The axis labeling information in pandas objects serves many purposes:
Identifies data (i.e. provides metadata) using known indicators, important for analysis, visualization, and interactive console display.
Enables automatic and explicit data alignment.
Allows intuitive getting and setting of subsets of the data set.
In this section, we will focus on the final point: namely, how to slice, dice, and generally get and set subsets of pandas objects. The primary focus will be on Series and DataFrame as they have received more development attention in this area.
Note
The Python and NumPy indexing operators [] and attribute operator . provide quick and easy access to pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, we recommended that you take advantage of the optimized pandas data access methods exposed in this chapter.
[]
.
Warning
Whether a copy or a reference is returned for a setting operation, may depend on the context. This is sometimes called chained assignment and should be avoided. See Returning a View versus Copy.
chained assignment
See the MultiIndex / Advanced Indexing for MultiIndex and more advanced indexing documentation.
MultiIndex
See the cookbook for some advanced strategies.
Object selection has had a number of user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing.
.loc is primarily label based, but may also be used with a boolean array. .loc will raise KeyError when the items are not found. Allowed inputs are:
.loc
KeyError
A single label, e.g. 5 or 'a' (Note that 5 is interpreted as a label of the index. This use is not an integer position along the index.). A list or array of labels ['a', 'b', 'c']. A slice object with labels 'a':'f' (Note that contrary to usual python slices, both the start and the stop are included, when present in the index! See Slicing with labels and Endpoints are inclusive.) A boolean array A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).
A single label, e.g. 5 or 'a' (Note that 5 is interpreted as a label of the index. This use is not an integer position along the index.).
5
'a'
A list or array of labels ['a', 'b', 'c'].
['a', 'b', 'c']
A slice object with labels 'a':'f' (Note that contrary to usual python slices, both the start and the stop are included, when present in the index! See Slicing with labels and Endpoints are inclusive.)
'a':'f'
A boolean array
A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).
callable
See more at Selection by Label.
.iloc is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing. (this conforms with Python/NumPy slice semantics). Allowed inputs are:
.iloc
0
length-1
IndexError
An integer e.g. 5. A list or array of integers [4, 3, 0]. A slice object with ints 1:7. A boolean array. A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above).
An integer e.g. 5.
A list or array of integers [4, 3, 0].
[4, 3, 0]
A slice object with ints 1:7.
1:7
A boolean array.
See more at Selection by Position, Advanced Indexing and Advanced Hierarchical.
.loc, .iloc, and also [] indexing can accept a callable as indexer. See more at Selection By Callable.
Getting values from an object with multi-axes selection uses the following notation (using .loc as an example, but the following applies to .iloc as well). Any of the axes accessors may be the null slice :. Axes left out of the specification are assumed to be :, e.g. p.loc['a'] is equivalent to p.loc['a', :, :].
:
p.loc['a']
p.loc['a', :, :]
Object Type
Indexers
Series
s.loc[indexer]
DataFrame
df.loc[row_indexer,column_indexer]
As mentioned when introducing the data structures in the last section, the primary function of indexing with [] (a.k.a. __getitem__ for those familiar with implementing class behavior in Python) is selecting out lower-dimensional slices. The following table shows return type values when indexing pandas objects with []:
__getitem__
Selection
Return Value Type
series[label]
scalar value
frame[colname]
Series corresponding to colname
Here we construct a simple time series data set to use for illustrating the indexing functionality:
In [1]: dates = pd.date_range('1/1/2000', periods=8) In [2]: df = pd.DataFrame(np.random.randn(8, 4), ...: index=dates, columns=['A', 'B', 'C', 'D']) ...: In [3]: df Out[3]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112 -0.173215 0.119209 -1.044236 2000-01-03 -0.861849 -2.104569 -0.494929 1.071804 2000-01-04 0.721555 -0.706771 -1.039575 0.271860 2000-01-05 -0.424972 0.567020 0.276232 -1.087401 2000-01-06 -0.673690 0.113648 -1.478427 0.524988 2000-01-07 0.404705 0.577046 -1.715002 -1.039268 2000-01-08 -0.370647 -1.157892 -1.344312 0.844885
None of the indexing functionality is time series specific unless specifically stated.
Thus, as per above, we have the most basic indexing using []:
In [4]: s = df['A'] In [5]: s[dates[5]] Out[5]: -0.6736897080883706
You can pass a list of columns to [] to select columns in that order. If a column is not contained in the DataFrame, an exception will be raised. Multiple columns can also be set in this manner:
In [6]: df Out[6]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112 -0.173215 0.119209 -1.044236 2000-01-03 -0.861849 -2.104569 -0.494929 1.071804 2000-01-04 0.721555 -0.706771 -1.039575 0.271860 2000-01-05 -0.424972 0.567020 0.276232 -1.087401 2000-01-06 -0.673690 0.113648 -1.478427 0.524988 2000-01-07 0.404705 0.577046 -1.715002 -1.039268 2000-01-08 -0.370647 -1.157892 -1.344312 0.844885 In [7]: df[['B', 'A']] = df[['A', 'B']] In [8]: df Out[8]: A B C D 2000-01-01 -0.282863 0.469112 -1.509059 -1.135632 2000-01-02 -0.173215 1.212112 0.119209 -1.044236 2000-01-03 -2.104569 -0.861849 -0.494929 1.071804 2000-01-04 -0.706771 0.721555 -1.039575 0.271860 2000-01-05 0.567020 -0.424972 0.276232 -1.087401 2000-01-06 0.113648 -0.673690 -1.478427 0.524988 2000-01-07 0.577046 0.404705 -1.715002 -1.039268 2000-01-08 -1.157892 -0.370647 -1.344312 0.844885
You may find this useful for applying a transform (in-place) to a subset of the columns.
pandas aligns all AXES when setting Series and DataFrame from .loc, and .iloc.
This will not modify df because the column alignment is before value assignment.
df
In [9]: df[['A', 'B']] Out[9]: A B 2000-01-01 -0.282863 0.469112 2000-01-02 -0.173215 1.212112 2000-01-03 -2.104569 -0.861849 2000-01-04 -0.706771 0.721555 2000-01-05 0.567020 -0.424972 2000-01-06 0.113648 -0.673690 2000-01-07 0.577046 0.404705 2000-01-08 -1.157892 -0.370647 In [10]: df.loc[:, ['B', 'A']] = df[['A', 'B']] In [11]: df[['A', 'B']] Out[11]: A B 2000-01-01 -0.282863 0.469112 2000-01-02 -0.173215 1.212112 2000-01-03 -2.104569 -0.861849 2000-01-04 -0.706771 0.721555 2000-01-05 0.567020 -0.424972 2000-01-06 0.113648 -0.673690 2000-01-07 0.577046 0.404705 2000-01-08 -1.157892 -0.370647
The correct way to swap column values is by using raw values:
In [12]: df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy() In [13]: df[['A', 'B']] Out[13]: A B 2000-01-01 0.469112 -0.282863 2000-01-02 1.212112 -0.173215 2000-01-03 -0.861849 -2.104569 2000-01-04 0.721555 -0.706771 2000-01-05 -0.424972 0.567020 2000-01-06 -0.673690 0.113648 2000-01-07 0.404705 0.577046 2000-01-08 -0.370647 -1.157892
You may access an index on a Series or column on a DataFrame directly as an attribute:
In [14]: sa = pd.Series([1, 2, 3], index=list('abc')) In [15]: dfa = df.copy()
In [16]: sa.b Out[16]: 2 In [17]: dfa.A Out[17]: 2000-01-01 0.469112 2000-01-02 1.212112 2000-01-03 -0.861849 2000-01-04 0.721555 2000-01-05 -0.424972 2000-01-06 -0.673690 2000-01-07 0.404705 2000-01-08 -0.370647 Freq: D, Name: A, dtype: float64
In [18]: sa.a = 5 In [19]: sa Out[19]: a 5 b 2 c 3 dtype: int64 In [20]: dfa.A = list(range(len(dfa.index))) # ok if A already exists In [21]: dfa Out[21]: A B C D 2000-01-01 0 -0.282863 -1.509059 -1.135632 2000-01-02 1 -0.173215 0.119209 -1.044236 2000-01-03 2 -2.104569 -0.494929 1.071804 2000-01-04 3 -0.706771 -1.039575 0.271860 2000-01-05 4 0.567020 0.276232 -1.087401 2000-01-06 5 0.113648 -1.478427 0.524988 2000-01-07 6 0.577046 -1.715002 -1.039268 2000-01-08 7 -1.157892 -1.344312 0.844885 In [22]: dfa['A'] = list(range(len(dfa.index))) # use this form to create a new column In [23]: dfa Out[23]: A B C D 2000-01-01 0 -0.282863 -1.509059 -1.135632 2000-01-02 1 -0.173215 0.119209 -1.044236 2000-01-03 2 -2.104569 -0.494929 1.071804 2000-01-04 3 -0.706771 -1.039575 0.271860 2000-01-05 4 0.567020 0.276232 -1.087401 2000-01-06 5 0.113648 -1.478427 0.524988 2000-01-07 6 0.577046 -1.715002 -1.039268 2000-01-08 7 -1.157892 -1.344312 0.844885
You can use this access only if the index element is a valid Python identifier, e.g. s.1 is not allowed. See here for an explanation of valid identifiers.
s.1
The attribute will not be available if it conflicts with an existing method name, e.g. s.min is not allowed, but s['min'] is possible.
s.min
s['min']
Similarly, the attribute will not be available if it conflicts with any of the following list: index, major_axis, minor_axis, items.
index
major_axis
minor_axis
items
In any of these cases, standard indexing will still work, e.g. s['1'], s['min'], and s['index'] will access the corresponding element or column.
s['1']
s['index']
If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.
You can also assign a dict to a row of a DataFrame:
dict
In [24]: x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]}) In [25]: x.iloc[1] = {'x': 9, 'y': 99} In [26]: x Out[26]: x y 0 1 3 1 9 99 2 3 5
You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. In 0.21.0 and later, this will raise a UserWarning:
UserWarning
In [1]: df = pd.DataFrame({'one': [1., 2., 3.]}) In [2]: df.two = [4, 5, 6] UserWarning: Pandas doesn't allow Series to be assigned into nonexistent columns - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute_access In [3]: df Out[3]: one 0 1.0 1 2.0 2 3.0
The most robust and consistent way of slicing ranges along arbitrary axes is described in the Selection by Position section detailing the .iloc method. For now, we explain the semantics of slicing using the [] operator.
With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels:
In [27]: s[:5] Out[27]: 2000-01-01 0.469112 2000-01-02 1.212112 2000-01-03 -0.861849 2000-01-04 0.721555 2000-01-05 -0.424972 Freq: D, Name: A, dtype: float64 In [28]: s[::2] Out[28]: 2000-01-01 0.469112 2000-01-03 -0.861849 2000-01-05 -0.424972 2000-01-07 0.404705 Freq: 2D, Name: A, dtype: float64 In [29]: s[::-1] Out[29]: 2000-01-08 -0.370647 2000-01-07 0.404705 2000-01-06 -0.673690 2000-01-05 -0.424972 2000-01-04 0.721555 2000-01-03 -0.861849 2000-01-02 1.212112 2000-01-01 0.469112 Freq: -1D, Name: A, dtype: float64
Note that setting works as well:
In [30]: s2 = s.copy() In [31]: s2[:5] = 0 In [32]: s2 Out[32]: 2000-01-01 0.000000 2000-01-02 0.000000 2000-01-03 0.000000 2000-01-04 0.000000 2000-01-05 0.000000 2000-01-06 -0.673690 2000-01-07 0.404705 2000-01-08 -0.370647 Freq: D, Name: A, dtype: float64
With DataFrame, slicing inside of [] slices the rows. This is provided largely as a convenience since it is such a common operation.
