0x7f55f8a66890>
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 3.538462 0.708814 0.867920 -0.681444 1 2000-01-02 3.327751 0.060214 0.170646 -1.239853 2 2000-01-03 3.736594 -0.790232 -0.955341 0.777303 3 2000-01-04 3.507770 -0.902764 0.019091 0.185178 4 2000-01-05 2.706285 -0.937815 1.130297 -0.302031 .. ... ... ... ... ... 995 2002-09-22 14.431740 -58.937363 20.424355 13.882051 996 2002-09-23 13.428467 -58.334534 19.236940 14.002610 997 2002-09-24 15.259785 -58.713229 19.039831 13.710882 998 2002-09-25 16.104898 -59.547079 17.827294 14.131420 999 2002-09-26 16.785207 -59.716472 18.613674 14.646230 [1000 rows x 5 columns]