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 0.960848 0.297661 0.870183 -1.575193 2013-01-02 1.295732 -0.535892 -1.174109 1.517341 2013-01-03 1.159627 1.780637 1.075509 1.431557 2013-01-04 -0.111415 0.299132 -1.348894 1.554470 2013-01-05 -0.892663 -1.086993 -0.520841 -0.544437 2013-01-06 1.342391 0.007117 -0.959792 0.565875
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 0.960848 0.297661 0.870183 -1.575193 2013-01-02 1.295732 -0.535892 -1.174109 1.517341 2013-01-03 1.159627 1.780637 1.075509 1.431557 2013-01-04 -0.111415 0.299132 -1.348894 1.554470 2013-01-05 -0.892663 -1.086993 -0.520841 -0.544437 In [13]: df.tail(3).execute() Out[13]: A B C D 2013-01-04 -0.111415 0.299132 -1.348894 1.554470 2013-01-05 -0.892663 -1.086993 -0.520841 -0.544437 2013-01-06 1.342391 0.007117 -0.959792 0.565875
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([[ 0.9608482 , 0.29766134, 0.87018271, -1.57519279], [ 1.29573241, -0.53589213, -1.17410891, 1.51734107], [ 1.15962749, 1.78063688, 1.07550894, 1.43155654], [-0.11141514, 0.29913241, -1.34889449, 1.55446998], [-0.89266252, -1.08699256, -0.52084058, -0.54443662], [ 1.34239051, 0.00711697, -0.95979215, 0.56587513]])
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 0.625753 0.126944 -0.342991 0.491602 std 0.917470 0.971684 1.058169 1.297372 min -0.892663 -1.086993 -1.348894 -1.575193 25% 0.156651 -0.400140 -1.120530 -0.266859 50% 1.060238 0.152389 -0.740316 0.998716 75% 1.261706 0.298765 0.522427 1.495895 max 1.342391 1.780637 1.075509 1.554470
Sorting by an axis:
In [19]: df.sort_index(axis=1, ascending=False).execute() Out[19]: D C B A 2013-01-01 -1.575193 0.870183 0.297661 0.960848 2013-01-02 1.517341 -1.174109 -0.535892 1.295732 2013-01-03 1.431557 1.075509 1.780637 1.159627 2013-01-04 1.554470 -1.348894 0.299132 -0.111415 2013-01-05 -0.544437 -0.520841 -1.086993 -0.892663 2013-01-06 0.565875 -0.959792 0.007117 1.342391
Sorting by values:
In [20]: df.sort_values(by='B').execute() Out[20]: A B C D 2013-01-05 -0.892663 -1.086993 -0.520841 -0.544437 2013-01-02 1.295732 -0.535892 -1.174109 1.517341 2013-01-06 1.342391 0.007117 -0.959792 0.565875 2013-01-01 0.960848 0.297661 0.870183 -1.575193 2013-01-04 -0.111415 0.299132 -1.348894 1.554470 2013-01-03 1.159627 1.780637 1.075509 1.431557
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 0.960848 2013-01-02 1.295732 2013-01-03 1.159627 2013-01-04 -0.111415 2013-01-05 -0.892663 2013-01-06 1.342391 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 0.960848 0.297661 0.870183 -1.575193 2013-01-02 1.295732 -0.535892 -1.174109 1.517341 2013-01-03 1.159627 1.780637 1.075509 1.431557 In [23]: df['20130102':'20130104'].execute() Out[23]: A B C D 2013-01-02 1.295732 -0.535892 -1.174109 1.517341 2013-01-03 1.159627 1.780637 1.075509 1.431557 2013-01-04 -0.111415 0.299132 -1.348894 1.554470
For getting a cross section using a label:
In [24]: df.loc['20130101'].execute() Out[24]: A 0.960848 B 0.297661 C 0.870183 D -1.575193 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 0.960848 0.297661 2013-01-02 1.295732 -0.535892 2013-01-03 1.159627 1.780637 2013-01-04 -0.111415 0.299132 2013-01-05 -0.892663 -1.086993 2013-01-06 1.342391 0.007117
Showing label slicing, both endpoints are included:
In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute() Out[26]: A B 2013-01-02 1.295732 -0.535892 2013-01-03 1.159627 1.780637 2013-01-04 -0.111415 0.299132
Reduction in the dimensions of the returned object:
In [27]: df.loc['20130102', ['A', 'B']].execute() Out[27]: A 1.295732 B -0.535892 Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [28]: df.loc['20130101', 'A'].execute() Out[28]: 0.9608482045183592
For getting fast access to a scalar (equivalent to the prior method):
In [29]: df.at['20130101', 'A'].execute() Out[29]: 0.9608482045183592
Select via the position of the passed integers:
In [30]: df.iloc[3].execute() Out[30]: A -0.111415 B 0.299132 C -1.348894 D 1.554470 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 -0.111415 0.299132 2013-01-05 -0.892663 -1.086993
