categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use 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 4.11

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 categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other python libraries that this column should be treated as a categorical variable (e.g. to use win_type=None all points are evenly weighted. To learn more about different window types see scipy.signal window functions. Examples >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use window over the values. 2. Weighted window: Weighted, non-rectangular window supplied by the scipy.signal library. 3. Expanding window: Accumulating window over the values. 4. Exponentially Weighted window: commonly used in filtering and spectral estimation. win_type must be string that corresponds to a scipy.signal window function. Scipy must be installed in order to use these windows, and supplementary arguments

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use window over the values. 2. Weighted window: Weighted, non-rectangular window supplied by the scipy.signal library. 3. Expanding window: Accumulating window over the values. 4. Exponentially Weighted window: commonly used in filtering and spectral estimation. win_type must be string that corresponds to a scipy.signal window function. Scipy must be installed in order to use these windows, and supplementary arguments

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use window over the values. 2. Weighted window: Weighted, non-rectangular window supplied by the scipy.signal library. 3. Expanding window: Accumulating window over the values. 4. Exponentially Weighted window: commonly used in filtering and spectral estimation. win_type must be string that corresponds to a scipy.signal window function. Scipy must be installed in order to use these windows, and supplementary arguments

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use window over the values. 2. Weighted window: Weighted, non-rectangular window supplied by the scipy.signal library. 3. Expanding window: Accumulating window over the values. 4. Exponentially Weighted window: commonly used in filtering and spectral estimation. win_type must be string that corresponds to a scipy.signal window function. Scipy must be installed in order to use these windows, and supplementary arguments

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use window over the values. 2. Weighted window: Weighted, non-rectangular window supplied by the scipy.signal library. 3. Expanding window: Accumulating window over the values. 4. Exponentially Weighted window: commonly used in filtering and spectral estimation. win_type must be string that corresponds to a scipy.signal window function. Scipy must be installed in order to use these windows, and supplementary arguments

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use 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 win_type=None all points are evenly weighted. To learn more about different window types see scipy.signal window functions. 1146 Chapter 4. API reference pandas: powerful Python data analysis toolkit,

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use 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 4.11 win_type=None all points are evenly weighted. To learn more about different window types see scipy.signal window functions. Examples >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0

categories, sorting and min/max will use the logical order instead of the lexical order, see here. • As a signal to other Python libraries that this column should be treated as a categorical variable (e.g. to use 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 4.11 win_type=None all points are evenly weighted. To learn more about different window types see scipy.signal window functions. Examples >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) >>> df B 0 0.0