Release 0.12.0 Example above is the same as previous except the plot is set to kernel density estimation. This shows how easy it is to have different plots for the same Trellis structure. In [12]: plt Python data analysis toolkit, Release 0.12.0 Above is a similar plot but with 2D kernel desnity estimation plot superimposed. In [24]: plt.figure() In [25]: plot relative weightings (viewing EWMA as a moving average) bias : boolean, default False Use a standard estimation bias correction Returns y : type of input argument 472 Chapter 25. API Reference pandas: powerful

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 = DataFrame(randn(1000, 5), columns=[’a’ Release 0.14.0 Example above is the same as previous except the plot is set to kernel density estimation. This shows how easy it is to have different plots for the same Trellis structure. In [12]: plt Python data analysis toolkit, Release 0.14.0 Above is a similar plot but with 2D kernel desnity estimation plot superimposed. In [24]: plt.figure() Out[24]: In

Release 0.13.1 Example above is the same as previous except the plot is set to kernel density estimation. This shows how easy it is to have different plots for the same Trellis structure. In [12]: plt Python data analysis toolkit, Release 0.13.1 Above is a similar plot but with 2D kernel desnity estimation plot superimposed. In [24]: plt.figure() Out[24]: In relative weightings (viewing EWMA as a moving average) bias : boolean, default False Use a standard estimation bias correction Returns y : type of input argument Notes Either center of mass or span must

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 = DataFrame(randn(1000, 5), columns=[’a’ at 0xa42e05cc> Example above is the same as previous except the plot is set to kernel density estimation. This shows how easy it is to have different plots for the same Trellis structure. In [196]: plt Out[207]: Above is a similar plot but with 2D kernel density estimation plot superimposed. In [208]: plt.figure() Out[208]:

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 = DataFrame(randn(1000, 5), columns=[’a’ at 0xa473c24c> Example above is the same as previous except the plot is set to kernel density estimation. This shows how easy it is to have different plots for the same Trellis structure. In [196]: plt Out[207]: Above is a similar plot but with 2D kernel density estimation plot superimposed. In [208]: plt.figure() Out[208]:

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) especially true for text data columns with relatively few unique values (commonly referred to as “low-cardinality” data). By using more efficient data types, you can store larger datasets in memory. In [6]: ts powerful Python data analysis toolkit, Release 1.0.0 • ‘box’ : boxplot • ‘kde’ : Kernel Density Estimation plot • ‘density’ : same as ‘kde’ • ‘area’ : area plot • ‘pie’ : pie plot • ‘scatter’ : scatter

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) especially true for text data columns with relatively few unique values (commonly referred to as “low-cardinality” data). By using more efficient data types, you can store larger datasets in memory. In [6]: ts powerful Python data analysis toolkit, Release 1.0.5 • ‘box’ : boxplot • ‘kde’ : Kernel Density Estimation plot • ‘density’ : same as ‘kde’ • ‘area’ : area plot • ‘pie’ : pie plot • ‘scatter’ : scatter

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) especially true for text data columns with relatively few unique values (commonly referred to as “low-cardinality” data). By using more efficient data types, you can store larger datasets in memory. In [6]: ts powerful Python data analysis toolkit, Release 1.0.4 • ‘box’ : boxplot • ‘kde’ : Kernel Density Estimation plot • ‘density’ : same as ‘kde’ • ‘area’ : area plot • ‘pie’ : pie plot • ‘scatter’ : scatter

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) especially true for text data columns with relatively few unique values (commonly referred to as “low-cardinality” data). By using more efficient data types, you can store larger datasets in memory. In [6]: ts ‘barh’ : horizontal bar plot • ‘hist’ : histogram • ‘box’ : boxplot • ‘kde’ : Kernel Density Estimation plot • ‘density’ : same as ‘kde’ • ‘area’ : area plot • ‘pie’ : pie plot • ‘scatter’ : scatter

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) especially true for text data columns with relatively few unique values (commonly referred to as “low-cardinality” data). By using more efficient data types, you can store larger datasets in memory. In [6]: ts ‘barh’ : horizontal bar plot • ‘hist’ : histogram • ‘box’ : boxplot • ‘kde’ : Kernel Density Estimation plot • ‘density’ : same as ‘kde’ • ‘area’ : area plot • ‘pie’ : pie plot • ‘scatter’ : scatter