Series and DataFrame have a method pct_change() to compute the percent change over a given number of periods (using fill_method to fill NA/null values before computing the percent change).
Series
DataFrame
pct_change()
fill_method
In [1]: ser = pd.Series(np.random.randn(8)) In [2]: ser.pct_change() Out[2]: 0 NaN 1 -1.602976 2 4.334938 3 -0.247456 4 -2.067345 5 -1.142903 6 -1.688214 7 -9.759729 dtype: float64
In [3]: df = pd.DataFrame(np.random.randn(10, 4)) In [4]: df.pct_change(periods=3) Out[4]: 0 1 2 3 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 -0.218320 -1.054001 1.987147 -0.510183 4 -0.439121 -1.816454 0.649715 -4.822809 5 -0.127833 -3.042065 -5.866604 -1.776977 6 -2.596833 -1.959538 -2.111697 -3.798900 7 -0.117826 -2.169058 0.036094 -0.067696 8 2.492606 -1.357320 -1.205802 -1.558697 9 -1.012977 2.324558 -1.003744 -0.371806
Series.cov() can be used to compute covariance between series (excluding missing values).
Series.cov()
In [5]: s1 = pd.Series(np.random.randn(1000)) In [6]: s2 = pd.Series(np.random.randn(1000)) In [7]: s1.cov(s2) Out[7]: 0.0006801088174310875
Analogously, DataFrame.cov() to compute pairwise covariances among the series in the DataFrame, also excluding NA/null values.
DataFrame.cov()
Note
Assuming the missing data are missing at random this results in an estimate for the covariance matrix which is unbiased. However, for many applications this estimate may not be acceptable because the estimated covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimated correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices for more details.
In [8]: frame = pd.DataFrame(np.random.randn(1000, 5), ...: columns=['a', 'b', 'c', 'd', 'e']) ...: In [9]: frame.cov() Out[9]: a b c d e a 1.000882 -0.003177 -0.002698 -0.006889 0.031912 b -0.003177 1.024721 0.000191 0.009212 0.000857 c -0.002698 0.000191 0.950735 -0.031743 -0.005087 d -0.006889 0.009212 -0.031743 1.002983 -0.047952 e 0.031912 0.000857 -0.005087 -0.047952 1.042487
DataFrame.cov also supports an optional min_periods keyword that specifies the required minimum number of observations for each column pair in order to have a valid result.
DataFrame.cov
min_periods
In [10]: frame = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c']) In [11]: frame.loc[frame.index[:5], 'a'] = np.nan In [12]: frame.loc[frame.index[5:10], 'b'] = np.nan In [13]: frame.cov() Out[13]: a b c a 1.123670 -0.412851 0.018169 b -0.412851 1.154141 0.305260 c 0.018169 0.305260 1.301149 In [14]: frame.cov(min_periods=12) Out[14]: a b c a 1.123670 NaN 0.018169 b NaN 1.154141 0.305260 c 0.018169 0.305260 1.301149
Correlation may be computed using the corr() method. Using the method parameter, several methods for computing correlations are provided:
corr()
method
Method name
Description
pearson (default)
Standard correlation coefficient
kendall
Kendall Tau correlation coefficient
spearman
Spearman rank correlation coefficient
All of these are currently computed using pairwise complete observations. Wikipedia has articles covering the above correlation coefficients:
Pearson correlation coefficient
Kendall rank correlation coefficient
Spearman’s rank correlation coefficient
Please see the caveats associated with this method of calculating correlation matrices in the covariance section.
In [15]: frame = pd.DataFrame(np.random.randn(1000, 5), ....: columns=['a', 'b', 'c', 'd', 'e']) ....: In [16]: frame.iloc[::2] = np.nan # Series with Series In [17]: frame['a'].corr(frame['b']) Out[17]: 0.013479040400098775 In [18]: frame['a'].corr(frame['b'], method='spearman') --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-18-3a09724e6474> in <module> ----> 1 frame['a'].corr(frame['b'], method='spearman') ~/scipy/pandas/pandas/core/series.py in corr(self, other, method, min_periods) 2179 if method in ["pearson", "spearman", "kendall"] or callable(method): 2180 return nanops.nancorr( -> 2181 this.values, other.values, method=method, min_periods=min_periods 2182 ) 2183 ~/scipy/pandas/pandas/core/nanops.py in _f(*args, **kwargs) 67 try: 68 with np.errstate(invalid="ignore"): ---> 69 return f(*args, **kwargs) 70 except ValueError as e: 71 # we want to transform an object array ~/scipy/pandas/pandas/core/nanops.py in nancorr(a, b, method, min_periods) 1237 return np.nan 1238 -> 1239 f = get_corr_func(method) 1240 return f(a, b) 1241 ~/scipy/pandas/pandas/core/nanops.py in get_corr_func(method) 1243 def get_corr_func(method): 1244 if method in ["kendall", "spearman"]: -> 1245 from scipy.stats import kendalltau, spearmanr 1246 elif method in ["pearson"]: 1247 pass ModuleNotFoundError: No module named 'scipy' # Pairwise correlation of DataFrame columns In [19]: frame.corr() Out[19]: a b c d e a 1.000000 0.013479 -0.049269 -0.042239 -0.028525 b 0.013479 1.000000 -0.020433 -0.011139 0.005654 c -0.049269 -0.020433 1.000000 0.018587 -0.054269 d -0.042239 -0.011139 0.018587 1.000000 -0.017060 e -0.028525 0.005654 -0.054269 -0.017060 1.000000
Note that non-numeric columns will be automatically excluded from the correlation calculation.
Like cov, corr also supports the optional min_periods keyword:
cov
corr
In [20]: frame = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c']) In [21]: frame.loc[frame.index[:5], 'a'] = np.nan In [22]: frame.loc[frame.index[5:10], 'b'] = np.nan In [23]: frame.corr() Out[23]: a b c a 1.000000 -0.121111 0.069544 b -0.121111 1.000000 0.051742 c 0.069544 0.051742 1.000000 In [24]: frame.corr(min_periods=12) Out[24]: a b c a 1.000000 NaN 0.069544 b NaN 1.000000 0.051742 c 0.069544 0.051742 1.000000
New in version 0.24.0.
