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 0.747997 -0.126235 0.533325 -1.627778 2013-01-02 -0.852121 0.234301 0.256816 -0.759642 2013-01-03 0.068857 -1.481766 -0.462483 -0.364045 2013-01-04 1.055744 0.379540 0.498037 1.969377 2013-01-05 -1.347387 0.932255 1.137664 -1.792416 2013-01-06 -0.519955 0.095720 -0.086973 -2.194934
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 0.747997 -0.126235 0.533325 -1.627778 2013-01-02 -0.852121 0.234301 0.256816 -0.759642 2013-01-03 0.068857 -1.481766 -0.462483 -0.364045 2013-01-04 1.055744 0.379540 0.498037 1.969377 2013-01-05 -1.347387 0.932255 1.137664 -1.792416 In [13]: df.tail(3).execute() Out[13]: A B C D 2013-01-04 1.055744 0.379540 0.498037 1.969377 2013-01-05 -1.347387 0.932255 1.137664 -1.792416 2013-01-06 -0.519955 0.095720 -0.086973 -2.194934
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([[ 0.74799724, -0.12623484, 0.533325 , -1.62777768], [-0.8521213 , 0.23430101, 0.25681636, -0.75964186], [ 0.06885708, -1.48176566, -0.46248279, -0.36404542], [ 1.05574432, 0.37953998, 0.49803696, 1.9693765 ], [-1.34738737, 0.93225531, 1.13766367, -1.79241616], [-0.51995541, 0.09571984, -0.0869735 , -2.19493442]])
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 -0.141144 0.005636 0.312731 -0.794907 std 0.935011 0.810997 0.552695 1.515053 min -1.347387 -1.481766 -0.462483 -2.194934 25% -0.769080 -0.070746 -0.001026 -1.751257 50% -0.225549 0.165010 0.377427 -1.193710 75% 0.578212 0.343230 0.524503 -0.462945 max 1.055744 0.932255 1.137664 1.969377
Sorting by an axis:
In [19]: df.sort_index(axis=1, ascending=False).execute() Out[19]: D C B A 2013-01-01 -1.627778 0.533325 -0.126235 0.747997 2013-01-02 -0.759642 0.256816 0.234301 -0.852121 2013-01-03 -0.364045 -0.462483 -1.481766 0.068857 2013-01-04 1.969377 0.498037 0.379540 1.055744 2013-01-05 -1.792416 1.137664 0.932255 -1.347387 2013-01-06 -2.194934 -0.086973 0.095720 -0.519955
Sorting by values:
In [20]: df.sort_values(by='B').execute() Out[20]: A B C D 2013-01-03 0.068857 -1.481766 -0.462483 -0.364045 2013-01-01 0.747997 -0.126235 0.533325 -1.627778 2013-01-06 -0.519955 0.095720 -0.086973 -2.194934 2013-01-02 -0.852121 0.234301 0.256816 -0.759642 2013-01-04 1.055744 0.379540 0.498037 1.969377 2013-01-05 -1.347387 0.932255 1.137664 -1.792416
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 0.747997 2013-01-02 -0.852121 2013-01-03 0.068857 2013-01-04 1.055744 2013-01-05 -1.347387 2013-01-06 -0.519955 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 0.747997 -0.126235 0.533325 -1.627778 2013-01-02 -0.852121 0.234301 0.256816 -0.759642 2013-01-03 0.068857 -1.481766 -0.462483 -0.364045 In [23]: df['20130102':'20130104'].execute() Out[23]: A B C D 2013-01-02 -0.852121 0.234301 0.256816 -0.759642 2013-01-03 0.068857 -1.481766 -0.462483 -0.364045 2013-01-04 1.055744 0.379540 0.498037 1.969377
For getting a cross section using a label:
In [24]: df.loc['20130101'].execute() Out[24]: A 0.747997 B -0.126235 C 0.533325 D -1.627778 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 0.747997 -0.126235 2013-01-02 -0.852121 0.234301 2013-01-03 0.068857 -1.481766 2013-01-04 1.055744 0.379540 2013-01-05 -1.347387 0.932255 2013-01-06 -0.519955 0.095720
Showing label slicing, both endpoints are included:
In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute() Out[26]: A B 2013-01-02 -0.852121 0.234301 2013-01-03 0.068857 -1.481766 2013-01-04 1.055744 0.379540
Reduction in the dimensions of the returned object:
In [27]: df.loc['20130102', ['A', 'B']].execute() Out[27]: A -0.852121 B 0.234301 Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [28]: df.loc['20130101', 'A'].execute() Out[28]: 0.7479972380178543
For getting fast access to a scalar (equivalent to the prior method):
In [29]: df.at['20130101', 'A'].execute() Out[29]: 0.7479972380178543
Select via the position of the passed integers:
In [30]: df.iloc[3].execute() Out[30]: A 1.055744 B 0.379540 C 0.498037 D 1.969377 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 1.055744 0.379540 2013-01-05 -1.347387 0.932255
