10 minutes to xorbits.pandas#
This is a short introduction to xorbits.pandas which is originated from pandas’ quickstart.
Customarily, we import and init as follows:
In [1]: import xorbits
In [2]: import xorbits.numpy as np
In [3]: import xorbits.pandas as pd
In [4]: xorbits.init()
Object creation#
Creating a Series by passing a list of values, letting it create a default integer index:
In [5]: s = pd.Series([1, 3, 5, np.nan, 6, 8])
In [6]: s
Out[6]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
Creating a DataFrame by passing an array, with a datetime index and labeled columns:
In [7]: dates = pd.date_range('20130101', periods=6)
In [8]: dates
Out[8]:
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 [9]: df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
In [10]: df
Out[10]:
A B C D
2013-01-01 -0.895370 2.126507 0.630915 1.413850
2013-01-02 -0.171440 -2.010562 -0.353420 0.429268
2013-01-03 0.142932 0.468562 0.218185 -0.220462
2013-01-04 0.943198 0.313965 1.366018 0.965330
2013-01-05 0.608089 1.220280 1.379540 -1.271964
2013-01-06 0.189339 -0.450788 0.504877 -2.264019
Creating a DataFrame by passing a dict of objects that can be converted to series-like.
In [11]: df2 = pd.DataFrame({'A': 1.,
....: 'B': pd.Timestamp('20130102'),
....: 'C': pd.Series(1, index=list(range(4)), dtype='float32'),
....: 'D': np.array([3] * 4, dtype='int32'),
....: 'E': 'foo'})
....:
In [12]: df2
Out[12]:
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 [13]: df2.dtypes
Out[13]:
A float64
B datetime64[s]
C float32
D int32
E object
dtype: object
Viewing data#
Here is how to view the top and bottom rows of the frame:
In [14]: df.head()
Out[14]:
A B C D
2013-01-01 -0.895370 2.126507 0.630915 1.413850
2013-01-02 -0.171440 -2.010562 -0.353420 0.429268
2013-01-03 0.142932 0.468562 0.218185 -0.220462
2013-01-04 0.943198 0.313965 1.366018 0.965330
2013-01-05 0.608089 1.220280 1.379540 -1.271964
In [15]: df.tail(3)
Out[15]:
A B C D
2013-01-04 0.943198 0.313965 1.366018 0.965330
2013-01-05 0.608089 1.220280 1.379540 -1.271964
2013-01-06 0.189339 -0.450788 0.504877 -2.264019
Display the index, columns:
In [16]: df.index
Out[16]:
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 [17]: df.columns
Out[17]: Index(['A', 'B', 'C', 'D'], dtype='object')
DataFrame.to_numpy() gives a ndarray 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 ndarray: ndarrays have one
dtype for the entire ndarray, while DataFrames have one dtype per column. When you call
DataFrame.to_numpy(), xorbits.pandas will find the ndarray 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.
For df, our DataFrame of all floating-point values,
DataFrame.to_numpy() is fast and doesn’t require copying data.
In [18]: df.to_numpy()
Out[18]:
array([[-0.89536986, 2.12650669, 0.63091484, 1.41384962],
[-0.17143963, -2.01056185, -0.35342023, 0.42926772],
[ 0.14293214, 0.46856241, 0.21818546, -0.22046165],
[ 0.94319762, 0.31396544, 1.36601814, 0.96533004],
[ 0.60808868, 1.2202805 , 1.37953977, -1.27196401],
[ 0.18933859, -0.45078809, 0.50487679, -2.26401882]])
For df2, the DataFrame with multiple dtypes, DataFrame.to_numpy() is relatively
expensive.
In [19]: df2.to_numpy()
Out[19]:
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_numpy() does not include the index or column
labels in the output.