The method argument can also be a callable for a generic correlation calculation. In this case, it should be a single function that produces a single value from two ndarray inputs. Suppose we wanted to compute the correlation based on histogram intersection:
# histogram intersection In [25]: def histogram_intersection(a, b): ....: return np.minimum(np.true_divide(a, a.sum()), ....: np.true_divide(b, b.sum())).sum() ....: In [26]: frame.corr(method=histogram_intersection) Out[26]: a b c a 1.000000 -6.404882 -2.058431 b -6.404882 1.000000 -19.255743 c -2.058431 -19.255743 1.000000
A related method corrwith() is implemented on DataFrame to compute the correlation between like-labeled Series contained in different DataFrame objects.
corrwith()
In [27]: index = ['a', 'b', 'c', 'd', 'e'] In [28]: columns = ['one', 'two', 'three', 'four'] In [29]: df1 = pd.DataFrame(np.random.randn(5, 4), index=index, columns=columns) In [30]: df2 = pd.DataFrame(np.random.randn(4, 4), index=index[:4], columns=columns) In [31]: df1.corrwith(df2) Out[31]: one -0.125501 two -0.493244 three 0.344056 four 0.004183 dtype: float64 In [32]: df2.corrwith(df1, axis=1) Out[32]: a -0.675817 b 0.458296 c 0.190809 d -0.186275 e NaN dtype: float64
The rank() method produces a data ranking with ties being assigned the mean of the ranks (by default) for the group:
rank()
In [33]: s = pd.Series(np.random.randn(5), index=list('abcde')) In [34]: s['d'] = s['b'] # so there's a tie In [35]: s.rank() Out[35]: a 5.0 b 2.5 c 1.0 d 2.5 e 4.0 dtype: float64
rank() is also a DataFrame method and can rank either the rows (axis=0) or the columns (axis=1). NaN values are excluded from the ranking.
axis=0
axis=1
NaN
In [36]: df = pd.DataFrame(np.random.randn(10, 6)) In [37]: df[4] = df[2][:5] # some ties In [38]: df Out[38]: 0 1 2 3 4 5 0 -0.904948 -1.163537 -1.457187 0.135463 -1.457187 0.294650 1 -0.976288 -0.244652 -0.748406 -0.999601 -0.748406 -0.800809 2 0.401965 1.460840 1.256057 1.308127 1.256057 0.876004 3 0.205954 0.369552 -0.669304 0.038378 -0.669304 1.140296 4 -0.477586 -0.730705 -1.129149 -0.601463 -1.129149 -0.211196 5 -1.092970 -0.689246 0.908114 0.204848 NaN 0.463347 6 0.376892 0.959292 0.095572 -0.593740 NaN -0.069180 7 -1.002601 1.957794 -0.120708 0.094214 NaN -1.467422 8 -0.547231 0.664402 -0.519424 -0.073254 NaN -1.263544 9 -0.250277 -0.237428 -1.056443 0.419477 NaN 1.375064 In [39]: df.rank(1) Out[39]: 0 1 2 3 4 5 0 4.0 3.0 1.5 5.0 1.5 6.0 1 2.0 6.0 4.5 1.0 4.5 3.0 2 1.0 6.0 3.5 5.0 3.5 2.0 3 4.0 5.0 1.5 3.0 1.5 6.0 4 5.0 3.0 1.5 4.0 1.5 6.0 5 1.0 2.0 5.0 3.0 NaN 4.0 6 4.0 5.0 3.0 1.0 NaN 2.0 7 2.0 5.0 3.0 4.0 NaN 1.0 8 2.0 5.0 3.0 4.0 NaN 1.0 9 2.0 3.0 1.0 4.0 NaN 5.0
rank optionally takes a parameter ascending which by default is true; when false, data is reverse-ranked, with larger values assigned a smaller rank.
rank
ascending
rank supports different tie-breaking methods, specified with the method parameter:
average : average rank of tied group min : lowest rank in the group max : highest rank in the group first : ranks assigned in the order they appear in the array
average : average rank of tied group
average
min : lowest rank in the group
min
max : highest rank in the group
max
first : ranks assigned in the order they appear in the array
first
For working with data, a number of window functions are provided for computing common window or rolling statistics. Among these are count, sum, mean, median, correlation, variance, covariance, standard deviation, skewness, and kurtosis.
The rolling() and expanding() functions can be used directly from DataFrameGroupBy objects, see the groupby docs.
rolling()
expanding()
The API for window statistics is quite similar to the way one works with GroupBy objects, see the documentation here.
GroupBy
We work with rolling, expanding and exponentially weighted data through the corresponding objects, Rolling, Expanding and EWM.
rolling
expanding
exponentially weighted
Rolling
Expanding
EWM
In [40]: s = pd.Series(np.random.randn(1000), ....: index=pd.date_range('1/1/2000', periods=1000)) ....: In [41]: s = s.cumsum() In [42]: s Out[42]: 2000-01-01 -0.268824 2000-01-02 -1.771855 2000-01-03 -0.818003 2000-01-04 -0.659244 2000-01-05 -1.942133 ... 2002-09-22 -67.457323 2002-09-23 -69.253182 2002-09-24 -70.296818 2002-09-25 -70.844674 2002-09-26 -72.475016 Freq: D, Length: 1000, dtype: float64
These are created from methods on Series and DataFrame.
In [43]: r = s.rolling(window=60) In [44]: r Out[44]: Rolling [window=60,center=False,axis=0]
These object provide tab-completion of the available methods and properties.
