DataFrame — pandas 0.24.0rc1 documentation (original) (raw)
Return a Series/DataFrame with absolute numeric value of each element.
DataFrame.all([axis, bool_only, skipna, level])
Return whether all elements are True, potentially over an axis.
DataFrame.any([axis, bool_only, skipna, level])
Return whether any element is True, potentially over an axis.
DataFrame.clip([lower, upper, axis, inplace])
Trim values at input threshold(s).
DataFrame.clip_lower(threshold[, axis, inplace])
(DEPRECATED) Trim values below a given threshold.
DataFrame.clip_upper(threshold[, axis, inplace])
(DEPRECATED) Trim values above a given threshold.
DataFrame.compound([axis, skipna, level])
Return the compound percentage of the values for the requested axis.
DataFrame.corr([method, min_periods])
Compute pairwise correlation of columns, excluding NA/null values.
DataFrame.corrwith(other[, axis, drop, method])
Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Series or DataFrame.
DataFrame.count([axis, level, numeric_only])
Count non-NA cells for each column or row.
DataFrame.cov([min_periods])
Compute pairwise covariance of columns, excluding NA/null values.
DataFrame.cummax([axis, skipna])
Return cumulative maximum over a DataFrame or Series axis.
DataFrame.cummin([axis, skipna])
Return cumulative minimum over a DataFrame or Series axis.
DataFrame.cumprod([axis, skipna])
Return cumulative product over a DataFrame or Series axis.
DataFrame.cumsum([axis, skipna])
Return cumulative sum over a DataFrame or Series axis.
DataFrame.describe([percentiles, include, …])
Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
DataFrame.diff([periods, axis])
First discrete difference of element.
DataFrame.eval(expr[, inplace])
Evaluate a string describing operations on DataFrame columns.
DataFrame.kurt([axis, skipna, level, …])
Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
DataFrame.kurtosis([axis, skipna, level, …])
Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
DataFrame.mad([axis, skipna, level])
Return the mean absolute deviation of the values for the requested axis.
DataFrame.max([axis, skipna, level, …])
Return the maximum of the values for the requested axis.
DataFrame.mean([axis, skipna, level, …])
Return the mean of the values for the requested axis.
DataFrame.median([axis, skipna, level, …])
Return the median of the values for the requested axis.
DataFrame.min([axis, skipna, level, …])
Return the minimum of the values for the requested axis.
DataFrame.mode([axis, numeric_only, dropna])
Get the mode(s) of each element along the selected axis.
DataFrame.pct_change([periods, fill_method, …])
Percentage change between the current and a prior element.
DataFrame.prod([axis, skipna, level, …])
Return the product of the values for the requested axis.
DataFrame.product([axis, skipna, level, …])
Return the product of the values for the requested axis.
DataFrame.quantile([q, axis, numeric_only, …])
Return values at the given quantile over requested axis.
DataFrame.rank([axis, method, numeric_only, …])
Compute numerical data ranks (1 through n) along axis.
DataFrame.round([decimals])
Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof, …])
Return unbiased standard error of the mean over requested axis.
DataFrame.skew([axis, skipna, level, …])
Return unbiased skew over requested axis Normalized by N-1.
DataFrame.sum([axis, skipna, level, …])
Return the sum of the values for the requested axis.
DataFrame.std([axis, skipna, level, ddof, …])
Return sample standard deviation over requested axis.
DataFrame.var([axis, skipna, level, ddof, …])
Return unbiased variance over requested axis.
DataFrame.nunique([axis, dropna])
Count distinct observations over requested axis.