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 1.295732 -1.174109 2013-01-03 1.159627 1.075509 2013-01-05 -0.892663 -0.520841
For slicing rows explicitly:
In [33]: df.iloc[1:3, :].execute() Out[33]: A B C D 2013-01-02 1.295732 -0.535892 -1.174109 1.517341 2013-01-03 1.159627 1.780637 1.075509 1.431557
For slicing columns explicitly:
In [34]: df.iloc[:, 1:3].execute() Out[34]: B C 2013-01-01 0.297661 0.870183 2013-01-02 -0.535892 -1.174109 2013-01-03 1.780637 1.075509 2013-01-04 0.299132 -1.348894 2013-01-05 -1.086993 -0.520841 2013-01-06 0.007117 -0.959792
For getting a value explicitly:
In [35]: df.iloc[1, 1].execute() Out[35]: -0.5358921333121628
In [36]: df.iat[1, 1].execute() Out[36]: -0.5358921333121628
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 0.960848 0.297661 0.870183 -1.575193 2013-01-02 1.295732 -0.535892 -1.174109 1.517341 2013-01-03 1.159627 1.780637 1.075509 1.431557 2013-01-06 1.342391 0.007117 -0.959792 0.565875
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 0.960848 0.297661 0.870183 NaN 2013-01-02 1.295732 NaN NaN 1.517341 2013-01-03 1.159627 1.780637 1.075509 1.431557 2013-01-04 NaN 0.299132 NaN 1.554470 2013-01-05 NaN NaN NaN NaN 2013-01-06 1.342391 0.007117 NaN 0.565875
Operations in general exclude missing data.
Performing a descriptive statistic:
In [39]: df.mean().execute() Out[39]: A 0.625753 B 0.126944 C -0.342991 D 0.491602 dtype: float64
Same operation on the other axis:
In [40]: df.mean(1).execute() Out[40]: 2013-01-01 0.138375 2013-01-02 0.275768 2013-01-03 1.361832 2013-01-04 0.098323 2013-01-05 -0.761233 2013-01-06 0.238898 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 0.159627 0.780637 0.075509 0.431557 2013-01-04 -3.111415 -2.700868 -4.348894 -1.445530 2013-01-05 -5.892663 -6.086993 -5.520841 -5.544437 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 2.235053 B 2.867629 C 2.424403 D 3.129663 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 -1.113954 -0.517166 -0.672981 -1.692474 1 -1.331356 -2.109324 -0.722626 0.463621 2 -2.027936 -2.071097 0.628846 0.700336 3 -0.491510 0.490953 -0.160228 -1.332878 4 -0.141396 0.718346 -0.099533 -0.835041 5 -1.295915 0.745409 0.134233 0.533249 6 1.295633 -0.153067 1.313489 -0.126268 7 0.405514 1.265972 0.146944 0.622759 8 -0.864954 0.627396 0.820537 -0.278805 9 0.526630 -1.650138 0.177117 0.303300 # 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 -1.113954 -0.517166 -0.672981 -1.692474 1 -1.331356 -2.109324 -0.722626 0.463621 2 -2.027936 -2.071097 0.628846 0.700336 3 -0.491510 0.490953 -0.160228 -1.332878 4 -0.141396 0.718346 -0.099533 -0.835041 5 -1.295915 0.745409 0.134233 0.533249 6 1.295633 -0.153067 1.313489 -0.126268 7 0.405514 1.265972 0.146944 0.622759 8 -0.864954 0.627396 0.820537 -0.278805 9 0.526630 -1.650138 0.177117 0.303300
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 -2.190958 -0.446821 1 bar one -1.136630 1.211013 2 foo two 0.428636 0.307273 3 bar three 1.112435 -0.640566 4 foo two -1.119867 -0.260091 5 bar two 0.466883 -0.250718 6 foo one -0.150621 -1.071470 7 foo three 0.176874 -1.210768
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 0.442688 0.319728 foo -2.855937 -2.681877
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 -2.341579 -1.518291 two -0.691231 0.047182 three 0.176874 -1.210768 bar one -1.136630 1.211013 two 0.466883 -0.250718 three 1.112435 -0.640566
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 0x7f0166f23b90>
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 0.647150 -1.727229 0.242598 0.078094 1 2000-01-02 -0.208577 -1.987557 1.030567 1.452177 2 2000-01-03 -1.744447 -2.827255 0.595119 2.697364 3 2000-01-04 -2.787622 -4.265996 1.239133 2.224166 4 2000-01-05 -3.467033 -5.392606 0.903943 3.297445 .. ... ... ... ... ... 995 2002-09-22 8.536527 -2.346387 -37.631081 -0.499993 996 2002-09-23 8.330832 -0.345227 -36.810593 0.505014 997 2002-09-24 9.685608 0.928617 -37.172674 0.502246 998 2002-09-25 9.020467 -0.448318 -38.884463 0.633670 999 2002-09-26 8.818261 0.429396 -40.779007 -0.658706 [1000 rows x 5 columns]