In [14]: r.<TAB> # noqa: E225, E999 r.agg r.apply r.count r.exclusions r.max r.median r.name r.skew r.sum r.aggregate r.corr r.cov r.kurt r.mean r.min r.quantile r.std r.var
Generally these methods all have the same interface. They all accept the following arguments:
window: size of moving window
window
min_periods: threshold of non-null data points to require (otherwise result is NA)
center: boolean, whether to set the labels at the center (default is False)
center
We can then call methods on these rolling objects. These return like-indexed objects:
In [45]: r.mean() Out[45]: 2000-01-01 NaN 2000-01-02 NaN 2000-01-03 NaN 2000-01-04 NaN 2000-01-05 NaN ... 2002-09-22 -62.914971 2002-09-23 -63.061867 2002-09-24 -63.213876 2002-09-25 -63.375074 2002-09-26 -63.539734 Freq: D, Length: 1000, dtype: float64
In [46]: s.plot(style='k--') Out[46]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65bd288828> In [47]: r.mean().plot(style='k') Out[47]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65bd288828>
They can also be applied to DataFrame objects. This is really just syntactic sugar for applying the moving window operator to all of the DataFrame’s columns:
In [48]: df = pd.DataFrame(np.random.randn(1000, 4), ....: index=pd.date_range('1/1/2000', periods=1000), ....: columns=['A', 'B', 'C', 'D']) ....: In [49]: df = df.cumsum() In [50]: df.rolling(window=60).sum().plot(subplots=True) Out[50]: array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f6589eae4e0>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f6589e69a58>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f6589e19a20>, <matplotlib.axes._subplots.AxesSubplot object at 0x7f6589dcb9e8>], dtype=object)
We provide a number of common statistical functions:
Method
count()
Number of non-null observations
sum()
Sum of values
mean()
Mean of values
median()
Arithmetic median of values
min()
Minimum
max()
Maximum
std()
Bessel-corrected sample standard deviation
var()
Unbiased variance
skew()
Sample skewness (3rd moment)
kurt()
Sample kurtosis (4th moment)
quantile()
Sample quantile (value at %)
apply()
Generic apply
cov()
Unbiased covariance (binary)
Correlation (binary)
The apply() function takes an extra func argument and performs generic rolling computations. The func argument should be a single function that produces a single value from an ndarray input. Suppose we wanted to compute the mean absolute deviation on a rolling basis:
func
In [51]: def mad(x): ....: return np.fabs(x - x.mean()).mean() ....: In [52]: s.rolling(window=60).apply(mad, raw=True).plot(style='k') Out[52]: <matplotlib.axes._subplots.AxesSubplot at 0x7f6589c61048>
New in version 1.0.
Additionally, apply() can leverage Numba if installed as an optional dependency. The apply aggregation can be executed using Numba by specifying engine='numba' and engine_kwargs arguments (raw must also be set to True). Numba will be applied in potentially two routines:
engine='numba'
engine_kwargs
raw
True
1. If func is a standard Python function, the engine will JIT the passed function. func can also be a JITed function in which case the engine will not JIT the function again.
The engine will JIT the for loop where the apply function is applied to each window.
The engine_kwargs argument is a dictionary of keyword arguments that will be passed into the numba.jit decorator. These keyword arguments will be applied to both the passed function (if a standard Python function) and the apply for loop over each window. Currently only nogil, nopython, and parallel are supported, and their default values are set to False, True and False respectively.
nogil
nopython
parallel
False
In terms of performance, the first time a function is run using the Numba engine will be slow as Numba will have some function compilation overhead. However, rolling objects will cache the function and subsequent calls will be fast. In general, the Numba engine is performant with a larger amount of data points (e.g. 1+ million).
In [1]: data = pd.Series(range(1_000_000)) In [2]: roll = data.rolling(10) In [3]: def f(x): ...: return np.sum(x) + 5 # Run the first time, compilation time will affect performance In [4]: %timeit -r 1 -n 1 roll.apply(f, engine='numba', raw=True) # noqa: E225 1.23 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) # Function is cached and performance will improve In [5]: %timeit roll.apply(f, engine='numba', raw=True) 188 ms ± 1.93 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) In [6]: %timeit roll.apply(f, engine='cython', raw=True) 3.92 s ± 59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Passing win_type to .rolling generates a generic rolling window computation, that is weighted according the win_type. The following methods are available:
win_type
.rolling
The weights used in the window are specified by the win_type keyword. The list of recognized types are the scipy.signal window functions:
boxcar
triang
blackman
hamming
bartlett
parzen
bohman
blackmanharris
nuttall
barthann
kaiser (needs beta)
kaiser
gaussian (needs std)
gaussian
general_gaussian (needs power, width)
general_gaussian
slepian (needs width)
slepian
exponential (needs tau).
exponential
In [53]: ser = pd.Series(np.random.randn(10), ....: index=pd.date_range('1/1/2000', periods=10)) ....: In [54]: ser.rolling(window=5, win_type='triang').mean() --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-54-a42237a4feee> in <module> ----> 1 ser.rolling(window=5, win_type='triang').mean() ~/scipy/pandas/pandas/core/generic.py in rolling(self, window, min_periods, center, win_type, on, axis, closed) 10102 on=on, 10103 axis=axis, > 10104 closed=closed, 10105 ) 10106 ~/scipy/pandas/pandas/core/window/rolling.py in __init__(self, obj, window, min_periods, center, win_type, axis, on, closed, **kwargs) 92 self.win_freq = None 93 self.axis = obj._get_axis_number(axis) if axis is not None else None ---> 94 self.validate() 95 self._numba_func_cache: Dict[Optional[str], Callable] = dict() 96 ~/scipy/pandas/pandas/core/window/rolling.py in validate(self) 949 raise ValueError("window must be > 0 ") 950 import_optional_dependency( --> 951 "scipy", extra="Scipy is required to generate window weight." 952 ) 953 import scipy.signal as sig ~/scipy/pandas/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version) 89 except ImportError: 90 if raise_on_missing: ---> 91 raise ImportError(msg) from None 92 else: 93 return None ImportError: Missing optional dependency 'scipy'. Scipy is required to generate window weight. Use pip or conda to install scipy.
Note that the boxcar window is equivalent to mean().