In [33]: df[:3] Out[33]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112 -0.173215 0.119209 -1.044236 2000-01-03 -0.861849 -2.104569 -0.494929 1.071804 In [34]: df[::-1] Out[34]: A B C D 2000-01-08 -0.370647 -1.157892 -1.344312 0.844885 2000-01-07 0.404705 0.577046 -1.715002 -1.039268 2000-01-06 -0.673690 0.113648 -1.478427 0.524988 2000-01-05 -0.424972 0.567020 0.276232 -1.087401 2000-01-04 0.721555 -0.706771 -1.039575 0.271860 2000-01-03 -0.861849 -2.104569 -0.494929 1.071804 2000-01-02 1.212112 -0.173215 0.119209 -1.044236 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632
.loc is strict when you present slicers that are not compatible (or convertible) with the index type. For example using integers in a DatetimeIndex. These will raise a TypeError.
DatetimeIndex
TypeError
In [35]: dfl = pd.DataFrame(np.random.randn(5, 4), ....: columns=list('ABCD'), ....: index=pd.date_range('20130101', periods=5)) ....: In [36]: dfl Out[36]: A B C D 2013-01-01 1.075770 -0.109050 1.643563 -1.469388 2013-01-02 0.357021 -0.674600 -1.776904 -0.968914 2013-01-03 -1.294524 0.413738 0.276662 -0.472035 2013-01-04 -0.013960 -0.362543 -0.006154 -0.923061 2013-01-05 0.895717 0.805244 -1.206412 2.565646
In [4]: dfl.loc[2:3] TypeError: cannot do slice indexing on <class 'pandas.tseries.index.DatetimeIndex'> with these indexers [2] of <type 'int'>
String likes in slicing can be convertible to the type of the index and lead to natural slicing.
In [37]: dfl.loc['20130102':'20130104'] Out[37]: A B C D 2013-01-02 0.357021 -0.674600 -1.776904 -0.968914 2013-01-03 -1.294524 0.413738 0.276662 -0.472035 2013-01-04 -0.013960 -0.362543 -0.006154 -0.923061
Starting in 0.21.0, pandas will show a FutureWarning if indexing with a list with missing labels. In the future this will raise a KeyError. See list-like Using loc with missing keys in a list is Deprecated.
FutureWarning
pandas provides a suite of methods in order to have purely label based indexing. This is a strict inclusion based protocol. Every label asked for must be in the index, or a KeyError will be raised. When slicing, both the start bound AND the stop bound are included, if present in the index. Integers are valid labels, but they refer to the label and not the position.
The .loc attribute is the primary access method. The following are valid inputs:
A slice object with labels 'a':'f' (Note that contrary to usual python slices, both the start and the stop are included, when present in the index! See Slicing with labels.
A callable, see Selection By Callable.
In [38]: s1 = pd.Series(np.random.randn(6), index=list('abcdef')) In [39]: s1 Out[39]: a 1.431256 b 1.340309 c -1.170299 d -0.226169 e 0.410835 f 0.813850 dtype: float64 In [40]: s1.loc['c':] Out[40]: c -1.170299 d -0.226169 e 0.410835 f 0.813850 dtype: float64 In [41]: s1.loc['b'] Out[41]: 1.3403088497993827
In [42]: s1.loc['c':] = 0 In [43]: s1 Out[43]: a 1.431256 b 1.340309 c 0.000000 d 0.000000 e 0.000000 f 0.000000 dtype: float64
With a DataFrame:
In [44]: df1 = pd.DataFrame(np.random.randn(6, 4), ....: index=list('abcdef'), ....: columns=list('ABCD')) ....: In [45]: df1 Out[45]: A B C D a 0.132003 -0.827317 -0.076467 -1.187678 b 1.130127 -1.436737 -1.413681 1.607920 c 1.024180 0.569605 0.875906 -2.211372 d 0.974466 -2.006747 -0.410001 -0.078638 e 0.545952 -1.219217 -1.226825 0.769804 f -1.281247 -0.727707 -0.121306 -0.097883 In [46]: df1.loc[['a', 'b', 'd'], :] Out[46]: A B C D a 0.132003 -0.827317 -0.076467 -1.187678 b 1.130127 -1.436737 -1.413681 1.607920 d 0.974466 -2.006747 -0.410001 -0.078638
Accessing via label slices:
In [47]: df1.loc['d':, 'A':'C'] Out[47]: A B C d 0.974466 -2.006747 -0.410001 e 0.545952 -1.219217 -1.226825 f -1.281247 -0.727707 -0.121306
For getting a cross section using a label (equivalent to df.xs('a')):
df.xs('a')
In [48]: df1.loc['a'] Out[48]: A 0.132003 B -0.827317 C -0.076467 D -1.187678 Name: a, dtype: float64
For getting values with a boolean array:
In [49]: df1.loc['a'] > 0 Out[49]: A True B False C False D False Name: a, dtype: bool In [50]: df1.loc[:, df1.loc['a'] > 0] Out[50]: A a 0.132003 b 1.130127 c 1.024180 d 0.974466 e 0.545952 f -1.281247
For getting a value explicitly:
# this is also equivalent to ``df1.at['a','A']`` In [51]: df1.loc['a', 'A'] Out[51]: 0.13200317033032932
When using .loc with slices, if both the start and the stop labels are present in the index, then elements located between the two (including them) are returned:
In [52]: s = pd.Series(list('abcde'), index=[0, 3, 2, 5, 4]) In [53]: s.loc[3:5] Out[53]: 3 b 2 c 5 d dtype: object
If at least one of the two is absent, but the index is sorted, and can be compared against start and stop labels, then slicing will still work as expected, by selecting labels which rank between the two:
In [54]: s.sort_index() Out[54]: 0 a 2 c 3 b 4 e 5 d dtype: object In [55]: s.sort_index().loc[1:6] Out[55]: 2 c 3 b 4 e 5 d dtype: object
However, if at least one of the two is absent and the index is not sorted, an error will be raised (since doing otherwise would be computationally expensive, as well as potentially ambiguous for mixed type indexes). For instance, in the above example, s.loc[1:6] would raise KeyError.
s.loc[1:6]
For the rationale behind this behavior, see Endpoints are inclusive.
Pandas provides a suite of methods in order to get purely integer based indexing. The semantics follow closely Python and NumPy slicing. These are 0-based indexing. When slicing, the start bound is included, while the upper bound is excluded. Trying to use a non-integer, even a valid label will raise an IndexError.
0-based
The .iloc attribute is the primary access method. The following are valid inputs:
In [56]: s1 = pd.Series(np.random.randn(5), index=list(range(0, 10, 2))) In [57]: s1 Out[57]: 0 0.695775 2 0.341734 4 0.959726 6 -1.110336 8 -0.619976 dtype: float64 In [58]: s1.iloc[:3] Out[58]: 0 0.695775 2 0.341734 4 0.959726 dtype: float64 In [59]: s1.iloc[3] Out[59]: -1.110336102891167
In [60]: s1.iloc[:3] = 0 In [61]: s1 Out[61]: 0 0.000000 2 0.000000 4 0.000000 6 -1.110336 8 -0.619976 dtype: float64
In [62]: df1 = pd.DataFrame(np.random.randn(6, 4), ....: index=list(range(0, 12, 2)), ....: columns=list(range(0, 8, 2))) ....: In [63]: df1 Out[63]: 0 2 4 6 0 0.149748 -0.732339 0.687738 0.176444 2 0.403310 -0.154951 0.301624 -2.179861 4 -1.369849 -0.954208 1.462696 -1.743161 6 -0.826591 -0.345352 1.314232 0.690579 8 0.995761 2.396780 0.014871 3.357427 10 -0.317441 -1.236269 0.896171 -0.487602
Select via integer slicing:
In [64]: df1.iloc[:3] Out[64]: 0 2 4 6 0 0.149748 -0.732339 0.687738 0.176444 2 0.403310 -0.154951 0.301624 -2.179861 4 -1.369849 -0.954208 1.462696 -1.743161 In [65]: df1.iloc[1:5, 2:4] Out[65]: 4 6 2 0.301624 -2.179861 4 1.462696 -1.743161 6 1.314232 0.690579 8 0.014871 3.357427
Select via integer list:
In [66]: df1.iloc[[1, 3, 5], [1, 3]] Out[66]: 2 6 2 -0.154951 -2.179861 6 -0.345352 0.690579 10 -1.236269 -0.487602
In [67]: df1.iloc[1:3, :] Out[67]: 0 2 4 6 2 0.403310 -0.154951 0.301624 -2.179861 4 -1.369849 -0.954208 1.462696 -1.743161
In [68]: df1.iloc[:, 1:3] Out[68]: 2 4 0 -0.732339 0.687738 2 -0.154951 0.301624 4 -0.954208 1.462696 6 -0.345352 1.314232 8 2.396780 0.014871 10 -1.236269 0.896171
# this is also equivalent to ``df1.iat[1,1]`` In [69]: df1.iloc[1, 1] Out[69]: -0.1549507744249032
For getting a cross section using an integer position (equiv to df.xs(1)):
df.xs(1)
In [70]: df1.iloc[1] Out[70]: 0 0.403310 2 -0.154951 4 0.301624 6 -2.179861 Name: 2, dtype: float64
Out of range slice indexes are handled gracefully just as in Python/Numpy.
# these are allowed in python/numpy. In [71]: x = list('abcdef') In [72]: x Out[72]: ['a', 'b', 'c', 'd', 'e', 'f'] In [73]: x[4:10] Out[73]: ['e', 'f'] In [74]: x[8:10] Out[74]: [] In [75]: s = pd.Series(x) In [76]: s Out[76]: 0 a 1 b 2 c 3 d 4 e 5 f dtype: object In [77]: s.iloc[4:10] Out[77]: 4 e 5 f dtype: object In [78]: s.iloc[8:10] Out[78]: Series([], dtype: object)
Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned).
In [79]: dfl = pd.DataFrame(np.random.randn(5, 2), columns=list('AB')) In [80]: dfl Out[80]: A B 0 -0.082240 -2.182937 1 0.380396 0.084844 2 0.432390 1.519970 3 -0.493662 0.600178 4 0.274230 0.132885 In [81]: dfl.iloc[:, 2:3] Out[81]: Empty DataFrame Columns: [] Index: [0, 1, 2, 3, 4] In [82]: dfl.iloc[:, 1:3] Out[82]: B 0 -2.182937 1 0.084844 2 1.519970 3 0.600178 4 0.132885 In [83]: dfl.iloc[4:6] Out[83]: A B 4 0.27423 0.132885
A single indexer that is out of bounds will raise an IndexError. A list of indexers where any element is out of bounds will raise an IndexError.
>>> dfl.iloc[[4, 5, 6]] IndexError: positional indexers are out-of-bounds >>> dfl.iloc[:, 4] IndexError: single positional indexer is out-of-bounds
.loc, .iloc, and also [] indexing can accept a callable as indexer. The callable must be a function with one argument (the calling Series or DataFrame) that returns valid output for indexing.
In [84]: df1 = pd.DataFrame(np.random.randn(6, 4), ....: index=list('abcdef'), ....: columns=list('ABCD')) ....: In [85]: df1 Out[85]: A B C D a -0.023688 2.410179 1.450520 0.206053 b -0.251905 -2.213588 1.063327 1.266143 c 0.299368 -0.863838 0.408204 -1.048089 d -0.025747 -0.988387 0.094055 1.262731 e 1.289997 0.082423 -0.055758 0.536580 f -0.489682 0.369374 -0.034571 -2.484478 In [86]: df1.loc[lambda df: df['A'] > 0, :] Out[86]: A B C D c 0.299368 -0.863838 0.408204 -1.048089 e 1.289997 0.082423 -0.055758 0.536580 In [87]: df1.loc[:, lambda df: ['A', 'B']] Out[87]: A B a -0.023688 2.410179 b -0.251905 -2.213588 c 0.299368 -0.863838 d -0.025747 -0.988387 e 1.289997 0.082423 f -0.489682 0.369374 In [88]: df1.iloc[:, lambda df: [0, 1]] Out[88]: A B a -0.023688 2.410179 b -0.251905 -2.213588 c 0.299368 -0.863838 d -0.025747 -0.988387 e 1.289997 0.082423 f -0.489682 0.369374 In [89]: df1[lambda df: df.columns[0]] Out[89]: a -0.023688 b -0.251905 c 0.299368 d -0.025747 e 1.289997 f -0.489682 Name: A, dtype: float64
You can use callable indexing in Series.
