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 0.996172 0.297444 -0.025738 -0.707780 2013-01-02 -0.555496 -0.354014 -0.745561 0.865680 2013-01-03 -0.133414 -1.006552 0.902451 1.514278 2013-01-04 -0.513947 0.558884 -1.549307 -0.590955 2013-01-05 -0.469396 0.269355 2.267890 0.660135 2013-01-06 -1.645099 -1.831922 -0.549775 -0.474557
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 0.996172 0.297444 -0.025738 -0.707780 2013-01-02 -0.555496 -0.354014 -0.745561 0.865680 2013-01-03 -0.133414 -1.006552 0.902451 1.514278 2013-01-04 -0.513947 0.558884 -1.549307 -0.590955 2013-01-05 -0.469396 0.269355 2.267890 0.660135 In [13]: df.tail(3).execute() Out[13]: A B C D 2013-01-04 -0.513947 0.558884 -1.549307 -0.590955 2013-01-05 -0.469396 0.269355 2.267890 0.660135 2013-01-06 -1.645099 -1.831922 -0.549775 -0.474557
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([[ 0.99617173, 0.29744392, -0.02573815, -0.70777961], [-0.55549562, -0.35401445, -0.74556121, 0.86567991], [-0.13341362, -1.0065516 , 0.90245066, 1.51427841], [-0.51394657, 0.55888406, -1.54930722, -0.59095507], [-0.46939609, 0.2693546 , 2.26789014, 0.66013511], [-1.64509948, -1.83192221, -0.54977473, -0.47455679]])
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 -0.386863 -0.344468 0.049993 0.211134 std 0.849892 0.922588 1.356604 0.925866 min -1.645099 -1.831922 -1.549307 -0.707780 25% -0.545108 -0.843417 -0.696615 -0.561856 50% -0.491671 -0.042330 -0.287756 0.092789 75% -0.217409 0.290422 0.670403 0.814294 max 0.996172 0.558884 2.267890 1.514278
Sorting by an axis:
In [19]: df.sort_index(axis=1, ascending=False).execute() Out[19]: D C B A 2013-01-01 -0.707780 -0.025738 0.297444 0.996172 2013-01-02 0.865680 -0.745561 -0.354014 -0.555496 2013-01-03 1.514278 0.902451 -1.006552 -0.133414 2013-01-04 -0.590955 -1.549307 0.558884 -0.513947 2013-01-05 0.660135 2.267890 0.269355 -0.469396 2013-01-06 -0.474557 -0.549775 -1.831922 -1.645099
Sorting by values:
In [20]: df.sort_values(by='B').execute() Out[20]: A B C D 2013-01-06 -1.645099 -1.831922 -0.549775 -0.474557 2013-01-03 -0.133414 -1.006552 0.902451 1.514278 2013-01-02 -0.555496 -0.354014 -0.745561 0.865680 2013-01-05 -0.469396 0.269355 2.267890 0.660135 2013-01-01 0.996172 0.297444 -0.025738 -0.707780 2013-01-04 -0.513947 0.558884 -1.549307 -0.590955
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 0.996172 2013-01-02 -0.555496 2013-01-03 -0.133414 2013-01-04 -0.513947 2013-01-05 -0.469396 2013-01-06 -1.645099 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 0.996172 0.297444 -0.025738 -0.707780 2013-01-02 -0.555496 -0.354014 -0.745561 0.865680 2013-01-03 -0.133414 -1.006552 0.902451 1.514278 In [23]: df['20130102':'20130104'].execute() Out[23]: A B C D 2013-01-02 -0.555496 -0.354014 -0.745561 0.865680 2013-01-03 -0.133414 -1.006552 0.902451 1.514278 2013-01-04 -0.513947 0.558884 -1.549307 -0.590955
For getting a cross section using a label:
In [24]: df.loc['20130101'].execute() Out[24]: A 0.996172 B 0.297444 C -0.025738 D -0.707780 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 0.996172 0.297444 2013-01-02 -0.555496 -0.354014 2013-01-03 -0.133414 -1.006552 2013-01-04 -0.513947 0.558884 2013-01-05 -0.469396 0.269355 2013-01-06 -1.645099 -1.831922
Showing label slicing, both endpoints are included:
In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute() Out[26]: A B 2013-01-02 -0.555496 -0.354014 2013-01-03 -0.133414 -1.006552 2013-01-04 -0.513947 0.558884
Reduction in the dimensions of the returned object:
In [27]: df.loc['20130102', ['A', 'B']].execute() Out[27]: A -0.555496 B -0.354014 Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [28]: df.loc['20130101', 'A'].execute() Out[28]: 0.9961717326498453
For getting fast access to a scalar (equivalent to the prior method):
In [29]: df.at['20130101', 'A'].execute() Out[29]: 0.9961717326498453
Select via the position of the passed integers:
In [30]: df.iloc[3].execute() Out[30]: A -0.513947 B 0.558884 C -1.549307 D -0.590955 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 -0.513947 0.558884 2013-01-05 -0.469396 0.269355