In [55]: ser.rolling(window=5, win_type='boxcar').mean() --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-55-d77bdc3f0969> in <module> ----> 1 ser.rolling(window=5, win_type='boxcar').mean() ~/scipy/pandas/pandas/core/generic.py in rolling(self, window, min_periods, center, win_type, on, axis, closed) 10102 on=on, 10103 axis=axis, > 10104 closed=closed, 10105 ) 10106 ~/scipy/pandas/pandas/core/window/rolling.py in __init__(self, obj, window, min_periods, center, win_type, axis, on, closed, **kwargs) 92 self.win_freq = None 93 self.axis = obj._get_axis_number(axis) if axis is not None else None ---> 94 self.validate() 95 self._numba_func_cache: Dict[Optional[str], Callable] = dict() 96 ~/scipy/pandas/pandas/core/window/rolling.py in validate(self) 949 raise ValueError("window must be > 0 ") 950 import_optional_dependency( --> 951 "scipy", extra="Scipy is required to generate window weight." 952 ) 953 import scipy.signal as sig ~/scipy/pandas/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version) 89 except ImportError: 90 if raise_on_missing: ---> 91 raise ImportError(msg) from None 92 else: 93 return None ImportError: Missing optional dependency 'scipy'. Scipy is required to generate window weight. Use pip or conda to install scipy. In [56]: ser.rolling(window=5).mean() Out[56]: 2000-01-01 NaN 2000-01-02 NaN 2000-01-03 NaN 2000-01-04 NaN 2000-01-05 -0.841164 2000-01-06 -0.779948 2000-01-07 -0.565487 2000-01-08 -0.502815 2000-01-09 -0.553755 2000-01-10 -0.472211 Freq: D, dtype: float64
For some windowing functions, additional parameters must be specified:
In [57]: ser.rolling(window=5, win_type='gaussian').mean(std=0.1) --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-57-dbbb08454bfa> in <module> ----> 1 ser.rolling(window=5, win_type='gaussian').mean(std=0.1) ~/scipy/pandas/pandas/core/generic.py in rolling(self, window, min_periods, center, win_type, on, axis, closed) 10102 on=on, 10103 axis=axis, > 10104 closed=closed, 10105 ) 10106 ~/scipy/pandas/pandas/core/window/rolling.py in __init__(self, obj, window, min_periods, center, win_type, axis, on, closed, **kwargs) 92 self.win_freq = None 93 self.axis = obj._get_axis_number(axis) if axis is not None else None ---> 94 self.validate() 95 self._numba_func_cache: Dict[Optional[str], Callable] = dict() 96 ~/scipy/pandas/pandas/core/window/rolling.py in validate(self) 949 raise ValueError("window must be > 0 ") 950 import_optional_dependency( --> 951 "scipy", extra="Scipy is required to generate window weight." 952 ) 953 import scipy.signal as sig ~/scipy/pandas/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version) 89 except ImportError: 90 if raise_on_missing: ---> 91 raise ImportError(msg) from None 92 else: 93 return None ImportError: Missing optional dependency 'scipy'. Scipy is required to generate window weight. Use pip or conda to install scipy.
For .sum() with a win_type, there is no normalization done to the weights for the window. Passing custom weights of [1, 1, 1] will yield a different result than passing weights of [2, 2, 2], for example. When passing a win_type instead of explicitly specifying the weights, the weights are already normalized so that the largest weight is 1.
.sum()
[1, 1, 1]
[2, 2, 2]
In contrast, the nature of the .mean() calculation is such that the weights are normalized with respect to each other. Weights of [1, 1, 1] and [2, 2, 2] yield the same result.
.mean()
It is possible to pass an offset (or convertible) to a .rolling() method and have it produce variable sized windows based on the passed time window. For each time point, this includes all preceding values occurring within the indicated time delta.
.rolling()
This can be particularly useful for a non-regular time frequency index.
In [58]: dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, ....: index=pd.date_range('20130101 09:00:00', ....: periods=5, ....: freq='s')) ....: In [59]: dft Out[59]: B 2013-01-01 09:00:00 0.0 2013-01-01 09:00:01 1.0 2013-01-01 09:00:02 2.0 2013-01-01 09:00:03 NaN 2013-01-01 09:00:04 4.0
This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.
In [60]: dft.rolling(2).sum() Out[60]: B 2013-01-01 09:00:00 NaN 2013-01-01 09:00:01 1.0 2013-01-01 09:00:02 3.0 2013-01-01 09:00:03 NaN 2013-01-01 09:00:04 NaN In [61]: dft.rolling(2, min_periods=1).sum() Out[61]: B 2013-01-01 09:00:00 0.0 2013-01-01 09:00:01 1.0 2013-01-01 09:00:02 3.0 2013-01-01 09:00:03 2.0 2013-01-01 09:00:04 4.0
Specifying an offset allows a more intuitive specification of the rolling frequency.
In [62]: dft.rolling('2s').sum() Out[62]: B 2013-01-01 09:00:00 0.0 2013-01-01 09:00:01 1.0 2013-01-01 09:00:02 3.0 2013-01-01 09:00:03 2.0 2013-01-01 09:00:04 4.0
Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation.
In [63]: dft = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, ....: index=pd.Index([pd.Timestamp('20130101 09:00:00'), ....: pd.Timestamp('20130101 09:00:02'), ....: pd.Timestamp('20130101 09:00:03'), ....: pd.Timestamp('20130101 09:00:05'), ....: pd.Timestamp('20130101 09:00:06')], ....: name='foo')) ....: In [64]: dft Out[64]: B foo 2013-01-01 09:00:00 0.0 2013-01-01 09:00:02 1.0 2013-01-01 09:00:03 2.0 2013-01-01 09:00:05 NaN 2013-01-01 09:00:06 4.0 In [65]: dft.rolling(2).sum() Out[65]: B foo 2013-01-01 09:00:00 NaN 2013-01-01 09:00:02 1.0 2013-01-01 09:00:03 3.0 2013-01-01 09:00:05 NaN 2013-01-01 09:00:06 NaN
Using the time-specification generates variable windows for this sparse data.