In [90]: df1['A'].loc[lambda s: s > 0] Out[90]: c 0.299368 e 1.289997 Name: A, dtype: float64
Using these methods / indexers, you can chain data selection operations without using a temporary variable.
In [91]: bb = pd.read_csv('data/baseball.csv', index_col='id') In [92]: (bb.groupby(['year', 'team']).sum() ....: .loc[lambda df: df['r'] > 100]) ....: Out[92]: stint g ab r h X2b X3b hr ... cs bb so ibb hbp sh sf gidp year team ... 2007 CIN 6 379 745 101 203 35 2 36 ... 1.0 105 127.0 14.0 1.0 1.0 15.0 18.0 DET 5 301 1062 162 283 54 4 37 ... 7.0 97 176.0 3.0 10.0 4.0 8.0 28.0 HOU 4 311 926 109 218 47 6 14 ... 4.0 60 212.0 3.0 9.0 16.0 6.0 17.0 LAN 11 413 1021 153 293 61 3 36 ... 5.0 114 141.0 8.0 9.0 3.0 8.0 29.0 NYN 13 622 1854 240 509 101 3 61 ... 4.0 174 310.0 24.0 23.0 18.0 15.0 48.0 SFN 5 482 1305 198 337 67 6 40 ... 7.0 235 188.0 51.0 8.0 16.0 6.0 41.0 TEX 2 198 729 115 200 40 4 28 ... 4.0 73 140.0 4.0 5.0 2.0 8.0 16.0 TOR 4 459 1408 187 378 96 2 58 ... 2.0 190 265.0 16.0 12.0 4.0 16.0 38.0 [8 rows x 18 columns]
Starting in 0.20.0, the .ix indexer is deprecated, in favor of the more strict .iloc and .loc indexers.
.ix
.ix offers a lot of magic on the inference of what the user wants to do. To wit, .ix can decide to index positionally OR via labels depending on the data type of the index. This has caused quite a bit of user confusion over the years.
The recommended methods of indexing are:
.loc if you want to label index.
.iloc if you want to positionally index.
In [93]: dfd = pd.DataFrame({'A': [1, 2, 3], ....: 'B': [4, 5, 6]}, ....: index=list('abc')) ....: In [94]: dfd Out[94]: A B a 1 4 b 2 5 c 3 6
Previous behavior, where you wish to get the 0th and the 2nd elements from the index in the ‘A’ column.
In [3]: dfd.ix[[0, 2], 'A'] Out[3]: a 1 c 3 Name: A, dtype: int64
Using .loc. Here we will select the appropriate indexes from the index, then use label indexing.
In [95]: dfd.loc[dfd.index[[0, 2]], 'A'] Out[95]: a 1 c 3 Name: A, dtype: int64
This can also be expressed using .iloc, by explicitly getting locations on the indexers, and using positional indexing to select things.
In [96]: dfd.iloc[[0, 2], dfd.columns.get_loc('A')] Out[96]: a 1 c 3 Name: A, dtype: int64
For getting multiple indexers, using .get_indexer:
.get_indexer
In [97]: dfd.iloc[[0, 2], dfd.columns.get_indexer(['A', 'B'])] Out[97]: A B a 1 4 c 3 6
Starting in 0.21.0, using .loc or [] with a list with one or more missing labels, is deprecated, in favor of .reindex.
.reindex
In prior versions, using .loc[list-of-labels] would work as long as at least 1 of the keys was found (otherwise it would raise a KeyError). This behavior is deprecated and will show a warning message pointing to this section. The recommended alternative is to use .reindex().
.loc[list-of-labels]
.reindex()
For example.
In [98]: s = pd.Series([1, 2, 3]) In [99]: s Out[99]: 0 1 1 2 2 3 dtype: int64
Selection with all keys found is unchanged.
In [100]: s.loc[[1, 2]] Out[100]: 1 2 2 3 dtype: int64
Previous behavior
In [4]: s.loc[[1, 2, 3]] Out[4]: 1 2.0 2 3.0 3 NaN dtype: float64
Current behavior
In [4]: s.loc[[1, 2, 3]] Passing list-likes to .loc with any non-matching elements will raise KeyError in the future, you can use .reindex() as an alternative. See the documentation here: https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike Out[4]: 1 2.0 2 3.0 3 NaN dtype: float64
The idiomatic way to achieve selecting potentially not-found elements is via .reindex(). See also the section on reindexing.
In [101]: s.reindex([1, 2, 3]) Out[101]: 1 2.0 2 3.0 3 NaN dtype: float64
Alternatively, if you want to select only valid keys, the following is idiomatic and efficient; it is guaranteed to preserve the dtype of the selection.
In [102]: labels = [1, 2, 3] In [103]: s.loc[s.index.intersection(labels)] Out[103]: 1 2 2 3 dtype: int64
Having a duplicated index will raise for a .reindex():
In [104]: s = pd.Series(np.arange(4), index=['a', 'a', 'b', 'c']) In [105]: labels = ['c', 'd']
In [17]: s.reindex(labels) ValueError: cannot reindex from a duplicate axis
Generally, you can intersect the desired labels with the current axis, and then reindex.
In [106]: s.loc[s.index.intersection(labels)].reindex(labels) Out[106]: c 3.0 d NaN dtype: float64
However, this would still raise if your resulting index is duplicated.
In [41]: labels = ['a', 'd'] In [42]: s.loc[s.index.intersection(labels)].reindex(labels) ValueError: cannot reindex from a duplicate axis
A random selection of rows or columns from a Series or DataFrame with the sample() method. The method will sample rows by default, and accepts a specific number of rows/columns to return, or a fraction of rows.
sample()
In [107]: s = pd.Series([0, 1, 2, 3, 4, 5]) # When no arguments are passed, returns 1 row. In [108]: s.sample() Out[108]: 4 4 dtype: int64 # One may specify either a number of rows: In [109]: s.sample(n=3) Out[109]: 0 0 4 4 1 1 dtype: int64 # Or a fraction of the rows: In [110]: s.sample(frac=0.5) Out[110]: 5 5 3 3 1 1 dtype: int64
By default, sample will return each row at most once, but one can also sample with replacement using the replace option:
sample
replace
In [111]: s = pd.Series([0, 1, 2, 3, 4, 5]) # Without replacement (default): In [112]: s.sample(n=6, replace=False) Out[112]: 0 0 1 1 5 5 3 3 2 2 4 4 dtype: int64 # With replacement: In [113]: s.sample(n=6, replace=True) Out[113]: 0 0 4 4 3 3 2 2 4 4 4 4 dtype: int64
By default, each row has an equal probability of being selected, but if you want rows to have different probabilities, you can pass the sample function sampling weights as weights. These weights can be a list, a NumPy array, or a Series, but they must be of the same length as the object you are sampling. Missing values will be treated as a weight of zero, and inf values are not allowed. If weights do not sum to 1, they will be re-normalized by dividing all weights by the sum of the weights. For example:
weights
In [114]: s = pd.Series([0, 1, 2, 3, 4, 5]) In [115]: example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4] In [116]: s.sample(n=3, weights=example_weights) Out[116]: 5 5 4 4 3 3 dtype: int64 # Weights will be re-normalized automatically In [117]: example_weights2 = [0.5, 0, 0, 0, 0, 0] In [118]: s.sample(n=1, weights=example_weights2) Out[118]: 0 0 dtype: int64
When applied to a DataFrame, you can use a column of the DataFrame as sampling weights (provided you are sampling rows and not columns) by simply passing the name of the column as a string.
In [119]: df2 = pd.DataFrame({'col1': [9, 8, 7, 6], .....: 'weight_column': [0.5, 0.4, 0.1, 0]}) .....: In [120]: df2.sample(n=3, weights='weight_column') Out[120]: col1 weight_column 1 8 0.4 0 9 0.5 2 7 0.1
sample also allows users to sample columns instead of rows using the axis argument.
axis
In [121]: df3 = pd.DataFrame({'col1': [1, 2, 3], 'col2': [2, 3, 4]}) In [122]: df3.sample(n=1, axis=1) Out[122]: col1 0 1 1 2 2 3
Finally, one can also set a seed for sample’s random number generator using the random_state argument, which will accept either an integer (as a seed) or a NumPy RandomState object.
random_state
In [123]: df4 = pd.DataFrame({'col1': [1, 2, 3], 'col2': [2, 3, 4]}) # With a given seed, the sample will always draw the same rows. In [124]: df4.sample(n=2, random_state=2) Out[124]: col1 col2 2 3 4 1 2 3 In [125]: df4.sample(n=2, random_state=2) Out[125]: col1 col2 2 3 4 1 2 3
The .loc/[] operations can perform enlargement when setting a non-existent key for that axis.
.loc/[]
In the Series case this is effectively an appending operation.
In [126]: se = pd.Series([1, 2, 3]) In [127]: se Out[127]: 0 1 1 2 2 3 dtype: int64 In [128]: se[5] = 5. In [129]: se Out[129]: 0 1.0 1 2.0 2 3.0 5 5.0 dtype: float64
A DataFrame can be enlarged on either axis via .loc.
In [130]: dfi = pd.DataFrame(np.arange(6).reshape(3, 2), .....: columns=['A', 'B']) .....: In [131]: dfi Out[131]: A B 0 0 1 1 2 3 2 4 5 In [132]: dfi.loc[:, 'C'] = dfi.loc[:, 'A'] In [133]: dfi Out[133]: A B C 0 0 1 0 1 2 3 2 2 4 5 4
This is like an append operation on the DataFrame.
append
In [134]: dfi.loc[3] = 5 In [135]: dfi Out[135]: A B C 0 0 1 0 1 2 3 2 2 4 5 4 3 5 5 5
Since indexing with [] must handle a lot of cases (single-label access, slicing, boolean indexing, etc.), it has a bit of overhead in order to figure out what you’re asking for. If you only want to access a scalar value, the fastest way is to use the at and iat methods, which are implemented on all of the data structures.
at
iat
Similarly to loc, at provides label based scalar lookups, while, iat provides integer based lookups analogously to iloc
loc
iloc
In [136]: s.iat[5] Out[136]: 5 In [137]: df.at[dates[5], 'A'] Out[137]: -0.6736897080883706 In [138]: df.iat[3, 0] Out[138]: 0.7215551622443669
You can also set using these same indexers.
In [139]: df.at[dates[5], 'E'] = 7 In [140]: df.iat[3, 0] = 7
at may enlarge the object in-place as above if the indexer is missing.