describe() shows a quick statistic summary of your data:
In [20]: df.describe()
Out[20]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.136125 0.277994 0.624352 -0.158000
std 0.638055 1.422050 0.671670 1.396968
min -0.895370 -2.010562 -0.353420 -2.264019
25% -0.092847 -0.259600 0.289858 -1.009088
50% 0.166135 0.391264 0.567896 0.104403
75% 0.503401 1.032351 1.182242 0.831314
max 0.943198 2.126507 1.379540 1.413850
Sorting by an axis:
In [21]: df.sort_index(axis=1, ascending=False)
Out[21]:
D C B A
2013-01-01 1.413850 0.630915 2.126507 -0.895370
2013-01-02 0.429268 -0.353420 -2.010562 -0.171440
2013-01-03 -0.220462 0.218185 0.468562 0.142932
2013-01-04 0.965330 1.366018 0.313965 0.943198
2013-01-05 -1.271964 1.379540 1.220280 0.608089
2013-01-06 -2.264019 0.504877 -0.450788 0.189339
Sorting by values:
In [22]: df.sort_values(by='B')
Out[22]:
A B C D
2013-01-02 -0.171440 -2.010562 -0.353420 0.429268
2013-01-06 0.189339 -0.450788 0.504877 -2.264019
2013-01-04 0.943198 0.313965 1.366018 0.965330
2013-01-03 0.142932 0.468562 0.218185 -0.220462
2013-01-05 0.608089 1.220280 1.379540 -1.271964
2013-01-01 -0.895370 2.126507 0.630915 1.413850
Selection#
Note
While standard Python expressions for selecting and setting are
intuitive and come in handy for interactive work, for production code, we
recommend the optimized xorbits.pandas data access methods, .at, .iat,
.loc and .iloc.
Getting#
Selecting a single column, which yields a Series, equivalent to df.A:
In [23]: df['A']
Out[23]:
2013-01-01 -0.895370
2013-01-02 -0.171440
2013-01-03 0.142932
2013-01-04 0.943198
2013-01-05 0.608089
2013-01-06 0.189339
Freq: D, Name: A, dtype: float64
Selecting via [], which slices the rows:
In [24]: df[0:3]
Out[24]:
A B C D
2013-01-01 -0.895370 2.126507 0.630915 1.413850
2013-01-02 -0.171440 -2.010562 -0.353420 0.429268
2013-01-03 0.142932 0.468562 0.218185 -0.220462
In [25]: df['20130102':'20130104']
Out[25]:
A B C D
2013-01-02 -0.171440 -2.010562 -0.353420 0.429268
2013-01-03 0.142932 0.468562 0.218185 -0.220462
2013-01-04 0.943198 0.313965 1.366018 0.965330
Selection by label#
For getting a cross section using a label:
In [26]: df.loc['20130101']
Out[26]:
A -0.895370
B 2.126507
C 0.630915
D 1.413850
Name: 2013-01-01 00:00:00, dtype: float64
Selecting on a multi-axis by label:
In [27]: df.loc[:, ['A', 'B']]
Out[27]:
A B
2013-01-01 -0.895370 2.126507
2013-01-02 -0.171440 -2.010562
2013-01-03 0.142932 0.468562
2013-01-04 0.943198 0.313965
2013-01-05 0.608089 1.220280
2013-01-06 0.189339 -0.450788
Showing label slicing, both endpoints are included:
In [28]: df.loc['20130102':'20130104', ['A', 'B']]
Out[28]:
A B
2013-01-02 -0.171440 -2.010562
2013-01-03 0.142932 0.468562
2013-01-04 0.943198 0.313965
Reduction in the dimensions of the returned object:
In [29]: df.loc['20130102', ['A', 'B']]
Out[29]:
A -0.171440
B -2.010562
Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [30]: df.loc['20130101', 'A']
Out[30]: -0.8953698607067291
For getting fast access to a scalar (equivalent to the prior method):
In [31]: df.at['20130101', 'A']
Out[31]: -0.8953698607067291
Selection by position#
Select via the position of the passed integers:
In [32]: df.iloc[3]
Out[32]:
A 0.943198
B 0.313965
C 1.366018
D 0.965330
Name: 2013-01-04 00:00:00, dtype: float64
By integer slices, acting similar to python:
In [33]: df.iloc[3:5, 0:2]
Out[33]:
A B
2013-01-04 0.943198 0.313965
2013-01-05 0.608089 1.220280
By lists of integer position locations, similar to the python style:
In [34]: df.iloc[[1, 2, 4], [0, 2]]
Out[34]:
A C
2013-01-02 -0.171440 -0.353420
2013-01-03 0.142932 0.218185
2013-01-05 0.608089 1.379540
For slicing rows explicitly:
In [35]: df.iloc[1:3, :]
Out[35]:
A B C D
2013-01-02 -0.171440 -2.010562 -0.353420 0.429268
2013-01-03 0.142932 0.468562 0.218185 -0.220462
For slicing columns explicitly:
In [36]: df.iloc[:, 1:3]
Out[36]:
B C
2013-01-01 2.126507 0.630915
2013-01-02 -2.010562 -0.353420
2013-01-03 0.468562 0.218185
2013-01-04 0.313965 1.366018
2013-01-05 1.220280 1.379540
2013-01-06 -0.450788 0.504877
For getting a value explicitly:
In [37]: df.iloc[1, 1]
Out[37]: -2.010561845234837
For getting fast access to a scalar (equivalent to the prior method):
In [38]: df.iat[1, 1]
Out[38]: -2.010561845234837
Boolean indexing#
Using a single column’s values to select data.