In [66]: dft.rolling('2s').sum() Out[66]: B foo 2013-01-01 09:00:00 0.0 2013-01-01 09:00:02 1.0 2013-01-01 09:00:03 3.0 2013-01-01 09:00:05 NaN 2013-01-01 09:00:06 4.0
Furthermore, we now allow an optional on parameter to specify a column (rather than the default of the index) in a DataFrame.
on
In [67]: dft = dft.reset_index() In [68]: dft Out[68]: foo B 0 2013-01-01 09:00:00 0.0 1 2013-01-01 09:00:02 1.0 2 2013-01-01 09:00:03 2.0 3 2013-01-01 09:00:05 NaN 4 2013-01-01 09:00:06 4.0 In [69]: dft.rolling('2s', on='foo').sum() Out[69]: foo B 0 2013-01-01 09:00:00 0.0 1 2013-01-01 09:00:02 1.0 2 2013-01-01 09:00:03 3.0 3 2013-01-01 09:00:05 NaN 4 2013-01-01 09:00:06 4.0
In addition to accepting an integer or offset as a window argument, rolling also accepts a BaseIndexer subclass that allows a user to define a custom method for calculating window bounds. The BaseIndexer subclass will need to define a get_window_bounds method that returns a tuple of two arrays, the first being the starting indices of the windows and second being the ending indices of the windows. Additionally, num_values, min_periods, center, closed and will automatically be passed to get_window_bounds and the defined method must always accept these arguments.
BaseIndexer
get_window_bounds
num_values
closed
For example, if we have the following DataFrame:
In [70]: use_expanding = [True, False, True, False, True] In [71]: use_expanding Out[71]: [True, False, True, False, True] In [72]: df = pd.DataFrame({'values': range(5)}) In [73]: df Out[73]: values 0 0 1 1 2 2 3 3 4 4
and we want to use an expanding window where use_expanding is True otherwise a window of size 1, we can create the following BaseIndexer:
use_expanding
In [2]: from pandas.api.indexers import BaseIndexer ...: ...: class CustomIndexer(BaseIndexer): ...: ...: def get_window_bounds(self, num_values, min_periods, center, closed): ...: start = np.empty(num_values, dtype=np.int64) ...: end = np.empty(num_values, dtype=np.int64) ...: for i in range(num_values): ...: if self.use_expanding[i]: ...: start[i] = 0 ...: end[i] = i + 1 ...: else: ...: start[i] = i ...: end[i] = i + self.window_size ...: return start, end ...: In [3]: indexer = CustomIndexer(window_size=1, use_expanding=use_expanding) In [4]: df.rolling(indexer).sum() Out[4]: values 0 0.0 1 1.0 2 3.0 3 3.0 4 10.0
The inclusion of the interval endpoints in rolling window calculations can be specified with the closed parameter:
Default for
right
close right endpoint
time-based windows
left
close left endpoint
both
close both endpoints
fixed windows
neither
open endpoints
For example, having the right endpoint open is useful in many problems that require that there is no contamination from present information back to past information. This allows the rolling window to compute statistics “up to that point in time”, but not including that point in time.
In [74]: df = pd.DataFrame({'x': 1}, ....: index=[pd.Timestamp('20130101 09:00:01'), ....: pd.Timestamp('20130101 09:00:02'), ....: pd.Timestamp('20130101 09:00:03'), ....: pd.Timestamp('20130101 09:00:04'), ....: pd.Timestamp('20130101 09:00:06')]) ....: In [75]: df["right"] = df.rolling('2s', closed='right').x.sum() # default In [76]: df["both"] = df.rolling('2s', closed='both').x.sum() In [77]: df["left"] = df.rolling('2s', closed='left').x.sum() In [78]: df["neither"] = df.rolling('2s', closed='neither').x.sum() In [79]: df Out[79]: x right both left neither 2013-01-01 09:00:01 1 1.0 1.0 NaN NaN 2013-01-01 09:00:02 1 2.0 2.0 1.0 1.0 2013-01-01 09:00:03 1 2.0 3.0 2.0 1.0 2013-01-01 09:00:04 1 2.0 3.0 2.0 1.0 2013-01-01 09:00:06 1 1.0 2.0 1.0 NaN
Currently, this feature is only implemented for time-based windows. For fixed windows, the closed parameter cannot be set and the rolling window will always have both endpoints closed.
Using .rolling() with a time-based index is quite similar to resampling. They both operate and perform reductive operations on time-indexed pandas objects.
When using .rolling() with an offset. The offset is a time-delta. Take a backwards-in-time looking window, and aggregate all of the values in that window (including the end-point, but not the start-point). This is the new value at that point in the result. These are variable sized windows in time-space for each point of the input. You will get a same sized result as the input.
When using .resample() with an offset. Construct a new index that is the frequency of the offset. For each frequency bin, aggregate points from the input within a backwards-in-time looking window that fall in that bin. The result of this aggregation is the output for that frequency point. The windows are fixed size in the frequency space. Your result will have the shape of a regular frequency between the min and the max of the original input object.
.resample()
To summarize, .rolling() is a time-based window operation, while .resample() is a frequency-based window operation.
By default the labels are set to the right edge of the window, but a center keyword is available so the labels can be set at the center.
In [80]: ser.rolling(window=5).mean() Out[80]: 2000-01-01 NaN 2000-01-02 NaN 2000-01-03 NaN 2000-01-04 NaN 2000-01-05 -0.841164 2000-01-06 -0.779948 2000-01-07 -0.565487 2000-01-08 -0.502815 2000-01-09 -0.553755 2000-01-10 -0.472211 Freq: D, dtype: float64 In [81]: ser.rolling(window=5, center=True).mean() Out[81]: 2000-01-01 NaN 2000-01-02 NaN 2000-01-03 -0.841164 2000-01-04 -0.779948 2000-01-05 -0.565487 2000-01-06 -0.502815 2000-01-07 -0.553755 2000-01-08 -0.472211 2000-01-09 NaN 2000-01-10 NaN Freq: D, dtype: float64
cov() and corr() can compute moving window statistics about two Series or any combination of DataFrame/Series or DataFrame/DataFrame. Here is the behavior in each case:
DataFrame/Series
DataFrame/DataFrame
two Series: compute the statistic for the pairing.