In [141]: df.at[dates[-1] + pd.Timedelta('1 day'), 0] = 7 In [142]: df Out[142]: A B C D E 0 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 NaN NaN 2000-01-02 1.212112 -0.173215 0.119209 -1.044236 NaN NaN 2000-01-03 -0.861849 -2.104569 -0.494929 1.071804 NaN NaN 2000-01-04 7.000000 -0.706771 -1.039575 0.271860 NaN NaN 2000-01-05 -0.424972 0.567020 0.276232 -1.087401 NaN NaN 2000-01-06 -0.673690 0.113648 -1.478427 0.524988 7.0 NaN 2000-01-07 0.404705 0.577046 -1.715002 -1.039268 NaN NaN 2000-01-08 -0.370647 -1.157892 -1.344312 0.844885 NaN NaN 2000-01-09 NaN NaN NaN NaN NaN 7.0
Another common operation is the use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses, since by default Python will evaluate an expression such as df['A'] > 2 & df['B'] < 3 as df['A'] > (2 & df['B']) < 3, while the desired evaluation order is (df['A > 2) & (df['B'] < 3).
|
or
&
and
~
not
df['A'] > 2 & df['B'] < 3
df['A'] > (2 & df['B']) < 3
(df['A > 2) & (df['B'] < 3)
Using a boolean vector to index a Series works exactly as in a NumPy ndarray:
In [143]: s = pd.Series(range(-3, 4)) In [144]: s Out[144]: 0 -3 1 -2 2 -1 3 0 4 1 5 2 6 3 dtype: int64 In [145]: s[s > 0] Out[145]: 4 1 5 2 6 3 dtype: int64 In [146]: s[(s < -1) | (s > 0.5)] Out[146]: 0 -3 1 -2 4 1 5 2 6 3 dtype: int64 In [147]: s[~(s < 0)] Out[147]: 3 0 4 1 5 2 6 3 dtype: int64
You may select rows from a DataFrame using a boolean vector the same length as the DataFrame’s index (for example, something derived from one of the columns of the DataFrame):
In [148]: df[df['A'] > 0] Out[148]: A B C D E 0 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 NaN NaN 2000-01-02 1.212112 -0.173215 0.119209 -1.044236 NaN NaN 2000-01-04 7.000000 -0.706771 -1.039575 0.271860 NaN NaN 2000-01-07 0.404705 0.577046 -1.715002 -1.039268 NaN NaN
List comprehensions and the map method of Series can also be used to produce more complex criteria:
map
In [149]: df2 = pd.DataFrame({'a': ['one', 'one', 'two', 'three', 'two', 'one', 'six'], .....: 'b': ['x', 'y', 'y', 'x', 'y', 'x', 'x'], .....: 'c': np.random.randn(7)}) .....: # only want 'two' or 'three' In [150]: criterion = df2['a'].map(lambda x: x.startswith('t')) In [151]: df2[criterion] Out[151]: a b c 2 two y 0.041290 3 three x 0.361719 4 two y -0.238075 # equivalent but slower In [152]: df2[[x.startswith('t') for x in df2['a']]] Out[152]: a b c 2 two y 0.041290 3 three x 0.361719 4 two y -0.238075 # Multiple criteria In [153]: df2[criterion & (df2['b'] == 'x')] Out[153]: a b c 3 three x 0.361719
With the choice methods Selection by Label, Selection by Position, and Advanced Indexing you may select along more than one axis using boolean vectors combined with other indexing expressions.
In [154]: df2.loc[criterion & (df2['b'] == 'x'), 'b':'c'] Out[154]: b c 3 x 0.361719
Consider the isin() method of Series, which returns a boolean vector that is true wherever the Series elements exist in the passed list. This allows you to select rows where one or more columns have values you want:
isin()
In [155]: s = pd.Series(np.arange(5), index=np.arange(5)[::-1], dtype='int64') In [156]: s Out[156]: 4 0 3 1 2 2 1 3 0 4 dtype: int64 In [157]: s.isin([2, 4, 6]) Out[157]: 4 False 3 False 2 True 1 False 0 True dtype: bool In [158]: s[s.isin([2, 4, 6])] Out[158]: 2 2 0 4 dtype: int64
The same method is available for Index objects and is useful for the cases when you don’t know which of the sought labels are in fact present:
Index
In [159]: s[s.index.isin([2, 4, 6])] Out[159]: 4 0 2 2 dtype: int64 # compare it to the following In [160]: s.reindex([2, 4, 6]) Out[160]: 2 2.0 4 0.0 6 NaN dtype: float64
In addition to that, MultiIndex allows selecting a separate level to use in the membership check:
In [161]: s_mi = pd.Series(np.arange(6), .....: index=pd.MultiIndex.from_product([[0, 1], ['a', 'b', 'c']])) .....: In [162]: s_mi Out[162]: 0 a 0 b 1 c 2 1 a 3 b 4 c 5 dtype: int64 In [163]: s_mi.iloc[s_mi.index.isin([(1, 'a'), (2, 'b'), (0, 'c')])] Out[163]: 0 c 2 1 a 3 dtype: int64 In [164]: s_mi.iloc[s_mi.index.isin(['a', 'c', 'e'], level=1)] Out[164]: 0 a 0 c 2 1 a 3 c 5 dtype: int64
DataFrame also has an isin() method. When calling isin, pass a set of values as either an array or dict. If values is an array, isin returns a DataFrame of booleans that is the same shape as the original DataFrame, with True wherever the element is in the sequence of values.
isin
In [165]: df = pd.DataFrame({'vals': [1, 2, 3, 4], 'ids': ['a', 'b', 'f', 'n'], .....: 'ids2': ['a', 'n', 'c', 'n']}) .....: In [166]: values = ['a', 'b', 1, 3] In [167]: df.isin(values) Out[167]: vals ids ids2 0 True True True 1 False True False 2 True False False 3 False False False
Oftentimes you’ll want to match certain values with certain columns. Just make values a dict where the key is the column, and the value is a list of items you want to check for.
In [168]: values = {'ids': ['a', 'b'], 'vals': [1, 3]} In [169]: df.isin(values) Out[169]: vals ids ids2 0 True True False 1 False True False 2 True False False 3 False False False
Combine DataFrame’s isin with the any() and all() methods to quickly select subsets of your data that meet a given criteria. To select a row where each column meets its own criterion:
any()
all()
In [170]: values = {'ids': ['a', 'b'], 'ids2': ['a', 'c'], 'vals': [1, 3]} In [171]: row_mask = df.isin(values).all(1) In [172]: df[row_mask] Out[172]: vals ids ids2 0 1 a a
where()
Selecting values from a Series with a boolean vector generally returns a subset of the data. To guarantee that selection output has the same shape as the original data, you can use the where method in Series and DataFrame.
where
To return only the selected rows:
In [173]: s[s > 0] Out[173]: 3 1 2 2 1 3 0 4 dtype: int64
To return a Series of the same shape as the original:
In [174]: s.where(s > 0) Out[174]: 4 NaN 3 1.0 2 2.0 1 3.0 0 4.0 dtype: float64
Selecting values from a DataFrame with a boolean criterion now also preserves input data shape. where is used under the hood as the implementation. The code below is equivalent to df.where(df < 0).
df.where(df < 0)
In [175]: df[df < 0] Out[175]: A B C D 2000-01-01 -2.104139 -1.309525 NaN NaN 2000-01-02 -0.352480 NaN -1.192319 NaN 2000-01-03 -0.864883 NaN -0.227870 NaN 2000-01-04 NaN -1.222082 NaN -1.233203 2000-01-05 NaN -0.605656 -1.169184 NaN 2000-01-06 NaN -0.948458 NaN -0.684718 2000-01-07 -2.670153 -0.114722 NaN -0.048048 2000-01-08 NaN NaN -0.048788 -0.808838
In addition, where takes an optional other argument for replacement of values where the condition is False, in the returned copy.
other
In [176]: df.where(df < 0, -df) Out[176]: A B C D 2000-01-01 -2.104139 -1.309525 -0.485855 -0.245166 2000-01-02 -0.352480 -0.390389 -1.192319 -1.655824 2000-01-03 -0.864883 -0.299674 -0.227870 -0.281059 2000-01-04 -0.846958 -1.222082 -0.600705 -1.233203 2000-01-05 -0.669692 -0.605656 -1.169184 -0.342416 2000-01-06 -0.868584 -0.948458 -2.297780 -0.684718 2000-01-07 -2.670153 -0.114722 -0.168904 -0.048048 2000-01-08 -0.801196 -1.392071 -0.048788 -0.808838
You may wish to set values based on some boolean criteria. This can be done intuitively like so:
In [177]: s2 = s.copy() In [178]: s2[s2 < 0] = 0 In [179]: s2 Out[179]: 4 0 3 1 2 2 1 3 0 4 dtype: int64 In [180]: df2 = df.copy() In [181]: df2[df2 < 0] = 0 In [182]: df2 Out[182]: A B C D 2000-01-01 0.000000 0.000000 0.485855 0.245166 2000-01-02 0.000000 0.390389 0.000000 1.655824 2000-01-03 0.000000 0.299674 0.000000 0.281059 2000-01-04 0.846958 0.000000 0.600705 0.000000 2000-01-05 0.669692 0.000000 0.000000 0.342416 2000-01-06 0.868584 0.000000 2.297780 0.000000 2000-01-07 0.000000 0.000000 0.168904 0.000000 2000-01-08 0.801196 1.392071 0.000000 0.000000
By default, where returns a modified copy of the data. There is an optional parameter inplace so that the original data can be modified without creating a copy:
inplace
In [183]: df_orig = df.copy() In [184]: df_orig.where(df > 0, -df, inplace=True) In [185]: df_orig Out[185]: A B C D 2000-01-01 2.104139 1.309525 0.485855 0.245166 2000-01-02 0.352480 0.390389 1.192319 1.655824 2000-01-03 0.864883 0.299674 0.227870 0.281059 2000-01-04 0.846958 1.222082 0.600705 1.233203 2000-01-05 0.669692 0.605656 1.169184 0.342416 2000-01-06 0.868584 0.948458 2.297780 0.684718 2000-01-07 2.670153 0.114722 0.168904 0.048048 2000-01-08 0.801196 1.392071 0.048788 0.808838
The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).
DataFrame.where()
numpy.where()
df1.where(m, df2)
np.where(m, df1, df2)
In [186]: df.where(df < 0, -df) == np.where(df < 0, df, -df) Out[186]: A B C D 2000-01-01 True True True True 2000-01-02 True True True True 2000-01-03 True True True True 2000-01-04 True True True True 2000-01-05 True True True True 2000-01-06 True True True True 2000-01-07 True True True True 2000-01-08 True True True True
Alignment
Furthermore, where aligns the input boolean condition (ndarray or DataFrame), such that partial selection with setting is possible. This is analogous to partial setting via .loc (but on the contents rather than the axis labels).
In [187]: df2 = df.copy() In [188]: df2[df2[1:4] > 0] = 3 In [189]: df2 Out[189]: A B C D 2000-01-01 -2.104139 -1.309525 0.485855 0.245166 2000-01-02 -0.352480 3.000000 -1.192319 3.000000 2000-01-03 -0.864883 3.000000 -0.227870 3.000000 2000-01-04 3.000000 -1.222082 3.000000 -1.233203 2000-01-05 0.669692 -0.605656 -1.169184 0.342416 2000-01-06 0.868584 -0.948458 2.297780 -0.684718 2000-01-07 -2.670153 -0.114722 0.168904 -0.048048 2000-01-08 0.801196 1.392071 -0.048788 -0.808838
Where can also accept axis and level parameters to align the input when performing the where.
level
In [190]: df2 = df.copy() In [191]: df2.where(df2 > 0, df2['A'], axis='index') Out[191]: A B C D 2000-01-01 -2.104139 -2.104139 0.485855 0.245166 2000-01-02 -0.352480 0.390389 -0.352480 1.655824 2000-01-03 -0.864883 0.299674 -0.864883 0.281059 2000-01-04 0.846958 0.846958 0.600705 0.846958 2000-01-05 0.669692 0.669692 0.669692 0.342416 2000-01-06 0.868584 0.868584 2.297780 0.868584 2000-01-07 -2.670153 -2.670153 0.168904 -2.670153 2000-01-08 0.801196 1.392071 0.801196 0.801196
This is equivalent to (but faster than) the following.
