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 0.704246 1.794114 -2.133356 1.046581 2013-01-02 -0.372697 0.369636 2.124072 -0.647618 2013-01-03 1.206585 0.885519 1.800061 0.166788 2013-01-04 0.145012 1.696025 2.044533 0.435416 2013-01-05 1.348160 -0.280187 -1.036002 0.160458 2013-01-06 -1.172302 -0.133233 -0.432243 1.000946
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 0.704246 1.794114 -2.133356 1.046581 2013-01-02 -0.372697 0.369636 2.124072 -0.647618 2013-01-03 1.206585 0.885519 1.800061 0.166788 2013-01-04 0.145012 1.696025 2.044533 0.435416 2013-01-05 1.348160 -0.280187 -1.036002 0.160458 In [13]: df.tail(3).execute() Out[13]: A B C D 2013-01-04 0.145012 1.696025 2.044533 0.435416 2013-01-05 1.348160 -0.280187 -1.036002 0.160458 2013-01-06 -1.172302 -0.133233 -0.432243 1.000946
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([[ 0.70424561, 1.79411375, -2.13335627, 1.04658094], [-0.37269656, 0.36963576, 2.12407192, -0.64761789], [ 1.20658497, 0.88551919, 1.80006131, 0.16678843], [ 0.1450118 , 1.69602542, 2.04453346, 0.43541608], [ 1.34815954, -0.28018688, -1.03600173, 0.16045793], [-1.17230185, -0.13323315, -0.4322434 , 1.00094643]])
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 0.309834 0.721979 0.394511 0.360429 std 0.972377 0.892732 1.833550 0.629338 min -1.172302 -0.280187 -2.133356 -0.647618 25% -0.243269 -0.007516 -0.885062 0.162041 50% 0.424629 0.627577 0.683909 0.301102 75% 1.081000 1.493399 1.983415 0.859564 max 1.348160 1.794114 2.124072 1.046581
Sorting by an axis:
In [19]: df.sort_index(axis=1, ascending=False).execute() Out[19]: D C B A 2013-01-01 1.046581 -2.133356 1.794114 0.704246 2013-01-02 -0.647618 2.124072 0.369636 -0.372697 2013-01-03 0.166788 1.800061 0.885519 1.206585 2013-01-04 0.435416 2.044533 1.696025 0.145012 2013-01-05 0.160458 -1.036002 -0.280187 1.348160 2013-01-06 1.000946 -0.432243 -0.133233 -1.172302
Sorting by values:
In [20]: df.sort_values(by='B').execute() Out[20]: A B C D 2013-01-05 1.348160 -0.280187 -1.036002 0.160458 2013-01-06 -1.172302 -0.133233 -0.432243 1.000946 2013-01-02 -0.372697 0.369636 2.124072 -0.647618 2013-01-03 1.206585 0.885519 1.800061 0.166788 2013-01-04 0.145012 1.696025 2.044533 0.435416 2013-01-01 0.704246 1.794114 -2.133356 1.046581
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 0.704246 2013-01-02 -0.372697 2013-01-03 1.206585 2013-01-04 0.145012 2013-01-05 1.348160 2013-01-06 -1.172302 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 0.704246 1.794114 -2.133356 1.046581 2013-01-02 -0.372697 0.369636 2.124072 -0.647618 2013-01-03 1.206585 0.885519 1.800061 0.166788 In [23]: df['20130102':'20130104'].execute() Out[23]: A B C D 2013-01-02 -0.372697 0.369636 2.124072 -0.647618 2013-01-03 1.206585 0.885519 1.800061 0.166788 2013-01-04 0.145012 1.696025 2.044533 0.435416
For getting a cross section using a label:
In [24]: df.loc['20130101'].execute() Out[24]: A 0.704246 B 1.794114 C -2.133356 D 1.046581 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 0.704246 1.794114 2013-01-02 -0.372697 0.369636 2013-01-03 1.206585 0.885519 2013-01-04 0.145012 1.696025 2013-01-05 1.348160 -0.280187 2013-01-06 -1.172302 -0.133233
Showing label slicing, both endpoints are included:
In [26]: df.loc['20130102':'20130104', ['A', 'B']].execute() Out[26]: A B 2013-01-02 -0.372697 0.369636 2013-01-03 1.206585 0.885519 2013-01-04 0.145012 1.696025
Reduction in the dimensions of the returned object:
In [27]: df.loc['20130102', ['A', 'B']].execute() Out[27]: A -0.372697 B 0.369636 Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [28]: df.loc['20130101', 'A'].execute() Out[28]: 0.7042456149386982
For getting fast access to a scalar (equivalent to the prior method):
In [29]: df.at['20130101', 'A'].execute() Out[29]: 0.7042456149386982
Select via the position of the passed integers:
In [30]: df.iloc[3].execute() Out[30]: A 0.145012 B 1.696025 C 2.044533 D 0.435416 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 0.145012 1.696025 2013-01-05 1.348160 -0.280187