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 -0.555496 -0.745561 2013-01-03 -0.133414 0.902451 2013-01-05 -0.469396 2.267890
For slicing rows explicitly:
In [33]: df.iloc[1:3, :].execute() Out[33]: A B C D 2013-01-02 -0.555496 -0.354014 -0.745561 0.865680 2013-01-03 -0.133414 -1.006552 0.902451 1.514278
For slicing columns explicitly:
In [34]: df.iloc[:, 1:3].execute() Out[34]: B C 2013-01-01 0.297444 -0.025738 2013-01-02 -0.354014 -0.745561 2013-01-03 -1.006552 0.902451 2013-01-04 0.558884 -1.549307 2013-01-05 0.269355 2.267890 2013-01-06 -1.831922 -0.549775
For getting a value explicitly:
In [35]: df.iloc[1, 1].execute() Out[35]: -0.354014452391651
In [36]: df.iat[1, 1].execute() Out[36]: -0.354014452391651
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 0.996172 0.297444 -0.025738 -0.70778
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 0.996172 0.297444 NaN NaN 2013-01-02 NaN NaN NaN 0.865680 2013-01-03 NaN NaN 0.902451 1.514278 2013-01-04 NaN 0.558884 NaN NaN 2013-01-05 NaN 0.269355 2.267890 0.660135 2013-01-06 NaN NaN NaN NaN
Operations in general exclude missing data.
Performing a descriptive statistic:
In [39]: df.mean().execute() Out[39]: A -0.386863 B -0.344468 C 0.049993 D 0.211134 dtype: float64
Same operation on the other axis:
In [40]: df.mean(1).execute() Out[40]: 2013-01-01 0.140024 2013-01-02 -0.197348 2013-01-03 0.319191 2013-01-04 -0.523831 2013-01-05 0.681996 2013-01-06 -1.125338 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 -1.133414 -2.006552 -0.097549 0.514278 2013-01-04 -3.513947 -2.441116 -4.549307 -3.590955 2013-01-05 -5.469396 -4.730645 -2.732110 -4.339865 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 2.641271 B 2.390806 C 3.817197 D 2.222058 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 0.246649 -1.121654 -0.104328 0.469352 1 -0.458209 -1.367174 -0.499511 -0.424648 2 0.811784 0.539248 1.212989 -0.664290 3 0.784364 0.706993 0.607985 -0.030239 4 -0.270892 -0.440902 -1.959573 -0.267640 5 -0.420808 0.426049 -0.155517 0.677272 6 1.103542 -0.592715 -0.277834 -0.741513 7 0.807872 -0.432451 0.639677 -0.576744 8 1.270396 0.992311 0.388719 1.663674 9 1.017798 0.002464 -0.212461 -0.627949 # 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 0.246649 -1.121654 -0.104328 0.469352 1 -0.458209 -1.367174 -0.499511 -0.424648 2 0.811784 0.539248 1.212989 -0.664290 3 0.784364 0.706993 0.607985 -0.030239 4 -0.270892 -0.440902 -1.959573 -0.267640 5 -0.420808 0.426049 -0.155517 0.677272 6 1.103542 -0.592715 -0.277834 -0.741513 7 0.807872 -0.432451 0.639677 -0.576744 8 1.270396 0.992311 0.388719 1.663674 9 1.017798 0.002464 -0.212461 -0.627949
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 0.201133 -0.509286 1 bar one -2.486497 0.819362 2 foo two 1.592384 -0.388189 3 bar three -0.417854 1.898835 4 foo two 0.740800 1.337719 5 bar two 1.446650 -1.069157 6 foo one 0.221476 0.364358 7 foo three -2.080049 0.660333
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 -1.457701 1.649041 foo 0.675743 1.464934
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 0.422608 -0.144929 two 2.333184 0.949530 three -2.080049 0.660333 bar one -2.486497 0.819362 two 1.446650 -1.069157 three -0.417854 1.898835
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 0x7fdbe38ab610>
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 -1.454651 0.225538 1.767234 -0.485952 1 2000-01-02 -0.546805 1.627389 0.665487 -1.909533 2 2000-01-03 -1.798785 1.505491 1.635632 -1.852234 3 2000-01-04 -1.562943 1.479468 0.569763 -2.766721 4 2000-01-05 -2.095900 1.169591 0.538246 -3.097193 .. ... ... ... ... ... 995 2002-09-22 -21.399585 -93.954730 -23.808535 19.700636 996 2002-09-23 -20.846456 -94.806360 -22.291834 21.326204 997 2002-09-24 -21.792252 -95.241904 -22.437903 20.599168 998 2002-09-25 -21.230538 -96.136098 -22.841493 19.696823 999 2002-09-26 -22.219553 -93.998680 -23.032008 19.539431 [1000 rows x 5 columns]