In [192]: df2 = df.copy() In [193]: df.apply(lambda x, y: x.where(x > 0, y), y=df['A']) Out[193]: A B C D 2000-01-01 -2.104139 -2.104139 0.485855 0.245166 2000-01-02 -0.352480 0.390389 -0.352480 1.655824 2000-01-03 -0.864883 0.299674 -0.864883 0.281059 2000-01-04 0.846958 0.846958 0.600705 0.846958 2000-01-05 0.669692 0.669692 0.669692 0.342416 2000-01-06 0.868584 0.868584 2.297780 0.868584 2000-01-07 -2.670153 -2.670153 0.168904 -2.670153 2000-01-08 0.801196 1.392071 0.801196 0.801196
where can accept a callable as condition and other arguments. The function must be with one argument (the calling Series or DataFrame) and that returns valid output as condition and other argument.
In [194]: df3 = pd.DataFrame({'A': [1, 2, 3], .....: 'B': [4, 5, 6], .....: 'C': [7, 8, 9]}) .....: In [195]: df3.where(lambda x: x > 4, lambda x: x + 10) Out[195]: A B C 0 11 14 7 1 12 5 8 2 13 6 9
mask() is the inverse boolean operation of where.
mask()
In [196]: s.mask(s >= 0) Out[196]: 4 NaN 3 NaN 2 NaN 1 NaN 0 NaN dtype: float64 In [197]: df.mask(df >= 0) Out[197]: A B C D 2000-01-01 -2.104139 -1.309525 NaN NaN 2000-01-02 -0.352480 NaN -1.192319 NaN 2000-01-03 -0.864883 NaN -0.227870 NaN 2000-01-04 NaN -1.222082 NaN -1.233203 2000-01-05 NaN -0.605656 -1.169184 NaN 2000-01-06 NaN -0.948458 NaN -0.684718 2000-01-07 -2.670153 -0.114722 NaN -0.048048 2000-01-08 NaN NaN -0.048788 -0.808838
query()
DataFrame objects have a query() method that allows selection using an expression.
You can get the value of the frame where column b has values between the values of columns a and c. For example:
b
a
c
In [198]: n = 10 In [199]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc')) In [200]: df Out[200]: a b c 0 0.438921 0.118680 0.863670 1 0.138138 0.577363 0.686602 2 0.595307 0.564592 0.520630 3 0.913052 0.926075 0.616184 4 0.078718 0.854477 0.898725 5 0.076404 0.523211 0.591538 6 0.792342 0.216974 0.564056 7 0.397890 0.454131 0.915716 8 0.074315 0.437913 0.019794 9 0.559209 0.502065 0.026437 # pure python In [201]: df[(df['a'] < df['b']) & (df['b'] < df['c'])] Out[201]: a b c 1 0.138138 0.577363 0.686602 4 0.078718 0.854477 0.898725 5 0.076404 0.523211 0.591538 7 0.397890 0.454131 0.915716 # query In [202]: df.query('(a < b) & (b < c)') Out[202]: a b c 1 0.138138 0.577363 0.686602 4 0.078718 0.854477 0.898725 5 0.076404 0.523211 0.591538 7 0.397890 0.454131 0.915716
Do the same thing but fall back on a named index if there is no column with the name a.
In [203]: df = pd.DataFrame(np.random.randint(n / 2, size=(n, 2)), columns=list('bc')) In [204]: df.index.name = 'a' In [205]: df Out[205]: b c a 0 0 4 1 0 1 2 3 4 3 4 3 4 1 4 5 0 3 6 0 1 7 3 4 8 2 3 9 1 1 In [206]: df.query('a < b and b < c') Out[206]: b c a 2 3 4
If instead you don’t want to or cannot name your index, you can use the name index in your query expression:
In [207]: df = pd.DataFrame(np.random.randint(n, size=(n, 2)), columns=list('bc')) In [208]: df Out[208]: b c 0 3 1 1 3 0 2 5 6 3 5 2 4 7 4 5 0 1 6 2 5 7 0 1 8 6 0 9 7 9 In [209]: df.query('index < b < c') Out[209]: b c 2 5 6
If the name of your index overlaps with a column name, the column name is given precedence. For example,
In [210]: df = pd.DataFrame({'a': np.random.randint(5, size=5)}) In [211]: df.index.name = 'a' In [212]: df.query('a > 2') # uses the column 'a', not the index Out[212]: a a 1 3 3 3
You can still use the index in a query expression by using the special identifier ‘index’:
In [213]: df.query('index > 2') Out[213]: a a 3 3 4 2
If for some reason you have a column named index, then you can refer to the index as ilevel_0 as well, but at this point you should consider renaming your columns to something less ambiguous.
ilevel_0
You can also use the levels of a DataFrame with a MultiIndex as if they were columns in the frame:
In [214]: n = 10 In [215]: colors = np.random.choice(['red', 'green'], size=n) In [216]: foods = np.random.choice(['eggs', 'ham'], size=n) In [217]: colors Out[217]: array(['red', 'red', 'red', 'green', 'green', 'green', 'green', 'green', 'green', 'green'], dtype='<U5') In [218]: foods Out[218]: array(['ham', 'ham', 'eggs', 'eggs', 'eggs', 'ham', 'ham', 'eggs', 'eggs', 'eggs'], dtype='<U4') In [219]: index = pd.MultiIndex.from_arrays([colors, foods], names=['color', 'food']) In [220]: df = pd.DataFrame(np.random.randn(n, 2), index=index) In [221]: df Out[221]: 0 1 color food red ham 0.194889 -0.381994 ham 0.318587 2.089075 eggs -0.728293 -0.090255 green eggs -0.748199 1.318931 eggs -2.029766 0.792652 ham 0.461007 -0.542749 ham -0.305384 -0.479195 eggs 0.095031 -0.270099 eggs -0.707140 -0.773882 eggs 0.229453 0.304418 In [222]: df.query('color == "red"') Out[222]: 0 1 color food red ham 0.194889 -0.381994 ham 0.318587 2.089075 eggs -0.728293 -0.090255
If the levels of the MultiIndex are unnamed, you can refer to them using special names:
In [223]: df.index.names = [None, None] In [224]: df Out[224]: 0 1 red ham 0.194889 -0.381994 ham 0.318587 2.089075 eggs -0.728293 -0.090255 green eggs -0.748199 1.318931 eggs -2.029766 0.792652 ham 0.461007 -0.542749 ham -0.305384 -0.479195 eggs 0.095031 -0.270099 eggs -0.707140 -0.773882 eggs 0.229453 0.304418 In [225]: df.query('ilevel_0 == "red"') Out[225]: 0 1 red ham 0.194889 -0.381994 ham 0.318587 2.089075 eggs -0.728293 -0.090255
The convention is ilevel_0, which means “index level 0” for the 0th level of the index.
A use case for query() is when you have a collection of DataFrame objects that have a subset of column names (or index levels/names) in common. You can pass the same query to both frames without having to specify which frame you’re interested in querying
In [226]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc')) In [227]: df Out[227]: a b c 0 0.224283 0.736107 0.139168 1 0.302827 0.657803 0.713897 2 0.611185 0.136624 0.984960 3 0.195246 0.123436 0.627712 4 0.618673 0.371660 0.047902 5 0.480088 0.062993 0.185760 6 0.568018 0.483467 0.445289 7 0.309040 0.274580 0.587101 8 0.258993 0.477769 0.370255 9 0.550459 0.840870 0.304611 In [228]: df2 = pd.DataFrame(np.random.rand(n + 2, 3), columns=df.columns) In [229]: df2 Out[229]: a b c 0 0.357579 0.229800 0.596001 1 0.309059 0.957923 0.965663 2 0.123102 0.336914 0.318616 3 0.526506 0.323321 0.860813 4 0.518736 0.486514 0.384724 5 0.190804 0.505723 0.614533 6 0.891939 0.623977 0.676639 7 0.480559 0.378528 0.460858 8 0.420223 0.136404 0.141295 9 0.732206 0.419540 0.604675 10 0.604466 0.848974 0.896165 11 0.589168 0.920046 0.732716 In [230]: expr = '0.0 <= a <= c <= 0.5' In [231]: map(lambda frame: frame.query(expr), [df, df2]) Out[231]: <map at 0x7f2d072371d0>
Full numpy-like syntax:
In [232]: df = pd.DataFrame(np.random.randint(n, size=(n, 3)), columns=list('abc')) In [233]: df Out[233]: a b c 0 7 8 9 1 1 0 7 2 2 7 2 3 6 2 2 4 2 6 3 5 3 8 2 6 1 7 2 7 5 1 5 8 9 8 0 9 1 5 0 In [234]: df.query('(a < b) & (b < c)') Out[234]: a b c 0 7 8 9 In [235]: df[(df['a'] < df['b']) & (df['b'] < df['c'])] Out[235]: a b c 0 7 8 9
Slightly nicer by removing the parentheses (by binding making comparison operators bind tighter than & and |).
In [236]: df.query('a < b & b < c') Out[236]: a b c 0 7 8 9
Use English instead of symbols:
In [237]: df.query('a < b and b < c') Out[237]: a b c 0 7 8 9
Pretty close to how you might write it on paper:
In [238]: df.query('a < b < c') Out[238]: a b c 0 7 8 9
in
not in
query() also supports special use of Python’s in and not in comparison operators, providing a succinct syntax for calling the isin method of a Series or DataFrame.
# get all rows where columns "a" and "b" have overlapping values In [239]: df = pd.DataFrame({'a': list('aabbccddeeff'), 'b': list('aaaabbbbcccc'), .....: 'c': np.random.randint(5, size=12), .....: 'd': np.random.randint(9, size=12)}) .....: In [240]: df Out[240]: a b c d 0 a a 2 6 1 a a 4 7 2 b a 1 6 3 b a 2 1 4 c b 3 6 5 c b 0 2 6 d b 3 3 7 d b 2 1 8 e c 4 3 9 e c 2 0 10 f c 0 6 11 f c 1 2 In [241]: df.query('a in b') Out[241]: a b c d 0 a a 2 6 1 a a 4 7 2 b a 1 6 3 b a 2 1 4 c b 3 6 5 c b 0 2 # How you'd do it in pure Python In [242]: df[df['a'].isin(df['b'])] Out[242]: a b c d 0 a a 2 6 1 a a 4 7 2 b a 1 6 3 b a 2 1 4 c b 3 6 5 c b 0 2 In [243]: df.query('a not in b') Out[243]: a b c d 6 d b 3 3 7 d b 2 1 8 e c 4 3 9 e c 2 0 10 f c 0 6 11 f c 1 2 # pure Python In [244]: df[~df['a'].isin(df['b'])] Out[244]: a b c d 6 d b 3 3 7 d b 2 1 8 e c 4 3 9 e c 2 0 10 f c 0 6 11 f c 1 2
You can combine this with other expressions for very succinct queries:
# rows where cols a and b have overlapping values # and col c's values are less than col d's In [245]: df.query('a in b and c < d') Out[245]: a b c d 0 a a 2 6 1 a a 4 7 2 b a 1 6 4 c b 3 6 5 c b 0 2 # pure Python In [246]: df[df['b'].isin(df['a']) & (df['c'] < df['d'])] Out[246]: a b c d 0 a a 2 6 1 a a 4 7 2 b a 1 6 4 c b 3 6 5 c b 0 2 10 f c 0 6 11 f c 1 2
Note that in and not in are evaluated in Python, since numexpr has no equivalent of this operation. However, only the in/not in expression itself is evaluated in vanilla Python. For example, in the expression
numexpr
df.query('a in b + c + d')
(b + c + d) is evaluated by numexpr and then the in operation is evaluated in plain Python. In general, any operations that can be evaluated using numexpr will be.
(b + c + d)
==
list
Comparing a list of values to a column using ==/!= works similarly to in/not in.