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 -0.372697 2.124072 2013-01-03 1.206585 1.800061 2013-01-05 1.348160 -1.036002
For slicing rows explicitly:
In [33]: df.iloc[1:3, :].execute() Out[33]: A B C D 2013-01-02 -0.372697 0.369636 2.124072 -0.647618 2013-01-03 1.206585 0.885519 1.800061 0.166788
For slicing columns explicitly:
In [34]: df.iloc[:, 1:3].execute() Out[34]: B C 2013-01-01 1.794114 -2.133356 2013-01-02 0.369636 2.124072 2013-01-03 0.885519 1.800061 2013-01-04 1.696025 2.044533 2013-01-05 -0.280187 -1.036002 2013-01-06 -0.133233 -0.432243
For getting a value explicitly:
In [35]: df.iloc[1, 1].execute() Out[35]: 0.369635758922324
In [36]: df.iat[1, 1].execute() Out[36]: 0.369635758922324
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 0.704246 1.794114 -2.133356 1.046581 2013-01-03 1.206585 0.885519 1.800061 0.166788 2013-01-04 0.145012 1.696025 2.044533 0.435416 2013-01-05 1.348160 -0.280187 -1.036002 0.160458
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 0.704246 1.794114 NaN 1.046581 2013-01-02 NaN 0.369636 2.124072 NaN 2013-01-03 1.206585 0.885519 1.800061 0.166788 2013-01-04 0.145012 1.696025 2.044533 0.435416 2013-01-05 1.348160 NaN NaN 0.160458 2013-01-06 NaN NaN NaN 1.000946
Operations in general exclude missing data.
Performing a descriptive statistic:
In [39]: df.mean().execute() Out[39]: A 0.309834 B 0.721979 C 0.394511 D 0.360429 dtype: float64
Same operation on the other axis:
In [40]: df.mean(1).execute() Out[40]: 2013-01-01 0.352896 2013-01-02 0.368348 2013-01-03 1.014738 2013-01-04 1.080247 2013-01-05 0.048107 2013-01-06 -0.184208 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 0.206585 -0.114481 0.800061 -0.833212 2013-01-04 -2.854988 -1.303975 -0.955467 -2.564584 2013-01-05 -3.651840 -5.280187 -6.036002 -4.839542 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 2.520461 B 2.074301 C 4.257428 D 1.694199 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 -1.157724 -0.189187 1.146274 -0.711210 1 -0.181547 -1.216801 -1.538909 0.432104 2 -0.539534 -0.334773 0.145556 0.435950 3 -0.915676 0.223804 1.414803 0.175416 4 -2.361754 -0.443951 -0.628845 -0.950372 5 -0.495911 0.079019 0.703715 0.817838 6 -0.827810 0.058145 1.293824 -1.176750 7 -0.258268 0.250121 0.419353 -0.869956 8 1.020997 0.802142 0.778613 -0.677730 9 -0.877974 -0.193732 0.272148 -0.143335 # 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 -1.157724 -0.189187 1.146274 -0.711210 1 -0.181547 -1.216801 -1.538909 0.432104 2 -0.539534 -0.334773 0.145556 0.435950 3 -0.915676 0.223804 1.414803 0.175416 4 -2.361754 -0.443951 -0.628845 -0.950372 5 -0.495911 0.079019 0.703715 0.817838 6 -0.827810 0.058145 1.293824 -1.176750 7 -0.258268 0.250121 0.419353 -0.869956 8 1.020997 0.802142 0.778613 -0.677730 9 -0.877974 -0.193732 0.272148 -0.143335
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 -1.150165 0.638342 1 bar one 2.093781 -0.430106 2 foo two -0.675295 -0.155558 3 bar three -0.202238 -0.482107 4 foo two -0.276685 0.159493 5 bar two -0.535216 -0.180477 6 foo one -0.856825 -1.418332 7 foo three 0.614378 -1.893399
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 1.356327 -1.092690 foo -2.344592 -2.669453
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 -2.006991 -0.779990 two -0.951980 0.003936 three 0.614378 -1.893399 bar one 2.093781 -0.430106 two -0.535216 -0.180477 three -0.202238 -0.482107
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 0x7faea4c3f5f8>
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 -0.748777 0.483233 0.122023 -0.213653 1 2000-01-02 -0.004817 -1.316996 0.211360 1.611840 2 2000-01-03 -0.099389 -1.921624 0.467984 1.260091 3 2000-01-04 -1.366523 -1.938647 0.084058 -0.293603 4 2000-01-05 -0.855157 -1.611363 1.021367 -1.343343 .. ... ... ... ... ... 995 2002-09-22 -16.907445 -31.699841 19.598046 -23.200662 996 2002-09-23 -18.927061 -32.042826 19.080499 -23.425057 997 2002-09-24 -16.930066 -31.171650 18.712998 -23.804921 998 2002-09-25 -17.608424 -31.682987 19.901356 -24.486571 999 2002-09-26 -17.118967 -30.709751 18.736074 -25.050285 [1000 rows x 5 columns]