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 0.411902 1.709468 -0.213158 0.821644
2013-01-02 -0.721910 -1.677311 -1.570986 -0.621969
2013-01-03 0.421083 -0.750191 0.269751 -2.799289
2013-01-04 -1.329158 1.274036 2.442691 -0.409725
2013-01-05 0.689205 -1.501951 0.363000 0.401498
2013-01-06 0.426947 -0.469598 -1.295293 -1.435165
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 0.411902 1.709468 -0.213158 0.821644
2013-01-02 -0.721910 -1.677311 -1.570986 -0.621969
2013-01-03 0.421083 -0.750191 0.269751 -2.799289
2013-01-04 -1.329158 1.274036 2.442691 -0.409725
2013-01-05 0.689205 -1.501951 0.363000 0.401498
In [15]: df.tail(3)
Out[15]:
A B C D
2013-01-04 -1.329158 1.274036 2.442691 -0.409725
2013-01-05 0.689205 -1.501951 0.363000 0.401498
2013-01-06 0.426947 -0.469598 -1.295293 -1.435165
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([[ 0.41190169, 1.70946816, -0.21315821, 0.82164367],
[-0.72191001, -1.67731119, -1.57098611, -0.62196894],
[ 0.42108334, -0.75019064, 0.26975121, -2.79928919],
[-1.32915794, 1.2740364 , 2.44269141, -0.40972548],
[ 0.68920499, -1.50195139, 0.36299995, 0.40149762],
[ 0.42694729, -0.46959787, -1.29529258, -1.43516459]])
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 -0.016988 -0.235924 -0.000666 -0.673834
std 0.811215 1.418734 1.439617 1.308619
min -1.329158 -1.677311 -1.570986 -2.799289
25% -0.438457 -1.314011 -1.024759 -1.231866
50% 0.416493 -0.609894 0.028296 -0.515847
75% 0.425481 0.838128 0.339688 0.198692
max 0.689205 1.709468 2.442691 0.821644
Sorting by an axis:
In [21]: df.sort_index(axis=1, ascending=False)
Out[21]:
D C B A
2013-01-01 0.821644 -0.213158 1.709468 0.411902
2013-01-02 -0.621969 -1.570986 -1.677311 -0.721910
2013-01-03 -2.799289 0.269751 -0.750191 0.421083
2013-01-04 -0.409725 2.442691 1.274036 -1.329158
2013-01-05 0.401498 0.363000 -1.501951 0.689205
2013-01-06 -1.435165 -1.295293 -0.469598 0.426947
Sorting by values:
In [22]: df.sort_values(by='B')
Out[22]:
A B C D
2013-01-02 -0.721910 -1.677311 -1.570986 -0.621969
2013-01-05 0.689205 -1.501951 0.363000 0.401498
2013-01-03 0.421083 -0.750191 0.269751 -2.799289
2013-01-06 0.426947 -0.469598 -1.295293 -1.435165
2013-01-04 -1.329158 1.274036 2.442691 -0.409725
2013-01-01 0.411902 1.709468 -0.213158 0.821644
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 0.411902
2013-01-02 -0.721910
2013-01-03 0.421083
2013-01-04 -1.329158
2013-01-05 0.689205
2013-01-06 0.426947
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 0.411902 1.709468 -0.213158 0.821644
2013-01-02 -0.721910 -1.677311 -1.570986 -0.621969
2013-01-03 0.421083 -0.750191 0.269751 -2.799289
In [25]: df['20130102':'20130104']
Out[25]:
A B C D
2013-01-02 -0.721910 -1.677311 -1.570986 -0.621969
2013-01-03 0.421083 -0.750191 0.269751 -2.799289
2013-01-04 -1.329158 1.274036 2.442691 -0.409725
Selection by label#
For getting a cross section using a label:
In [26]: df.loc['20130101']
Out[26]:
A 0.411902
B 1.709468
C -0.213158
D 0.821644
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 0.411902 1.709468
2013-01-02 -0.721910 -1.677311
2013-01-03 0.421083 -0.750191
2013-01-04 -1.329158 1.274036
2013-01-05 0.689205 -1.501951
2013-01-06 0.426947 -0.469598
Showing label slicing, both endpoints are included:
In [28]: df.loc['20130102':'20130104', ['A', 'B']]
Out[28]:
A B
2013-01-02 -0.721910 -1.677311
2013-01-03 0.421083 -0.750191
2013-01-04 -1.329158 1.274036
Reduction in the dimensions of the returned object:
In [29]: df.loc['20130102', ['A', 'B']]
Out[29]:
A -0.721910
B -1.677311
Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [30]: df.loc['20130101', 'A']
Out[30]: 0.41190169091385387
For getting fast access to a scalar (equivalent to the prior method):
In [31]: df.at['20130101', 'A']
Out[31]: 0.41190169091385387
Selection by position#
Select via the position of the passed integers:
In [32]: df.iloc[3]
Out[32]:
A -1.329158
B 1.274036
C 2.442691
D -0.409725
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 -1.329158 1.274036
2013-01-05 0.689205 -1.501951
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 -0.721910 -1.570986
2013-01-03 0.421083 0.269751
2013-01-05 0.689205 0.363000
For slicing rows explicitly:
In [35]: df.iloc[1:3, :]
Out[35]:
A B C D
2013-01-02 -0.721910 -1.677311 -1.570986 -0.621969
2013-01-03 0.421083 -0.750191 0.269751 -2.799289
For slicing columns explicitly:
In [36]: df.iloc[:, 1:3]
Out[36]:
B C
2013-01-01 1.709468 -0.213158
2013-01-02 -1.677311 -1.570986
2013-01-03 -0.750191 0.269751
2013-01-04 1.274036 2.442691
2013-01-05 -1.501951 0.363000
2013-01-06 -0.469598 -1.295293
For getting a value explicitly:
In [37]: df.iloc[1, 1]
Out[37]: -1.6773111933012679
For getting fast access to a scalar (equivalent to the prior method):
In [38]: df.iat[1, 1]
Out[38]: -1.6773111933012679
Boolean indexing#
Using a single column’s values to select data.