!=
In [247]: df.query('b == ["a", "b", "c"]') Out[247]: a b c d 0 a a 2 6 1 a a 4 7 2 b a 1 6 3 b a 2 1 4 c b 3 6 5 c b 0 2 6 d b 3 3 7 d b 2 1 8 e c 4 3 9 e c 2 0 10 f c 0 6 11 f c 1 2 # pure Python In [248]: df[df['b'].isin(["a", "b", "c"])] Out[248]: a b c d 0 a a 2 6 1 a a 4 7 2 b a 1 6 3 b a 2 1 4 c b 3 6 5 c b 0 2 6 d b 3 3 7 d b 2 1 8 e c 4 3 9 e c 2 0 10 f c 0 6 11 f c 1 2 In [249]: df.query('c == [1, 2]') Out[249]: a b c d 0 a a 2 6 2 b a 1 6 3 b a 2 1 7 d b 2 1 9 e c 2 0 11 f c 1 2 In [250]: df.query('c != [1, 2]') Out[250]: a b c d 1 a a 4 7 4 c b 3 6 5 c b 0 2 6 d b 3 3 8 e c 4 3 10 f c 0 6 # using in/not in In [251]: df.query('[1, 2] in c') Out[251]: a b c d 0 a a 2 6 2 b a 1 6 3 b a 2 1 7 d b 2 1 9 e c 2 0 11 f c 1 2 In [252]: df.query('[1, 2] not in c') Out[252]: a b c d 1 a a 4 7 4 c b 3 6 5 c b 0 2 6 d b 3 3 8 e c 4 3 10 f c 0 6 # pure Python In [253]: df[df['c'].isin([1, 2])] Out[253]: a b c d 0 a a 2 6 2 b a 1 6 3 b a 2 1 7 d b 2 1 9 e c 2 0 11 f c 1 2
You can negate boolean expressions with the word not or the ~ operator.
In [254]: df = pd.DataFrame(np.random.rand(n, 3), columns=list('abc')) In [255]: df['bools'] = np.random.rand(len(df)) > 0.5 In [256]: df.query('~bools') Out[256]: a b c bools 2 0.697753 0.212799 0.329209 False 7 0.275396 0.691034 0.826619 False 8 0.190649 0.558748 0.262467 False In [257]: df.query('not bools') Out[257]: a b c bools 2 0.697753 0.212799 0.329209 False 7 0.275396 0.691034 0.826619 False 8 0.190649 0.558748 0.262467 False In [258]: df.query('not bools') == df[~df['bools']] Out[258]: a b c bools 2 True True True True 7 True True True True 8 True True True True
Of course, expressions can be arbitrarily complex too:
# short query syntax In [259]: shorter = df.query('a < b < c and (not bools) or bools > 2') # equivalent in pure Python In [260]: longer = df[(df['a'] < df['b']) .....: & (df['b'] < df['c']) .....: & (~df['bools']) .....: | (df['bools'] > 2)] .....: In [261]: shorter Out[261]: a b c bools 7 0.275396 0.691034 0.826619 False In [262]: longer Out[262]: a b c bools 7 0.275396 0.691034 0.826619 False In [263]: shorter == longer Out[263]: a b c bools 7 True True True True
DataFrame.query() using numexpr is slightly faster than Python for large frames.
DataFrame.query()
You will only see the performance benefits of using the numexpr engine with DataFrame.query() if your frame has more than approximately 200,000 rows.
This plot was created using a DataFrame with 3 columns each containing floating point values generated using numpy.random.randn().
numpy.random.randn()
If you want to identify and remove duplicate rows in a DataFrame, there are two methods that will help: duplicated and drop_duplicates. Each takes as an argument the columns to use to identify duplicated rows.
duplicated
drop_duplicates
duplicated returns a boolean vector whose length is the number of rows, and which indicates whether a row is duplicated.
drop_duplicates removes duplicate rows.
By default, the first observed row of a duplicate set is considered unique, but each method has a keep parameter to specify targets to be kept.
keep
keep='first' (default): mark / drop duplicates except for the first occurrence.
keep='first'
keep='last': mark / drop duplicates except for the last occurrence.
keep='last'
keep=False: mark / drop all duplicates.
keep=False
In [264]: df2 = pd.DataFrame({'a': ['one', 'one', 'two', 'two', 'two', 'three', 'four'], .....: 'b': ['x', 'y', 'x', 'y', 'x', 'x', 'x'], .....: 'c': np.random.randn(7)}) .....: In [265]: df2 Out[265]: a b c 0 one x -1.067137 1 one y 0.309500 2 two x -0.211056 3 two y -1.842023 4 two x -0.390820 5 three x -1.964475 6 four x 1.298329 In [266]: df2.duplicated('a') Out[266]: 0 False 1 True 2 False 3 True 4 True 5 False 6 False dtype: bool In [267]: df2.duplicated('a', keep='last') Out[267]: 0 True 1 False 2 True 3 True 4 False 5 False 6 False dtype: bool In [268]: df2.duplicated('a', keep=False) Out[268]: 0 True 1 True 2 True 3 True 4 True 5 False 6 False dtype: bool In [269]: df2.drop_duplicates('a') Out[269]: a b c 0 one x -1.067137 2 two x -0.211056 5 three x -1.964475 6 four x 1.298329 In [270]: df2.drop_duplicates('a', keep='last') Out[270]: a b c 1 one y 0.309500 4 two x -0.390820 5 three x -1.964475 6 four x 1.298329 In [271]: df2.drop_duplicates('a', keep=False) Out[271]: a b c 5 three x -1.964475 6 four x 1.298329
Also, you can pass a list of columns to identify duplications.
In [272]: df2.duplicated(['a', 'b']) Out[272]: 0 False 1 False 2 False 3 False 4 True 5 False 6 False dtype: bool In [273]: df2.drop_duplicates(['a', 'b']) Out[273]: a b c 0 one x -1.067137 1 one y 0.309500 2 two x -0.211056 3 two y -1.842023 5 three x -1.964475 6 four x 1.298329
To drop duplicates by index value, use Index.duplicated then perform slicing. The same set of options are available for the keep parameter.
Index.duplicated
In [274]: df3 = pd.DataFrame({'a': np.arange(6), .....: 'b': np.random.randn(6)}, .....: index=['a', 'a', 'b', 'c', 'b', 'a']) .....: In [275]: df3 Out[275]: a b a 0 1.440455 a 1 2.456086 b 2 1.038402 c 3 -0.894409 b 4 0.683536 a 5 3.082764 In [276]: df3.index.duplicated() Out[276]: array([False, True, False, False, True, True]) In [277]: df3[~df3.index.duplicated()] Out[277]: a b a 0 1.440455 b 2 1.038402 c 3 -0.894409 In [278]: df3[~df3.index.duplicated(keep='last')] Out[278]: a b c 3 -0.894409 b 4 0.683536 a 5 3.082764 In [279]: df3[~df3.index.duplicated(keep=False)] Out[279]: a b c 3 -0.894409
get()
Each of Series or DataFrame have a get method which can return a default value.
get
In [280]: s = pd.Series([1, 2, 3], index=['a', 'b', 'c']) In [281]: s.get('a') # equivalent to s['a'] Out[281]: 1 In [282]: s.get('x', default=-1) Out[282]: -1
lookup()
Sometimes you want to extract a set of values given a sequence of row labels and column labels, and the lookup method allows for this and returns a NumPy array. For instance:
lookup
In [283]: dflookup = pd.DataFrame(np.random.rand(20, 4), columns = ['A', 'B', 'C', 'D']) In [284]: dflookup.lookup(list(range(0, 10, 2)), ['B', 'C', 'A', 'B', 'D']) Out[284]: array([0.3506, 0.4779, 0.4825, 0.9197, 0.5019])
The pandas Index class and its subclasses can be viewed as implementing an ordered multiset. Duplicates are allowed. However, if you try to convert an Index object with duplicate entries into a set, an exception will be raised.
set
Index also provides the infrastructure necessary for lookups, data alignment, and reindexing. The easiest way to create an Index directly is to pass a list or other sequence to Index:
In [285]: index = pd.Index(['e', 'd', 'a', 'b']) In [286]: index Out[286]: Index(['e', 'd', 'a', 'b'], dtype='object') In [287]: 'd' in index Out[287]: True
You can also pass a name to be stored in the index:
name
In [288]: index = pd.Index(['e', 'd', 'a', 'b'], name='something') In [289]: index.name Out[289]: 'something'
The name, if set, will be shown in the console display:
In [290]: index = pd.Index(list(range(5)), name='rows') In [291]: columns = pd.Index(['A', 'B', 'C'], name='cols') In [292]: df = pd.DataFrame(np.random.randn(5, 3), index=index, columns=columns) In [293]: df Out[293]: cols A B C rows 0 1.295989 0.185778 0.436259 1 0.678101 0.311369 -0.528378 2 -0.674808 -1.103529 -0.656157 3 1.889957 2.076651 -1.102192 4 -1.211795 -0.791746 0.634724 In [294]: df['A'] Out[294]: rows 0 1.295989 1 0.678101 2 -0.674808 3 1.889957 4 -1.211795 Name: A, dtype: float64
Indexes are “mostly immutable”, but it is possible to set and change their metadata, like the index name (or, for MultiIndex, levels and codes).
levels
codes
You can use the rename, set_names, set_levels, and set_codes to set these attributes directly. They default to returning a copy; however, you can specify inplace=True to have the data change in place.
rename
set_names
set_levels
set_codes
inplace=True
See Advanced Indexing for usage of MultiIndexes.
In [295]: ind = pd.Index([1, 2, 3]) In [296]: ind.rename("apple") Out[296]: Int64Index([1, 2, 3], dtype='int64', name='apple') In [297]: ind Out[297]: Int64Index([1, 2, 3], dtype='int64') In [298]: ind.set_names(["apple"], inplace=True) In [299]: ind.name = "bob" In [300]: ind Out[300]: Int64Index([1, 2, 3], dtype='int64', name='bob')
set_names, set_levels, and set_codes also take an optional level argument
In [301]: index = pd.MultiIndex.from_product([range(3), ['one', 'two']], names=['first', 'second']) In [302]: index Out[302]: MultiIndex([(0, 'one'), (0, 'two'), (1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')], names=['first', 'second']) In [303]: index.levels[1] Out[303]: Index(['one', 'two'], dtype='object', name='second') In [304]: index.set_levels(["a", "b"], level=1) Out[304]: MultiIndex([(0, 'a'), (0, 'b'), (1, 'a'), (1, 'b'), (2, 'a'), (2, 'b')], names=['first', 'second'])
The two main operations are union (|) and intersection (&). These can be directly called as instance methods or used via overloaded operators. Difference is provided via the .difference() method.
union (|)
intersection (&)
.difference()
In [305]: a = pd.Index(['c', 'b', 'a']) In [306]: b = pd.Index(['c', 'e', 'd']) In [307]: a | b Out[307]: Index(['a', 'b', 'c', 'd', 'e'], dtype='object') In [308]: a & b Out[308]: Index(['c'], dtype='object') In [309]: a.difference(b) Out[309]: Index(['a', 'b'], dtype='object')
Also available is the symmetric_difference (^) operation, which returns elements that appear in either idx1 or idx2, but not in both. This is equivalent to the Index created by idx1.difference(idx2).union(idx2.difference(idx1)), with duplicates dropped.
symmetric_difference (^)
idx1
idx2
idx1.difference(idx2).union(idx2.difference(idx1))
In [310]: idx1 = pd.Index([1, 2, 3, 4]) In [311]: idx2 = pd.Index([2, 3, 4, 5]) In [312]: idx1.symmetric_difference(idx2) Out[312]: Int64Index([1, 5], dtype='int64') In [313]: idx1 ^ idx2 Out[313]: Int64Index([1, 5], dtype='int64')
The resulting index from a set operation will be sorted in ascending order.
