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 0.224446 -1.164657 1.515325 -0.553190
2013-01-02 -0.490051 0.065176 -0.364318 -0.729461
2013-01-03 -0.561802 -2.508903 -0.540759 -1.184117
2013-01-04 -1.266986 1.598020 -0.151260 -1.528504
2013-01-05 0.057634 0.830278 -0.654922 1.041385
2013-01-06 0.674938 0.021462 -1.439848 1.046305
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[ns]
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 0.224446 -1.164657 1.515325 -0.553190
2013-01-02 -0.490051 0.065176 -0.364318 -0.729461
2013-01-03 -0.561802 -2.508903 -0.540759 -1.184117
2013-01-04 -1.266986 1.598020 -0.151260 -1.528504
2013-01-05 0.057634 0.830278 -0.654922 1.041385
In [15]: df.tail(3)
Out[15]:
A B C D
2013-01-04 -1.266986 1.598020 -0.151260 -1.528504
2013-01-05 0.057634 0.830278 -0.654922 1.041385
2013-01-06 0.674938 0.021462 -1.439848 1.046305
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([[ 0.22444615, -1.16465735, 1.51532458, -0.55318981],
[-0.4900508 , 0.06517642, -0.36431766, -0.7294606 ],
[-0.56180194, -2.5089035 , -0.54075921, -1.18411743],
[-1.26698561, 1.59801965, -0.15125973, -1.52850352],
[ 0.05763376, 0.83027825, -0.654922 , 1.04138489],
[ 0.67493784, 0.02146243, -1.43984801, 1.04630517]])
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 -0.226970 -0.193104 -0.272630 -0.317930
std 0.687193 1.461166 0.979832 1.108995
min -1.266986 -2.508903 -1.439848 -1.528504
25% -0.543864 -0.868127 -0.626381 -1.070453
50% -0.216209 0.043319 -0.452538 -0.641325
75% 0.182743 0.639003 -0.204524 0.642741
max 0.674938 1.598020 1.515325 1.046305
Sorting by an axis:
In [21]: df.sort_index(axis=1, ascending=False)
Out[21]:
D C B A
2013-01-01 -0.553190 1.515325 -1.164657 0.224446
2013-01-02 -0.729461 -0.364318 0.065176 -0.490051
2013-01-03 -1.184117 -0.540759 -2.508903 -0.561802
2013-01-04 -1.528504 -0.151260 1.598020 -1.266986
2013-01-05 1.041385 -0.654922 0.830278 0.057634
2013-01-06 1.046305 -1.439848 0.021462 0.674938
Sorting by values:
In [22]: df.sort_values(by='B')
Out[22]:
A B C D
2013-01-03 -0.561802 -2.508903 -0.540759 -1.184117
2013-01-01 0.224446 -1.164657 1.515325 -0.553190
2013-01-06 0.674938 0.021462 -1.439848 1.046305
2013-01-02 -0.490051 0.065176 -0.364318 -0.729461
2013-01-05 0.057634 0.830278 -0.654922 1.041385
2013-01-04 -1.266986 1.598020 -0.151260 -1.528504
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 0.224446
2013-01-02 -0.490051
2013-01-03 -0.561802
2013-01-04 -1.266986
2013-01-05 0.057634
2013-01-06 0.674938
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 0.224446 -1.164657 1.515325 -0.553190
2013-01-02 -0.490051 0.065176 -0.364318 -0.729461
2013-01-03 -0.561802 -2.508903 -0.540759 -1.184117
In [25]: df['20130102':'20130104']
Out[25]:
A B C D
2013-01-02 -0.490051 0.065176 -0.364318 -0.729461
2013-01-03 -0.561802 -2.508903 -0.540759 -1.184117
2013-01-04 -1.266986 1.598020 -0.151260 -1.528504
Selection by label#
For getting a cross section using a label:
In [26]: df.loc['20130101']
Out[26]:
A 0.224446
B -1.164657
C 1.515325
D -0.553190
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 0.224446 -1.164657
2013-01-02 -0.490051 0.065176
2013-01-03 -0.561802 -2.508903
2013-01-04 -1.266986 1.598020
2013-01-05 0.057634 0.830278
2013-01-06 0.674938 0.021462
Showing label slicing, both endpoints are included:
In [28]: df.loc['20130102':'20130104', ['A', 'B']]
Out[28]:
A B
2013-01-02 -0.490051 0.065176
2013-01-03 -0.561802 -2.508903
2013-01-04 -1.266986 1.598020
Reduction in the dimensions of the returned object:
In [29]: df.loc['20130102', ['A', 'B']]
Out[29]:
A -0.490051
B 0.065176
Name: 2013-01-02 00:00:00, dtype: float64
For getting a scalar value:
In [30]: df.loc['20130101', 'A']
Out[30]: 0.22444615122186354
For getting fast access to a scalar (equivalent to the prior method):
In [31]: df.at['20130101', 'A']
Out[31]: 0.22444615122186354
Selection by position#
Select via the position of the passed integers:
In [32]: df.iloc[3]
Out[32]:
A -1.266986
B 1.598020
C -0.151260
D -1.528504
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 -1.266986 1.598020