In [39]: df[df['A'] > 0]
Out[39]:
A B C D
2013-01-03 0.142932 0.468562 0.218185 -0.220462
2013-01-04 0.943198 0.313965 1.366018 0.965330
2013-01-05 0.608089 1.220280 1.379540 -1.271964
2013-01-06 0.189339 -0.450788 0.504877 -2.264019
Selecting values from a DataFrame where a boolean condition is met.
In [40]: df[df > 0]
Out[40]:
A B C D
2013-01-01 NaN 2.126507 0.630915 1.413850
2013-01-02 NaN NaN NaN 0.429268
2013-01-03 0.142932 0.468562 0.218185 NaN
2013-01-04 0.943198 0.313965 1.366018 0.965330
2013-01-05 0.608089 1.220280 1.379540 NaN
2013-01-06 0.189339 NaN 0.504877 NaN
Operations#
Stats#
Operations in general exclude missing data.
Performing a descriptive statistic:
In [41]: df.mean()
Out[41]:
A 0.136125
B 0.277994
C 0.624352
D -0.158000
dtype: float64
Same operation on the other axis:
In [42]: df.mean(1)
Out[42]:
2013-01-01 0.818975
2013-01-02 -0.526538
2013-01-03 0.152305
2013-01-04 0.897128
2013-01-05 0.483986
2013-01-06 -0.505148
Freq: D, dtype: float64
Operating with objects that have different dimensionality and need alignment. In addition,
xorbits.pandas automatically broadcasts along the specified dimension.
In [43]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2)
In [44]: s
Out[44]:
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 [45]: df.sub(s, axis='index')
Out[45]:
A B C D
2013-01-01 NaN NaN NaN NaN
2013-01-02 NaN NaN NaN NaN
2013-01-03 -0.857068 -0.531438 -0.781815 -1.220462
2013-01-04 -2.056802 -2.686035 -1.633982 -2.034670
2013-01-05 -4.391911 -3.779720 -3.620460 -6.271964
2013-01-06 NaN NaN NaN NaN
Apply#
Applying functions to the data:
In [46]: df.apply(lambda x: x.max() - x.min())
Out[46]:
A 1.838567
B 4.137069
C 1.732960
D 3.677868
dtype: float64
String Methods#
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).
In [47]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
In [48]: s.str.lower()
Out[48]:
0 a
1 b
2 c
3 aaba
4 baca
5 NaN
6 caba
7 dog
8 cat
dtype: object
Merge#
Concat#
xorbits.pandas 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 xorbits.pandas objects together with concat():
In [49]: df = pd.DataFrame(np.random.randn(10, 4))
In [50]: df
Out[50]:
0 1 2 3
0 -2.222102 0.089420 0.444085 0.497213
1 -2.419492 0.274049 -0.304995 -1.175190
2 -0.067044 -0.525800 -1.264446 0.960053
3 -0.143600 -0.340298 0.385199 -1.351128
4 2.121553 0.623916 0.647778 -0.646439
5 -1.113658 -2.215303 -0.083893 0.259849
6 -0.182575 0.161977 1.115416 0.809280
7 1.028048 0.685183 -0.740458 1.099633
8 0.026282 0.749932 0.451062 1.211574
9 0.635577 0.958059 -0.381735 1.822400
# break it into pieces
In [51]: pieces = [df[:3], df[3:7], df[7:]]
In [52]: pd.concat(pieces)
Out[52]:
0 1 2 3
0 -2.222102 0.089420 0.444085 0.497213
1 -2.419492 0.274049 -0.304995 -1.175190
2 -0.067044 -0.525800 -1.264446 0.960053
3 -0.143600 -0.340298 0.385199 -1.351128
4 2.121553 0.623916 0.647778 -0.646439
5 -1.113658 -2.215303 -0.083893 0.259849
6 -0.182575 0.161977 1.115416 0.809280
7 1.028048 0.685183 -0.740458 1.099633
8 0.026282 0.749932 0.451062 1.211574
9 0.635577 0.958059 -0.381735 1.822400
Join#
SQL style merges.