DataFrame/Series: compute the statistics for each column of the DataFrame with the passed Series, thus returning a DataFrame.
DataFrame/DataFrame: by default compute the statistic for matching column names, returning a DataFrame. If the keyword argument pairwise=True is passed then computes the statistic for each pair of columns, returning a MultiIndexed DataFrame whose index are the dates in question (see the next section).
pairwise=True
MultiIndexed DataFrame
index
For example:
In [82]: df = pd.DataFrame(np.random.randn(1000, 4), ....: index=pd.date_range('1/1/2000', periods=1000), ....: columns=['A', 'B', 'C', 'D']) ....: In [83]: df = df.cumsum() In [84]: df2 = df[:20] In [85]: df2.rolling(window=5).corr(df2['B']) Out[85]: A B C D 2000-01-01 NaN NaN NaN NaN 2000-01-02 NaN NaN NaN NaN 2000-01-03 NaN NaN NaN NaN 2000-01-04 NaN NaN NaN NaN 2000-01-05 0.768775 1.0 -0.977990 0.800252 ... ... ... ... ... 2000-01-16 0.691078 1.0 0.807450 -0.939302 2000-01-17 0.274506 1.0 0.582601 -0.902954 2000-01-18 0.330459 1.0 0.515707 -0.545268 2000-01-19 0.046756 1.0 -0.104334 -0.419799 2000-01-20 -0.328241 1.0 -0.650974 -0.777777 [20 rows x 4 columns]
In financial data analysis and other fields it’s common to compute covariance and correlation matrices for a collection of time series. Often one is also interested in moving-window covariance and correlation matrices. This can be done by passing the pairwise keyword argument, which in the case of DataFrame inputs will yield a MultiIndexed DataFrame whose index are the dates in question. In the case of a single DataFrame argument the pairwise argument can even be omitted:
pairwise
Missing values are ignored and each entry is computed using the pairwise complete observations. Please see the covariance section for caveats associated with this method of calculating covariance and correlation matrices.
In [86]: covs = (df[['B', 'C', 'D']].rolling(window=50) ....: .cov(df[['A', 'B', 'C']], pairwise=True)) ....: In [87]: covs.loc['2002-09-22':] Out[87]: B C D 2002-09-22 A 1.367467 8.676734 -8.047366 B 3.067315 0.865946 -1.052533 C 0.865946 7.739761 -4.943924 2002-09-23 A 0.910343 8.669065 -8.443062 B 2.625456 0.565152 -0.907654 C 0.565152 7.825521 -5.367526 2002-09-24 A 0.463332 8.514509 -8.776514 B 2.306695 0.267746 -0.732186 C 0.267746 7.771425 -5.696962 2002-09-25 A 0.467976 8.198236 -9.162599 B 2.307129 0.267287 -0.754080 C 0.267287 7.466559 -5.822650 2002-09-26 A 0.545781 7.899084 -9.326238 B 2.311058 0.322295 -0.844451 C 0.322295 7.038237 -5.684445
In [88]: correls = df.rolling(window=50).corr() In [89]: correls.loc['2002-09-22':] Out[89]: A B C D 2002-09-22 A 1.000000 0.186397 0.744551 -0.769767 B 0.186397 1.000000 0.177725 -0.240802 C 0.744551 0.177725 1.000000 -0.712051 D -0.769767 -0.240802 -0.712051 1.000000 2002-09-23 A 1.000000 0.134723 0.743113 -0.758758 ... ... ... ... ... 2002-09-25 D -0.739160 -0.164179 -0.704686 1.000000 2002-09-26 A 1.000000 0.087756 0.727792 -0.736562 B 0.087756 1.000000 0.079913 -0.179477 C 0.727792 0.079913 1.000000 -0.692303 D -0.736562 -0.179477 -0.692303 1.000000 [20 rows x 4 columns]
You can efficiently retrieve the time series of correlations between two columns by reshaping and indexing:
In [90]: correls.unstack(1)[('A', 'C')].plot() Out[90]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65bd171048>
Once the Rolling, Expanding or EWM objects have been created, several methods are available to perform multiple computations on the data. These operations are similar to the aggregating API, groupby API, and resample API.
In [91]: dfa = pd.DataFrame(np.random.randn(1000, 3), ....: index=pd.date_range('1/1/2000', periods=1000), ....: columns=['A', 'B', 'C']) ....: In [92]: r = dfa.rolling(window=60, min_periods=1) In [93]: r Out[93]: Rolling [window=60,min_periods=1,center=False,axis=0]
We can aggregate by passing a function to the entire DataFrame, or select a Series (or multiple Series) via standard __getitem__.
__getitem__
In [94]: r.aggregate(np.sum) Out[94]: A B C 2000-01-01 -0.289838 -0.370545 -1.284206 2000-01-02 -0.216612 -1.675528 -1.169415 2000-01-03 1.154661 -1.634017 -1.566620 2000-01-04 2.969393 -4.003274 -1.816179 2000-01-05 4.690630 -4.682017 -2.717209 ... ... ... ... 2002-09-22 2.860036 -9.270337 6.415245 2002-09-23 3.510163 -8.151439 5.177219 2002-09-24 6.524983 -10.168078 5.792639 2002-09-25 6.409626 -9.956226 5.704050 2002-09-26 5.093787 -7.074515 6.905823 [1000 rows x 3 columns] In [95]: r['A'].aggregate(np.sum) Out[95]: 2000-01-01 -0.289838 2000-01-02 -0.216612 2000-01-03 1.154661 2000-01-04 2.969393 2000-01-05 4.690630 ... 2002-09-22 2.860036 2002-09-23 3.510163 2002-09-24 6.524983 2002-09-25 6.409626 2002-09-26 5.093787 Freq: D, Name: A, Length: 1000, dtype: float64 In [96]: r[['A', 'B']].aggregate(np.sum) Out[96]: A B 2000-01-01 -0.289838 -0.370545 2000-01-02 -0.216612 -1.675528 2000-01-03 1.154661 -1.634017 2000-01-04 2.969393 -4.003274 2000-01-05 4.690630 -4.682017 ... ... ... 2002-09-22 2.860036 -9.270337 2002-09-23 3.510163 -8.151439 2002-09-24 6.524983 -10.168078 2002-09-25 6.409626 -9.956226 2002-09-26 5.093787 -7.074515 [1000 rows x 2 columns]
As you can see, the result of the aggregation will have the selected columns, or all columns if none are selected.