2013-01-05 0.057634 0.830278
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 -0.490051 -0.364318
2013-01-03 -0.561802 -0.540759
2013-01-05 0.057634 -0.654922
For slicing rows explicitly:
In [35]: df.iloc[1:3, :]
Out[35]:
A B C D
2013-01-02 -0.490051 0.065176 -0.364318 -0.729461
2013-01-03 -0.561802 -2.508903 -0.540759 -1.184117
For slicing columns explicitly:
In [36]: df.iloc[:, 1:3]
Out[36]:
B C
2013-01-01 -1.164657 1.515325
2013-01-02 0.065176 -0.364318
2013-01-03 -2.508903 -0.540759
2013-01-04 1.598020 -0.151260
2013-01-05 0.830278 -0.654922
2013-01-06 0.021462 -1.439848
For getting a value explicitly:
In [37]: df.iloc[1, 1]
Out[37]: 0.06517641626685941
For getting fast access to a scalar (equivalent to the prior method):
In [38]: df.iat[1, 1]
Out[38]: 0.06517641626685941
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 0.224446 -1.164657 1.515325 -0.553190
2013-01-05 0.057634 0.830278 -0.654922 1.041385
2013-01-06 0.674938 0.021462 -1.439848 1.046305
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 0.224446 NaN 1.515325 NaN
2013-01-02 NaN 0.065176 NaN NaN
2013-01-03 NaN NaN NaN NaN
2013-01-04 NaN 1.598020 NaN NaN
2013-01-05 0.057634 0.830278 NaN 1.041385
2013-01-06 0.674938 0.021462 NaN 1.046305
Operations#
Stats#
Operations in general exclude missing data.
Performing a descriptive statistic:
In [41]: df.mean()
Out[41]:
A -0.226970
B -0.193104
C -0.272630
D -0.317930
dtype: float64
Same operation on the other axis:
In [42]: df.mean(1)
Out[42]:
2013-01-01 0.005481
2013-01-02 -0.379663
2013-01-03 -1.198896
2013-01-04 -0.337182
2013-01-05 0.318594
2013-01-06 0.075714
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 -1.561802 -3.508903 -1.540759 -2.184117
2013-01-04 -4.266986 -1.401980 -3.151260 -4.528504
2013-01-05 -4.942366 -4.169722 -5.654922 -3.958615
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 1.941923
B 4.106923
C 2.955173
D 2.574809
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 -0.630711 -1.055283 0.700147 -1.388102
1 0.890356 -1.709177 0.078013 0.607967
2 1.873063 0.569527 -2.301107 -0.740584
3 -0.360962 2.270846 1.031973 -0.476972
4 2.418310 -0.686323 -1.147042 -0.292606
5 1.089239 0.343140 -0.156534 -0.349143
6 0.742573 -1.403164 0.381083 -1.586600
7 -1.108405 -0.700347 -0.433201 -0.261067
8 -1.478137 0.795965 0.778158 -0.998803
9 -1.553243 0.210058 -2.481455 -0.581690
# 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 -0.630711 -1.055283 0.700147 -1.388102
1 0.890356 -1.709177 0.078013 0.607967
2 1.873063 0.569527 -2.301107 -0.740584
3 -0.360962 2.270846 1.031973 -0.476972
4 2.418310 -0.686323 -1.147042 -0.292606
5 1.089239 0.343140 -0.156534 -0.349143
6 0.742573 -1.403164 0.381083 -1.586600
7 -1.108405 -0.700347 -0.433201 -0.261067
8 -1.478137 0.795965 0.778158 -0.998803
9 -1.553243 0.210058 -2.481455 -0.581690
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 0.224976 -0.990328
1 bar one 0.740208 -0.199152
2 foo two -0.693955 1.684258
3 bar three -0.658850 0.431106
4 foo two 1.023329 -0.316393
5 bar two 1.409466 1.184248
6 foo one 1.429536 1.139899
7 foo three 1.271735 0.270696
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 1.490823 1.416202
foo onetwotwoonethree 3.255622 1.788130
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 0.740208 -0.199152
three -0.658850 0.431106
two 1.409466 1.184248
foo one 1.654512 0.149571
three 1.271735 0.270696
two 0.329375 1.367864
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 0x7f068e01ce50>
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 1.178291 0.364987 -0.840177 -0.332956
1 2000-01-02 2.057435 0.296857 -0.003532 -1.758008
2 2000-01-03 0.953813 0.271327 -0.756990 -1.198440
3 2000-01-04 0.589310 -0.392329 0.681356 -1.951529
4 2000-01-05 0.904952 1.599681 1.959410 -3.059426
.. ... ... ... ... ...
995 2002-09-22 -12.510332 39.213884 26.434486 59.426481
996 2002-09-23 -13.653644 38.167130 25.543169 58.687596
997 2002-09-24 -16.472875 37.242324 24.860751 56.588386
998 2002-09-25 -15.509081 37.077324 24.493449 56.980079
999 2002-09-26 -16.018922 36.578095 26.079306 57.032693
[1000 rows x 5 columns]