When performing Index.union() between indexes with different dtypes, the indexes must be cast to a common dtype. Typically, though not always, this is object dtype. The exception is when performing a union between integer and float data. In this case, the integer values are converted to float
Index.union()
In [314]: idx1 = pd.Index([0, 1, 2]) In [315]: idx2 = pd.Index([0.5, 1.5]) In [316]: idx1 | idx2 Out[316]: Float64Index([0.0, 0.5, 1.0, 1.5, 2.0], dtype='float64')
Important
Even though Index can hold missing values (NaN), it should be avoided if you do not want any unexpected results. For example, some operations exclude missing values implicitly.
NaN
Index.fillna fills missing values with specified scalar value.
Index.fillna
In [317]: idx1 = pd.Index([1, np.nan, 3, 4]) In [318]: idx1 Out[318]: Float64Index([1.0, nan, 3.0, 4.0], dtype='float64') In [319]: idx1.fillna(2) Out[319]: Float64Index([1.0, 2.0, 3.0, 4.0], dtype='float64') In [320]: idx2 = pd.DatetimeIndex([pd.Timestamp('2011-01-01'), .....: pd.NaT, .....: pd.Timestamp('2011-01-03')]) .....: In [321]: idx2 Out[321]: DatetimeIndex(['2011-01-01', 'NaT', '2011-01-03'], dtype='datetime64[ns]', freq=None) In [322]: idx2.fillna(pd.Timestamp('2011-01-02')) Out[322]: DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], dtype='datetime64[ns]', freq=None)
Occasionally you will load or create a data set into a DataFrame and want to add an index after you’ve already done so. There are a couple of different ways.
DataFrame has a set_index() method which takes a column name (for a regular Index) or a list of column names (for a MultiIndex). To create a new, re-indexed DataFrame:
set_index()
In [323]: data Out[323]: a b c d 0 bar one z 1.0 1 bar two y 2.0 2 foo one x 3.0 3 foo two w 4.0 In [324]: indexed1 = data.set_index('c') In [325]: indexed1 Out[325]: a b d c z bar one 1.0 y bar two 2.0 x foo one 3.0 w foo two 4.0 In [326]: indexed2 = data.set_index(['a', 'b']) In [327]: indexed2 Out[327]: c d a b bar one z 1.0 two y 2.0 foo one x 3.0 two w 4.0
The append keyword option allow you to keep the existing index and append the given columns to a MultiIndex:
In [328]: frame = data.set_index('c', drop=False) In [329]: frame = frame.set_index(['a', 'b'], append=True) In [330]: frame Out[330]: c d c a b z bar one z 1.0 y bar two y 2.0 x foo one x 3.0 w foo two w 4.0
Other options in set_index allow you not drop the index columns or to add the index in-place (without creating a new object):
set_index
In [331]: data.set_index('c', drop=False) Out[331]: a b c d c z bar one z 1.0 y bar two y 2.0 x foo one x 3.0 w foo two w 4.0 In [332]: data.set_index(['a', 'b'], inplace=True) In [333]: data Out[333]: c d a b bar one z 1.0 two y 2.0 foo one x 3.0 two w 4.0
As a convenience, there is a new function on DataFrame called reset_index() which transfers the index values into the DataFrame’s columns and sets a simple integer index. This is the inverse operation of set_index().
reset_index()
In [334]: data Out[334]: c d a b bar one z 1.0 two y 2.0 foo one x 3.0 two w 4.0 In [335]: data.reset_index() Out[335]: a b c d 0 bar one z 1.0 1 bar two y 2.0 2 foo one x 3.0 3 foo two w 4.0
The output is more similar to a SQL table or a record array. The names for the columns derived from the index are the ones stored in the names attribute.
names
You can use the level keyword to remove only a portion of the index:
In [336]: frame Out[336]: c d c a b z bar one z 1.0 y bar two y 2.0 x foo one x 3.0 w foo two w 4.0 In [337]: frame.reset_index(level=1) Out[337]: a c d c b z one bar z 1.0 y two bar y 2.0 x one foo x 3.0 w two foo w 4.0
reset_index takes an optional parameter drop which if true simply discards the index, instead of putting index values in the DataFrame’s columns.
reset_index
drop
If you create an index yourself, you can just assign it to the index field:
data.index = index
When setting values in a pandas object, care must be taken to avoid what is called chained indexing. Here is an example.
chained indexing
In [338]: dfmi = pd.DataFrame([list('abcd'), .....: list('efgh'), .....: list('ijkl'), .....: list('mnop')], .....: columns=pd.MultiIndex.from_product([['one', 'two'], .....: ['first', 'second']])) .....:  It is instructive to understand the order of operations on these and why method 2 (.loc) is much preferred over method 1 (chained []).
dfmi['one'] selects the first level of the columns and returns a DataFrame that is singly-indexed. Then another Python operation dfmi_with_one['second'] selects the series indexed by 'second'. This is indicated by the variable dfmi_with_one because pandas sees these operations as separate events. e.g. separate calls to __getitem__, so it has to treat them as linear operations, they happen one after another.
dfmi['one']
dfmi_with_one['second']
'second'
dfmi_with_one
Contrast this to df.loc[:,('one','second')] which passes a nested tuple of (slice(None),('one','second')) to a single call to __getitem__. This allows pandas to deal with this as a single entity. Furthermore this order of operations can be significantly faster, and allows one to index both axes if so desired.
df.loc[:,('one','second')]
(slice(None),('one','second'))
The problem in the previous section is just a performance issue. What’s up with the SettingWithCopy warning? We don’t usually throw warnings around when you do something that might cost a few extra milliseconds!
SettingWithCopy
But it turns out that assigning to the product of chained indexing has inherently unpredictable results. To see this, think about how the Python interpreter executes this code:
dfmi.loc[:, ('one', 'second')] = value # becomes dfmi.loc.__setitem__((slice(None), ('one', 'second')), value)
But this code is handled differently:
dfmi['one']['second'] = value # becomes dfmi.__getitem__('one').__setitem__('second', value)
See that __getitem__ in there? Outside of simple cases, it’s very hard to predict whether it will return a view or a copy (it depends on the memory layout of the array, about which pandas makes no guarantees), and therefore whether the __setitem__ will modify dfmi or a temporary object that gets thrown out immediately afterward. That’s what SettingWithCopy is warning you about!
__setitem__
dfmi
You may be wondering whether we should be concerned about the loc property in the first example. But dfmi.loc is guaranteed to be dfmi itself with modified indexing behavior, so dfmi.loc.__getitem__ / dfmi.loc.__setitem__ operate on dfmi directly. Of course, dfmi.loc.__getitem__(idx) may be a view or a copy of dfmi.
dfmi.loc
dfmi.loc.__getitem__
dfmi.loc.__setitem__
dfmi.loc.__getitem__(idx)
Sometimes a SettingWithCopy warning will arise at times when there’s no obvious chained indexing going on. These are the bugs that SettingWithCopy is designed to catch! Pandas is probably trying to warn you that you’ve done this:
def do_something(df): foo = df[['bar', 'baz']] # Is foo a view? A copy? Nobody knows! # ... many lines here ... # We don't know whether this will modify df or not! foo['quux'] = value return foo
Yikes!
When you use chained indexing, the order and type of the indexing operation partially determine whether the result is a slice into the original object, or a copy of the slice.
Pandas has the SettingWithCopyWarning because assigning to a copy of a slice is frequently not intentional, but a mistake caused by chained indexing returning a copy where a slice was expected.
SettingWithCopyWarning
If you would like pandas to be more or less trusting about assignment to a chained indexing expression, you can set the option mode.chained_assignment to one of these values:
mode.chained_assignment
'warn', the default, means a SettingWithCopyWarning is printed.
'warn'
'raise' means pandas will raise a SettingWithCopyException you have to deal with.
'raise'
SettingWithCopyException
None will suppress the warnings entirely.
None
In [342]: dfb = pd.DataFrame({'a': ['one', 'one', 'two', .....: 'three', 'two', 'one', 'six'], .....: 'c': np.arange(7)}) .....: # This will show the SettingWithCopyWarning # but the frame values will be set In [343]: dfb['c'][dfb['a'].str.startswith('o')] = 42
This however is operating on a copy and will not work.
>>> pd.set_option('mode.chained_assignment','warn') >>> dfb[dfb['a'].str.startswith('o')]['c'] = 42 Traceback (most recent call last) ... SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead
A chained assignment can also crop up in setting in a mixed dtype frame.
These setting rules apply to all of .loc/.iloc.
.loc/.iloc
This is the correct access method:
In [344]: dfc = pd.DataFrame({'A': ['aaa', 'bbb', 'ccc'], 'B': [1, 2, 3]}) In [345]: dfc.loc[0, 'A'] = 11 In [346]: dfc Out[346]: A B 0 11 1 1 bbb 2 2 ccc 3
This can work at times, but it is not guaranteed to, and therefore should be avoided:
In [347]: dfc = dfc.copy() In [348]: dfc['A'][0] = 111 In [349]: dfc Out[349]: A B 0 111 1 1 bbb 2 2 ccc 3
This will not work at all, and so should be avoided:
>>> pd.set_option('mode.chained_assignment','raise') >>> dfc.loc[0]['A'] = 1111 Traceback (most recent call last) ... SettingWithCopyException: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead
The chained assignment warnings / exceptions are aiming to inform the user of a possibly invalid assignment. There may be false positives; situations where a chained assignment is inadvertently reported.


| CONTENTS | PREV | NEXT |
A security manager is not automatically installed when an application is running. To apply the same security policy to an application found on the local file system as to downloaded applets, either the user running the application must invoke the Java Virtual Machine with the new "-Djava.security.manager" command-line argument (which sets the value of the java.security.manager property), as in
java -Djava.security.manager SomeAppor the application itself must call the
setSecurityManager
method in the
java.lang.System class to install a security manager.
It is possible to specify on the command line a particular security manager to be utilized, by following "-Djava.security.manager" with an equals and the name of the class to be used as the security manager, as in
java -Djava.security.manager=COM.abc.MySecMgr SomeAppIf no security manager is specified, the built-in default security manager is utilized (unless the application installs a different security manager). All of the following are equivalent and result in usage of the default security manager:
java -Djava.security.manager SomeApp java -Djava.security.manager="" SomeApp java -Djava.security.manager=default SomeAppJava 2 SDK includes a property named java.class.path. Classes that are stored on the local file system but should not be treated as base classes (e.g., classes built into the SDK) should be on this path. Classes on this path are loaded with a secure class loader and are thus subjected to the security policy being enforced.
There is also a "-Djava.security.policy" command-line argument whose usage determines what policy files are utilized. This command-line argument is described in detail in the "Default System and User Policy Files" section. Basically, if you don't include "-Djava.security.policy" on the command line, then the policy files specified in the security properties file will be used.
You can use a "-Djava.security.policy" command-line argument to specify an additional or a different policy file when invoking execution of an application. For example, if you type the following, where pURL is a URL specifying the location of a policy file, then the specified policy file will be loaded in addition to all the policy files specified in the security properties file:
java -Djava.security.manager -Djava.security.policy=pURL SomeAppIf you instead type the following command, using a double equals, then just the specified policy file will be used; all others will be ignored:
java -Djava.security.manager -Djava.security.policy==pURL SomeApp
check
methods in SecurityManager are still supported,
although most of their implementations are changed to call the new
SecurityManager checkPermission
method, whose default
implementation calls the AccessController
checkPermission
method. Note that certain internal
security checks may stay in the SecurityManager class, unless it
can be parameterized.
