Pandas common statistical methods

Statistical methods

There are some statistical methods for pandas objects. Most of them are reduction and summary statistics, used to extract a single value from a series, or to extract a series from a DataFrame row or column.

For example DataFrame.mean(axis=0,skipna=True) , when an NA value exists in a dataset, these values are simply skipped, unless the entire slice (row or column) is all Na, and if you don't want to, you can skipna=False disable this feature by:

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>>> df    one  twoa  1.40NaNb  7.10 -4.5c   NaN  NaNd  0.75 -1.3[4 rows x 2 columns]>>> df.mean()one    3.083333two   -2.900000dtype: float64>>> df.mean(axis=1)a    1.400b    1.300c      NaNd   -0.275dtype: float64>>> df.mean(axis=1,skipna=False)a      NaNb    1.300c      NaNd   -0.275dtype: float64

Other commonly used statistical methods are:

########################	******************************************
Count	Number of non-NA values
Describe	Calculate summary statistics for columns of series or DF
Min, max	Minimum value and maximum value
Argmin, Argmax	Index position (integer) of minimum and maximum values
Idxmin, Idxmax	Index values for minimum and maximum values
Quantile	Sample sub-positions (0 to 1)
Sum	Sum
Mean	Mean value
Median	Number of Median
Mad	Average absolute deviation based on mean value
Var	Variance
Std	Standard deviation
Skew	The skewness of the sample value (third-order moment)
Kurt	Kurtosis of sample values (four-order moment)
Cumsum	The cumulative sum of the sample values
Cummin, Cummax	Cumulative maximum and cumulative minimum values for sample values
Cumprod	Cumulative product of sample values
Diff	Calculate first-order difference (useful for time series)
Pct_change	Calculate percent Change

Pandas common statistical methods