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 -0.852121 0.256816 2013-01-03 0.068857 -0.462483 2013-01-05 -1.347387 1.137664
For slicing rows explicitly:
In [33]: df.iloc[1:3, :].execute() Out[33]: A B C D 2013-01-02 -0.852121 0.234301 0.256816 -0.759642 2013-01-03 0.068857 -1.481766 -0.462483 -0.364045
For slicing columns explicitly:
In [34]: df.iloc[:, 1:3].execute() Out[34]: B C 2013-01-01 -0.126235 0.533325 2013-01-02 0.234301 0.256816 2013-01-03 -1.481766 -0.462483 2013-01-04 0.379540 0.498037 2013-01-05 0.932255 1.137664 2013-01-06 0.095720 -0.086973
For getting a value explicitly:
In [35]: df.iloc[1, 1].execute() Out[35]: 0.23430101352595223
In [36]: df.iat[1, 1].execute() Out[36]: 0.23430101352595223
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 0.747997 -0.126235 0.533325 -1.627778 2013-01-03 0.068857 -1.481766 -0.462483 -0.364045 2013-01-04 1.055744 0.379540 0.498037 1.969377
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 0.747997 NaN 0.533325 NaN 2013-01-02 NaN 0.234301 0.256816 NaN 2013-01-03 0.068857 NaN NaN NaN 2013-01-04 1.055744 0.379540 0.498037 1.969377 2013-01-05 NaN 0.932255 1.137664 NaN 2013-01-06 NaN 0.095720 NaN NaN
Operations in general exclude missing data.
Performing a descriptive statistic:
In [39]: df.mean().execute() Out[39]: A -0.141144 B 0.005636 C 0.312731 D -0.794907 dtype: float64
Same operation on the other axis:
In [40]: df.mean(1).execute() Out[40]: 2013-01-01 -0.118173 2013-01-02 -0.280161 2013-01-03 -0.559859 2013-01-04 0.975674 2013-01-05 -0.267471 2013-01-06 -0.676536 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 -0.931143 -2.481766 -1.462483 -1.364045 2013-01-04 -1.944256 -2.620460 -2.501963 -1.030623 2013-01-05 -6.347387 -4.067745 -3.862336 -6.792416 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 2.403132 B 2.414021 C 1.600146 D 4.164311 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 0.994734 -0.546457 -1.419974 -0.037988 1 1.360098 1.740276 0.753210 -0.767813 2 1.227487 0.863034 1.644027 -1.374760 3 0.380818 0.027993 -1.641427 0.717723 4 1.364787 -0.150053 0.832147 0.327401 5 -1.784067 -0.492302 -1.093105 -0.360189 6 2.057348 -1.074902 -0.415452 -0.371856 7 0.356218 0.468275 -1.047147 -0.886524 8 -0.398982 -0.094578 -1.313450 -0.205729 9 -0.788926 -0.072814 0.767682 -0.630996 # 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 0.994734 -0.546457 -1.419974 -0.037988 1 1.360098 1.740276 0.753210 -0.767813 2 1.227487 0.863034 1.644027 -1.374760 3 0.380818 0.027993 -1.641427 0.717723 4 1.364787 -0.150053 0.832147 0.327401 5 -1.784067 -0.492302 -1.093105 -0.360189 6 2.057348 -1.074902 -0.415452 -0.371856 7 0.356218 0.468275 -1.047147 -0.886524 8 -0.398982 -0.094578 -1.313450 -0.205729 9 -0.788926 -0.072814 0.767682 -0.630996
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 -0.085166 0.706216 1 bar one 0.468461 -0.169199 2 foo two -0.975011 0.057508 3 bar three 0.069394 0.239354 4 foo two 0.096698 0.449747 5 bar two 0.256967 -1.350881 6 foo one 0.853393 -0.182098 7 foo three -1.552939 0.703761
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 0.794821 -1.280725 foo -1.663025 1.735135
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 0.768227 0.524118 two -0.878313 0.507255 three -1.552939 0.703761 bar one 0.468461 -0.169199 two 0.256967 -1.350881 three 0.069394 0.239354
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 0x7f7f7d667450>
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 0.591172 -0.020792 -0.311366 -0.249707 1 2000-01-02 0.954831 1.231866 0.060967 0.367260 2 2000-01-03 1.543444 1.693592 0.619293 -0.234265 3 2000-01-04 1.738200 0.231179 1.503979 -0.120758 4 2000-01-05 -0.200018 1.339425 2.546024 -0.907049 .. ... ... ... ... ... 995 2002-09-22 19.891988 -36.056552 33.093392 2.407129 996 2002-09-23 19.372245 -36.823996 34.087287 4.148152 997 2002-09-24 21.290717 -35.097480 33.496580 5.638094 998 2002-09-25 20.930483 -34.539374 33.191667 4.277192 999 2002-09-26 19.231929 -34.216166 32.605880 5.475596 [1000 rows x 5 columns]