With windowed Series you can also pass a list of functions to do aggregation with, outputting a DataFrame:
In [97]: r['A'].agg([np.sum, np.mean, np.std]) Out[97]: sum mean std 2000-01-01 -0.289838 -0.289838 NaN 2000-01-02 -0.216612 -0.108306 0.256725 2000-01-03 1.154661 0.384887 0.873311 2000-01-04 2.969393 0.742348 1.009734 2000-01-05 4.690630 0.938126 0.977914 ... ... ... ... 2002-09-22 2.860036 0.047667 1.132051 2002-09-23 3.510163 0.058503 1.134296 2002-09-24 6.524983 0.108750 1.144204 2002-09-25 6.409626 0.106827 1.142913 2002-09-26 5.093787 0.084896 1.151416 [1000 rows x 3 columns]
On a windowed DataFrame, you can pass a list of functions to apply to each column, which produces an aggregated result with a hierarchical index:
In [98]: r.agg([np.sum, np.mean]) Out[98]: A B C sum mean sum mean sum mean 2000-01-01 -0.289838 -0.289838 -0.370545 -0.370545 -1.284206 -1.284206 2000-01-02 -0.216612 -0.108306 -1.675528 -0.837764 -1.169415 -0.584708 2000-01-03 1.154661 0.384887 -1.634017 -0.544672 -1.566620 -0.522207 2000-01-04 2.969393 0.742348 -4.003274 -1.000819 -1.816179 -0.454045 2000-01-05 4.690630 0.938126 -4.682017 -0.936403 -2.717209 -0.543442 ... ... ... ... ... ... ... 2002-09-22 2.860036 0.047667 -9.270337 -0.154506 6.415245 0.106921 2002-09-23 3.510163 0.058503 -8.151439 -0.135857 5.177219 0.086287 2002-09-24 6.524983 0.108750 -10.168078 -0.169468 5.792639 0.096544 2002-09-25 6.409626 0.106827 -9.956226 -0.165937 5.704050 0.095068 2002-09-26 5.093787 0.084896 -7.074515 -0.117909 6.905823 0.115097 [1000 rows x 6 columns]
Passing a dict of functions has different behavior by default, see the next section.
By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame:
aggregate
In [99]: r.agg({'A': np.sum, 'B': lambda x: np.std(x, ddof=1)}) Out[99]: A B 2000-01-01 -0.289838 NaN 2000-01-02 -0.216612 0.660747 2000-01-03 1.154661 0.689929 2000-01-04 2.969393 1.072199 2000-01-05 4.690630 0.939657 ... ... ... 2002-09-22 2.860036 1.113208 2002-09-23 3.510163 1.132381 2002-09-24 6.524983 1.080963 2002-09-25 6.409626 1.082911 2002-09-26 5.093787 1.136199 [1000 rows x 2 columns]
The function names can also be strings. In order for a string to be valid it must be implemented on the windowed object
In [100]: r.agg({'A': 'sum', 'B': 'std'}) Out[100]: A B 2000-01-01 -0.289838 NaN 2000-01-02 -0.216612 0.660747 2000-01-03 1.154661 0.689929 2000-01-04 2.969393 1.072199 2000-01-05 4.690630 0.939657 ... ... ... 2002-09-22 2.860036 1.113208 2002-09-23 3.510163 1.132381 2002-09-24 6.524983 1.080963 2002-09-25 6.409626 1.082911 2002-09-26 5.093787 1.136199 [1000 rows x 2 columns]
Furthermore you can pass a nested dict to indicate different aggregations on different columns.
In [101]: r.agg({'A': ['sum', 'std'], 'B': ['mean', 'std']}) Out[101]: A B sum std mean std 2000-01-01 -0.289838 NaN -0.370545 NaN 2000-01-02 -0.216612 0.256725 -0.837764 0.660747 2000-01-03 1.154661 0.873311 -0.544672 0.689929 2000-01-04 2.969393 1.009734 -1.000819 1.072199 2000-01-05 4.690630 0.977914 -0.936403 0.939657 ... ... ... ... ... 2002-09-22 2.860036 1.132051 -0.154506 1.113208 2002-09-23 3.510163 1.134296 -0.135857 1.132381 2002-09-24 6.524983 1.144204 -0.169468 1.080963 2002-09-25 6.409626 1.142913 -0.165937 1.082911 2002-09-26 5.093787 1.151416 -0.117909 1.136199 [1000 rows x 4 columns]
A common alternative to rolling statistics is to use an expanding window, which yields the value of the statistic with all the data available up to that point in time.
These follow a similar interface to .rolling, with the .expanding method returning an Expanding object.
.expanding
As these calculations are a special case of rolling statistics, they are implemented in pandas such that the following two calls are equivalent:
In [102]: df.rolling(window=len(df), min_periods=1).mean()[:5] Out[102]: A B C D 2000-01-01 0.314226 -0.001675 0.071823 0.892566 2000-01-02 0.654522 -0.171495 0.179278 0.853361 2000-01-03 0.708733 -0.064489 -0.238271 1.371111 2000-01-04 0.987613 0.163472 -0.919693 1.566485 2000-01-05 1.426971 0.288267 -1.358877 1.808650 In [103]: df.expanding(min_periods=1).mean()[:5] Out[103]: A B C D 2000-01-01 0.314226 -0.001675 0.071823 0.892566 2000-01-02 0.654522 -0.171495 0.179278 0.853361 2000-01-03 0.708733 -0.064489 -0.238271 1.371111 2000-01-04 0.987613 0.163472 -0.919693 1.566485 2000-01-05 1.426971 0.288267 -1.358877 1.808650
These have a similar set of methods to .rolling methods.