We have not at this
time revised any system code to call AccessController instead of
calling SecurityManager (and checking for the existence of a
classloader), because of the potential of existing third-party
applications that subclass the SecurityManager and customize the
check
methods. In fact, we added a new method
SecurityManager.checkPermission
that by default simply
invokes AccessController.checkPermission
.
To understand the
relationship between SecurityManager and AccessController, it is
sufficient to note that SecurityManager represents the concept of a
central point of access control, while AccessController implements
a particular access control algorithm, with special features such
as the doPrivileged
method. By keeping SecurityManager
up to date, we maintain backward compatibility (e.g., for those
applications that have written their own security manager classes
based on earlier versions of the JDK) and flexibility (e.g., for
someone wanting to customize the security model to implement
mandatory access control or multilevel security). By providing
AccessController, we build in the algorithm that we believe is the
most restrictive and that relieves the typical programmer from the
burden of having to write extensive security code in most
scenarios.
We encourage the use of AccessController in application code, while customization of a security manager (via subclassing) should be the last resort and should be done with extreme care. Moreover, a customized security manager, such as one that always checks the time of the day before invoking standard security checks, could and should utilize the algorithm provided by AccessController whenever appropriate.
One thing to remember is that, when you implement your own SecurityManager
, you should install it as trusted software and grant it java.security.AllPermission
. There are several ways to do this:
You can install your SecurityManager
as an extension, because all extensions are granted AllPermission
by default. For more information, see Lesson: Creating and Using Extensions.
You can adjust the policy file to grant AllPermission
to your SecurityManager
. For more information, see Default Policy Implementation and Policy File Syntax.
keytool stores the keys and certificates in a so-called keystore. The default keystore implementation implements the keystore as a file. It protects private keys with a password.
The chains of X.509 certificates are provided by organizations called Certification Authorities, or CAs. Identities (including CAs) use their private keys to authenticate their association with objects (such as with channels which are secured using SSL), with archives of code they signed, or (for CAs) with X.509 certificates they have issued. As a bootstrapping tool, certificates generated using the -genkey command may be used until a Certification Authority returns a certificate chain.
The private keys in this database are always stored in encrypted form, to make it difficult to disclose these private keys inappropriately. A password is required to access or modify the database. These private keys are encrypted using the "password", which should be several words long. If the password is lost, those authentication keys cannot be recovered.
In fact, each private key in the keystore can be protected using its own individual password, which may or may not be the same as the password that protects the keystore's overall integrity.
This tool is (currently) intended to be used from the command line, where one simply types "keytool" as a shell prompt. keytool is a script that executes the appropriate Java classes and is built together with the SDK.
The command line options for each command may be provided in any order. Typing an incorrect option or typing "keytool -help" will cause the tool's usage to be summarized on the output device (such as a shell window), as given below.
% keytool -help KeyTool usage: -certreq [-v] [-alias <alias>] [-sigalg <sigalg>] [-file <certreq_file>] [-keypass <keypass>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -delete [-v] -alias <alias> [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -export [-v] [rfc] [-alias <alias>] [-file <cert_file>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -genkey [-v] [-alias <alias>] [-keyalg <keyalg>] [-keysize <keysize>] [-sigalg <sigalg>] [-dname <distinguished_name>] [-validity <valDays>] [-keypass <keypass>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -help -identitydb [-v] [-file <idb_file>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -import [-v] [-noprompt] [-alias <alias>] [-file <cert_file>] [-keypass <keypass>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -keyclone [-v] [-alias <alias>] -dest <dest_alias> [-keypass <keypass>] [-new <new_keypass>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -keypasswd [-v] [-alias <alias>] [-keypass <old_keypass>] [-new <new_keypass>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -list [-v | -rfc] [-alias <alias>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -printcert [-v] [-file <cert_file>] -selfcert [-v] [-alias <alias>] [-sigalg <sigalg>] [-dname <distinguished_name>] [-validity <valDays>] [-keypass <keypass>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype] -storepasswd [-v] [-new <new_storepass>] [-keystore <keystore>] [-storepass <storepass>] [-storetype storetype]
policytool
.
This again is a script built with the SDK that invokes the
appropriate (non-public) implementation classes.
See the PolicyTool documentation for usage information and examples with up-to-date screen shots. The documentation can be found in the policytool.html file in the
/docs/technotes/tools/unix/or
/docs/technotes/tools/windows/directory in the directory in which the SDK was installed (where the file separators are actually backslashes on Windows systems).
% jarsigner Usage: jarsigner [options] jar-file alias jarsigner -verify [options] jar-file [-keystore <url>] keystore file location [-storepass <password>] password for keystore integrity [-keypass <password>] password for private key (if different) [-sigfile <file>] name of .SF/.DSA file [-signedjar <file>] name of signed JAR file [-verify] verify a signed JAR file [-verbose] verbose output when signing/verifying [-certs] display certificates when verbose and verifying [-internalsf] include .SF file inside signature block [-sectionsonly] don't compute hash of entire manifestAgain, this tool is a script built with the SDK. Note that it is expected that this tool and the existing jar tool script may be merged in the near future to form a single command-line primitive to create JARs, either signed or unsigned.


PURE CSS
50 CSS rules
38 html elements
The database was downloaded from kaggle. I took a table that contains data about each user (which animes they completed and their ratings...) this table was initially about 35 million rows, i needed to do a lot of data wrangling in order for postgres to accept the data as it had numerous errors. i had to write scripts to remove these errors and i later randomly (uniformly) removed about a 3rd of the rows and ended up with about 12 million rows.
I then joined the the above table with the animes table which is about 7 thousand rows and contains info about all the animes. (rank, popularity, released date, number of episodes...) from the resulting table I can get info such as how many people finished an anime and it's rank and I created the graph from this info.
The visualization is about animes. one visualization that correlates between anime's rank (based on user score aggregate) on the x axis and the anime's number of views by all users on the y axis. ( the number of views only includes people who set the anime as completed in their account) anime movies show up as red circles while anime tv shows show up as blue circles. when you hover over a circle, it changes to green color and displays the anime's name in a tooltip.
1. rank slider: the range of the rank of the shown points gets filtered according to the range of the slider. this slider can be dragged, expanded and shrunk. (the maximum allowed range of expansion is 750. otherwise graph will be too cluttered if more is allowed)
2. two checkboxs: one for movies and one for TV shows. toggling them will affect which is shown in the vizualization.
Server side
The server is built in Node.js using the express and pg packages. it listens for requests on the /data path. the client sends the current TV/Movie and rank range filtering, the server then creates an SQL query and sends it along to the postgres database and returns the resulting to the client
Client side
The graph is built using the d3 library. everytime the user changes either the rank or the TV/Movie checkbox filtering, it issues a request to the server in order to get the new data that will show up in the graph.


Yet Another Collection Approach
YACA is another approach to introduce Collections to TypeScript / JavaScript like known in Java, C# or other object-oriented programming languages. There are other approaches, but sometimes, small things regarding the convenience are missing.
YACA contains at the moment List<T>, Dictionary<K,V> and SortedDictionary<K,V> as collection types. Further types (e.g. Stack) are planned.
See the Change Log for recent updates.
npm install -S yaca
A list stores values similar to an array. In comparison to an array, several functions like copies of a range, clearing or sorting can be applied out of the box. Additionally, operations for stacks and queues are included.
import {List} from 'yaca';
// Default constructor
let numberList: List<number> = new List<number>();
// Constructor with initial value
var stringList: List<string> = new List<string>("initial value");
// Constructor with array as initial value
let booelanList: List<boolean> = new List<boolean>([true, false, true, true]);
// Usage of more complex types
var otherList: List<SomeType> = new List<SomeType>();
numberList.add(22);
numberList.addRange([23,24,25]);
numberList.sort();
numberList.forEach(element => {
console.log(element);
if (element === 2)
{
numberList.continue(); // Optional / syntactic call to skip this iteration
return; // Mandatory! -> sufficient as continue call
}
if (element === 4)
{
numberList.break(); // Breaks the forEach loop immediately
return; // Mandatory!
}
});
numberList.clear();
See The List documentation page for further details:
A dictionary stores tuples of keys and an values. The type of both can be arbitrary. However, keys must be unique. Thus, a dictionary with boolean as keys allows only two tuples (true and false) in the Dictionary. If custom classes are used as keys, a valid toString function must be implemented in those classes or the function overrideHashFunction must be used in the Dictionary.
import {Dictionary} from 'yaca';
// Default constructor
let dictionary: Dictionary<number, string> = new Dictionary<number, string>();
// Constructor with initial value
let dictionary2: Dictionary<number, string> = new Dictionary<number, string>([1,2,3],["one","two","three"]]);
// Constructor with custom function to override the hashing of the keys (defaul is toString)
let dictionary3: Dictionary<number, Date> = new Dictionary<number, Date>(MyUtils.DateHashingFunction);
// Usage of more complex types
var otherDictionary: Dictionary<Date, SomeType> = new Dictionary<Date, SomeType>();
dictionary.add(22, "twenty two");
let value:string = dictionary2.get(2);
dictionary2.forEach(item => {
console.log("key:" + item.key + " -> value:" + item.value);
if (item.value === "two")
{
dictionary2.continue(); // Optional / syntactic call to skip this iteration
return; // Mandatory! -> sufficient as continue call
}
if (item.key === 3)
{
dictionary2.break(); // Breaks the forEach loop immediately
return; // Mandatory!
}
});
dictionary3.clear();
See The Dictionary documentation page for further details:
A sorted dictionary has the same behavior properties and functions like a standard dictionary. Additionally, there are many functions regarding the indices of the key value tuples and the possibility to sorting the dictionary by keys or values.
All functions of Dictionary <K,V>
import {SortedDictionary} from 'yaca';
import {Comparer} from 'yaca';
// Default constructor
let dictionary: SortedDictionary<number, string> = new SortedDictionary<number, string>();
// Constructor with initial value
let dictionary2: Dictionary<number, string> = new Dictionary<number, string>([1,2,3],["one","two","three"]]);
// -> All operation of the standard Dictionary<K,V> class possible
let value: string = dictionary2.getByIndex(1); // Returns "two" (key=2; index=1; value="two")
dictionary2.removeByIndex(2); // Removes the third element (key=3; index=2; value="three")
dictionary2.sortByKey(); // Sort the dictionary by its keys (using default behavior of number)
dictionary2.sortByKey(Comparer.compareNumbers); // Sort the dictionary by its keys (using a comparison function)
dictionary2.sortByValue(); // Sort the dictionary by its values (using default behavior of string)
See The SortedDictionary documentation page for further details:
Generated using TypeDoc


|
pyrax
Python Bindings for the Rackspace Cloud
|
Manager class for a Queue Claims. More...
Public Member Functions | |
| def | claim |
| Claims up to `count` unclaimed messages from this queue. | |
| def | update |
| Updates the specified claim with either a new TTL or grace period, or both. |
Manager class for a Queue Claims.
| def claim | ( | self, | |
| ttl, | |||
| grace, | |||
count = None |
|||
| ) |
Claims up to `count` unclaimed messages from this queue.
If count is not specified, the default is to claim 10 messages.
The `ttl` parameter specifies how long the server should wait before releasing the claim. The ttl value MUST be between 60 and 43200 seconds.
The `grace` parameter is the message grace period in seconds. The value of grace MUST be between 60 and 43200 seconds. The server extends the lifetime of claimed messages to be at least as long as the lifetime of the claim itself, plus a specified grace period to deal with crashed workers (up to 1209600 or 14 days including claim lifetime). If a claimed message would normally live longer than the grace period, its expiration will not be adjusted.
bReturns a QueueClaim object, whose 'messages' attribute contains the list of QueueMessage objects representing the claimed messages.
| def update | ( | self, | |
| claim, | |||
ttl = None , |
|||
grace = None |
|||
| ) |
Updates the specified claim with either a new TTL or grace period, or both.