In [39]: df[df['A'] > 0]
Out[39]:
A B C D
2013-01-01 0.411902 1.709468 -0.213158 0.821644
2013-01-03 0.421083 -0.750191 0.269751 -2.799289
2013-01-05 0.689205 -1.501951 0.363000 0.401498
2013-01-06 0.426947 -0.469598 -1.295293 -1.435165
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 0.411902 1.709468 NaN 0.821644
2013-01-02 NaN NaN NaN NaN
2013-01-03 0.421083 NaN 0.269751 NaN
2013-01-04 NaN 1.274036 2.442691 NaN
2013-01-05 0.689205 NaN 0.363000 0.401498
2013-01-06 0.426947 NaN NaN NaN
Operations#
Stats#
Operations in general exclude missing data.
Performing a descriptive statistic:
In [41]: df.mean()
Out[41]:
A -0.016988
B -0.235924
C -0.000666
D -0.673834
dtype: float64
Same operation on the other axis:
In [42]: df.mean(1)
Out[42]:
2013-01-01 0.682464
2013-01-02 -1.148044
2013-01-03 -0.714661
2013-01-04 0.494461
2013-01-05 -0.012062
2013-01-06 -0.693277
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 -0.578917 -1.750191 -0.730249 -3.799289
2013-01-04 -4.329158 -1.725964 -0.557309 -3.409725
2013-01-05 -4.310795 -6.501951 -4.637000 -4.598502
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 2.018363
B 3.386779
C 4.013678
D 3.620933
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 -0.495508 0.903802 2.152979 1.098698
1 -0.327001 -0.586382 1.999350 -1.056401
2 0.341923 -0.024582 0.439198 0.662602
3 -1.896886 0.181549 0.119640 -1.426697
4 -2.407668 -0.780552 -1.301063 0.510010
5 -0.350738 -0.147771 -0.566869 -2.414299
6 -1.994935 -0.486425 -0.531758 1.624540
7 -0.358207 -0.884470 1.257721 0.587503
8 -0.945414 -1.055967 1.334790 0.817954
9 1.116094 -0.664818 -0.298791 0.042105
# 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 -0.495508 0.903802 2.152979 1.098698
1 -0.327001 -0.586382 1.999350 -1.056401
2 0.341923 -0.024582 0.439198 0.662602
3 -1.896886 0.181549 0.119640 -1.426697
4 -2.407668 -0.780552 -1.301063 0.510010
5 -0.350738 -0.147771 -0.566869 -2.414299
6 -1.994935 -0.486425 -0.531758 1.624540
7 -0.358207 -0.884470 1.257721 0.587503
8 -0.945414 -1.055967 1.334790 0.817954
9 1.116094 -0.664818 -0.298791 0.042105
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 -0.473456 1.016378
1 bar one 0.373591 0.480215
2 foo two -0.538622 -0.490436
3 bar three -1.833243 -1.471246
4 foo two -0.083388 1.389476
5 bar two 0.874384 2.006862
6 foo one -0.968538 -1.703000
7 foo three -1.840837 0.066493
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 -0.585268 1.015831
foo onetwotwoonethree -3.904840 0.278910
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 0.373591 0.480215
three -1.833243 -1.471246
two 0.874384 2.006862
foo one -1.441994 -0.686622
three -1.840837 0.066493
two -0.622010 0.899039
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 0x7f523c12ae50>
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 0.385646 1.201584 -1.701511 -0.693112
1 2000-01-02 0.331648 -0.203431 -1.030354 -0.045550
2 2000-01-03 0.112350 0.024239 -0.690759 -1.354678
3 2000-01-04 -0.492772 -1.407550 0.535260 -0.030373
4 2000-01-05 -0.557673 0.116826 2.127525 -0.835155
.. ... ... ... ... ...
995 2002-09-22 6.795263 15.514409 -8.909048 -43.613612
996 2002-09-23 5.241447 15.386009 -9.248272 -43.035980
997 2002-09-24 2.541217 14.514584 -9.051257 -43.824801
998 2002-09-25 1.450811 14.913616 -9.681888 -42.579596
999 2002-09-26 1.895067 16.139412 -8.192430 -42.140289
[1000 rows x 5 columns]