Function
Unbiased standard deviation
Unbiased skewness (3rd moment)
Unbiased kurtosis (4th moment)
Aside from not having a window parameter, these functions have the same interfaces as their .rolling counterparts. Like above, the parameters they all accept are:
min_periods: threshold of non-null data points to require. Defaults to minimum needed to compute statistic. No NaNs will be output once min_periods non-null data points have been seen.
NaNs
center: boolean, whether to set the labels at the center (default is False).
The output of the .rolling and .expanding methods do not return a NaN if there are at least min_periods non-null values in the current window. For example:
In [104]: sn = pd.Series([1, 2, np.nan, 3, np.nan, 4]) In [105]: sn Out[105]: 0 1.0 1 2.0 2 NaN 3 3.0 4 NaN 5 4.0 dtype: float64 In [106]: sn.rolling(2).max() Out[106]: 0 NaN 1 2.0 2 NaN 3 NaN 4 NaN 5 NaN dtype: float64 In [107]: sn.rolling(2, min_periods=1).max() Out[107]: 0 1.0 1 2.0 2 2.0 3 3.0 4 3.0 5 4.0 dtype: float64
In case of expanding functions, this differs from cumsum(), cumprod(), cummax(), and cummin(), which return NaN in the output wherever a NaN is encountered in the input. In order to match the output of cumsum with expanding, use fillna():
cumsum()
cumprod()
cummax()
cummin()
cumsum
fillna()
In [108]: sn.expanding().sum() Out[108]: 0 1.0 1 3.0 2 3.0 3 6.0 4 6.0 5 10.0 dtype: float64 In [109]: sn.cumsum() Out[109]: 0 1.0 1 3.0 2 NaN 3 6.0 4 NaN 5 10.0 dtype: float64 In [110]: sn.cumsum().fillna(method='ffill') Out[110]: 0 1.0 1 3.0 2 3.0 3 6.0 4 6.0 5 10.0 dtype: float64
An expanding window statistic will be more stable (and less responsive) than its rolling window counterpart as the increasing window size decreases the relative impact of an individual data point. As an example, here is the mean() output for the previous time series dataset:
In [111]: s.plot(style='k--') Out[111]: <matplotlib.axes._subplots.AxesSubplot at 0x7f6589c770b8> In [112]: s.expanding().mean().plot(style='k') Out[112]: <matplotlib.axes._subplots.AxesSubplot at 0x7f6589c770b8>
A related set of functions are exponentially weighted versions of several of the above statistics. A similar interface to .rolling and .expanding is accessed through the .ewm method to receive an EWM object. A number of expanding EW (exponentially weighted) methods are provided:
.ewm
EW moving average
EW moving variance
EW moving standard deviation
EW moving correlation
EW moving covariance
In general, a weighted moving average is calculated as
where \(x_t\) is the input, \(y_t\) is the result and the \(w_i\) are the weights.
The EW functions support two variants of exponential weights. The default, adjust=True, uses the weights \(w_i = (1 - \alpha)^i\) which gives
adjust=True
When adjust=False is specified, moving averages are calculated as
adjust=False
which is equivalent to using weights
These equations are sometimes written in terms of \(\alpha' = 1 - \alpha\), e.g.
The difference between the above two variants arises because we are dealing with series which have finite history. Consider a series of infinite history, with adjust=True:
Noting that the denominator is a geometric series with initial term equal to 1 and a ratio of \(1 - \alpha\) we have
which is the same expression as adjust=False above and therefore shows the equivalence of the two variants for infinite series. When adjust=False, we have \(y_0 = x_0\) and \(y_t = \alpha x_t + (1 - \alpha) y_{t-1}\). Therefore, there is an assumption that \(x_0\) is not an ordinary value but rather an exponentially weighted moment of the infinite series up to that point.
One must have \(0 < \alpha \leq 1\), and while it is possible to pass \(\alpha\) directly, it’s often easier to think about either the span, center of mass (com) or half-life of an EW moment:
One must specify precisely one of span, center of mass, half-life and alpha to the EW functions:
Span corresponds to what is commonly called an “N-day EW moving average”.
Center of mass has a more physical interpretation and can be thought of in terms of span: \(c = (s - 1) / 2\).
Half-life is the period of time for the exponential weight to reduce to one half.
Alpha specifies the smoothing factor directly.
Here is an example for a univariate time series:
In [113]: s.plot(style='k--') Out[113]: <matplotlib.axes._subplots.AxesSubplot at 0x7f6589ac9668> In [114]: s.ewm(span=20).mean().plot(style='k') Out[114]: <matplotlib.axes._subplots.AxesSubplot at 0x7f6589ac9668>
EWM has a min_periods argument, which has the same meaning it does for all the .expanding and .rolling methods: no output values will be set until at least min_periods non-null values are encountered in the (expanding) window.
EWM also has an ignore_na argument, which determines how intermediate null values affect the calculation of the weights. When ignore_na=False (the default), weights are calculated based on absolute positions, so that intermediate null values affect the result. When ignore_na=True, weights are calculated by ignoring intermediate null values. For example, assuming adjust=True, if ignore_na=False, the weighted average of 3, NaN, 5 would be calculated as
ignore_na
ignore_na=False
ignore_na=True
3, NaN, 5
Whereas if ignore_na=True, the weighted average would be calculated as
The var(), std(), and cov() functions have a bias argument, specifying whether the result should contain biased or unbiased statistics. For example, if bias=True, ewmvar(x) is calculated as ewmvar(x) = ewma(x**2) - ewma(x)**2; whereas if bias=False (the default), the biased variance statistics are scaled by debiasing factors
bias
bias=True
ewmvar(x)
ewmvar(x) = ewma(x**2) - ewma(x)**2
bias=False
(For \(w_i = 1\), this reduces to the usual \(N / (N - 1)\) factor, with \(N = t + 1\).) See Weighted Sample Variance on Wikipedia for further details.