cov(self, min_periods: 'int | None' = None, ddof: 'int | None' = 1) -> 'DataFrame'
Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the :None:None:`covariance matrix
<https://en.wikipedia.org/wiki/Covariance_matrix>` of the columns of the DataFrame.
Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN
.
This method is generally used for the analysis of time series data to understand the relationship between different measures across time.
Returns the covariance matrix of the DataFrame's time series. The covariance is normalized by N-ddof.
For DataFrames that have Series that are missing data (assuming that data is :None:None:`missing at random
<https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.
However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See :None:None:`Estimation of covariance matrices
<https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
matrices>` for more details.
Minimum number of observations required per pair of columns to have a valid result.
Delta degrees of freedom. The divisor used in calculations is N - ddof
, where N
represents the number of elements.
The covariance matrix of the series of the DataFrame.
Compute pairwise covariance of columns, excluding NA/null values.
Series.cov
Compute covariance with another Series.
core.window.Expanding.cov
Expanding sample covariance.
core.window.ExponentialMovingWindow.cov
Exponential weighted sample covariance.
core.window.Rolling.cov
Rolling sample covariance.
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],This example is valid syntax, but we were not able to check execution
... columns=['dogs', 'cats'])
... df.cov() dogs cats dogs 0.666667 -1.000000 cats -1.000000 1.666667
>>> np.random.seed(42)
... df = pd.DataFrame(np.random.randn(1000, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
... df.cov() a b c d e a 0.998438 -0.020161 0.059277 -0.008943 0.014144 b -0.020161 1.059352 -0.008543 -0.024738 0.009826 c 0.059277 -0.008543 1.010670 -0.001486 -0.000271 d -0.008943 -0.024738 -0.001486 0.921297 -0.013692 e 0.014144 0.009826 -0.000271 -0.013692 0.977795
Minimum number of periods
This method also supports an optional min_periods
keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:
>>> np.random.seed(42)See :
... df = pd.DataFrame(np.random.randn(20, 3),
... columns=['a', 'b', 'c'])
... df.loc[df.index[:5], 'a'] = np.nan
... df.loc[df.index[5:10], 'b'] = np.nan
... df.cov(min_periods=12) a b c a 0.316741 NaN -0.150812 b NaN 1.248003 0.191417 c -0.150812 0.191417 0.895202
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.window.expanding.Expanding.cov
pandas.core.window.ewm.ExponentialMovingWindow.cov
pandas.core.window.rolling.Rolling.cov
pandas.core.series.Series.cov
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