In [53]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
In [54]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
In [55]: left
Out[55]:
key lval
0 foo 1
1 foo 2
In [56]: right
Out[56]:
key rval
0 foo 4
1 foo 5
In [57]: pd.merge(left, right, on='key')
Out[57]:
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 [58]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
In [59]: right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
In [60]: left
Out[60]:
key lval
0 foo 1
1 bar 2
In [61]: right
Out[61]:
key rval
0 foo 4
1 bar 5
In [62]: pd.merge(left, right, on='key')
Out[62]:
key lval rval
0 foo 1 4
1 bar 2 5
Grouping#
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
In [63]: df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
....: 'foo', 'bar', 'foo', 'foo'],
....: 'B': ['one', 'one', 'two', 'three',
....: 'two', 'two', 'one', 'three'],
....: 'C': np.random.randn(8),
....: 'D': np.random.randn(8)})
....:
In [64]: df
Out[64]:
A B C D
0 foo one -0.006975 -0.563461
1 bar one -1.596581 -0.019810
2 foo two -0.079158 0.409955
3 bar three -0.376174 0.189913
4 foo two -1.685014 -0.473401
5 bar two -0.317097 0.925201
6 foo one -0.674133 -0.987325
7 foo three -1.948111 0.476968
Grouping and then applying the sum() function to
the resulting groups.
In [65]: df.groupby('A').sum()
Out[65]:
B C D
A
bar onethreetwo -2.289852 1.095304
foo onetwotwoonethree -4.393390 -1.137264
Grouping by multiple columns forms a hierarchical index, and again we can apply the sum function.
In [66]: df.groupby(['A', 'B']).sum()
Out[66]:
C D
A B
bar one -1.596581 -0.019810
three -0.376174 0.189913
two -0.317097 0.925201
foo one -0.681107 -1.550786
three -1.948111 0.476968
two -1.764172 -0.063446
Plotting#
We use the standard convention for referencing the matplotlib API:
In [67]: import matplotlib.pyplot as plt
In [68]: plt.close('all')
In [69]: ts = pd.Series(np.random.randn(1000),
....: index=pd.date_range('1/1/2000', periods=1000))
....:
In [70]: ts = ts.cumsum()
In [71]: ts.plot()
Out[71]: <Axes: >
On a DataFrame, the plot() method is a convenience to plot all
of the columns with labels:
In [72]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
....: columns=['A', 'B', 'C', 'D'])
....:
In [73]: df = df.cumsum()
In [74]: plt.figure()
Out[74]: <Figure size 640x480 with 0 Axes>
In [75]: df.plot()
Out[75]: <Axes: >
In [76]: plt.legend(loc='best')
Out[76]: <matplotlib.legend.Legend at 0x7f6c45045fa0>
Getting data in/out#
CSV#
Writing to a csv file.
In [77]: df.to_csv('foo.csv')
Out[77]:
Empty DataFrame
Columns: []
Index: []
Reading from a csv file.
In [78]: pd.read_csv('foo.csv')
Out[78]:
Unnamed: 0 A B C D
0 2000-01-01 -0.771442 1.000700 0.878125 2.056192
1 2000-01-02 -0.693299 -0.224015 -0.576945 -0.247457
2 2000-01-03 0.392360 -1.431820 1.180861 1.259017
3 2000-01-04 2.264389 -0.085838 -0.307007 0.538263
4 2000-01-05 3.226983 0.824098 1.046063 -0.515959
.. ... ... ... ... ...
995 2002-09-22 27.254041 6.937970 18.681139 -18.979443
996 2002-09-23 27.339197 5.966404 19.230130 -19.203582
997 2002-09-24 28.336219 6.241372 17.442813 -19.330066
998 2002-09-25 29.551700 5.160639 17.915526 -18.414204
999 2002-09-26 28.983247 5.327610 18.110839 -17.842912
[1000 rows x 5 columns]