DataFrame — pandas 0.25.3 documentation (original) (raw)

DataFrame.abs(self)

Return a Series/DataFrame with absolute numeric value of each element.

DataFrame.all(self[, axis, bool_only, …])

Return whether all elements are True, potentially over an axis.

DataFrame.any(self[, axis, bool_only, …])

Return whether any element is True, potentially over an axis.

DataFrame.clip(self[, lower, upper, axis, …])

Trim values at input threshold(s).

DataFrame.clip_lower(self, threshold[, …])

(DEPRECATED) Trim values below a given threshold.

DataFrame.clip_upper(self, threshold[, …])

(DEPRECATED) Trim values above a given threshold.

DataFrame.compound(self[, axis, skipna, level])

(DEPRECATED) Return the compound percentage of the values for the requested axis.

DataFrame.corr(self[, method, min_periods])

Compute pairwise correlation of columns, excluding NA/null values.

DataFrame.corrwith(self, other[, axis, …])

Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Series or DataFrame.

DataFrame.count(self[, axis, level, …])

Count non-NA cells for each column or row.

DataFrame.cov(self[, min_periods])

Compute pairwise covariance of columns, excluding NA/null values.

DataFrame.cummax(self[, axis, skipna])

Return cumulative maximum over a DataFrame or Series axis.

DataFrame.cummin(self[, axis, skipna])

Return cumulative minimum over a DataFrame or Series axis.

DataFrame.cumprod(self[, axis, skipna])

Return cumulative product over a DataFrame or Series axis.

DataFrame.cumsum(self[, axis, skipna])

Return cumulative sum over a DataFrame or Series axis.

DataFrame.describe(self[, percentiles, …])

Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

DataFrame.diff(self[, periods, axis])

First discrete difference of element.

DataFrame.eval(self, expr[, inplace])

Evaluate a string describing operations on DataFrame columns.

DataFrame.kurt(self[, axis, skipna, level, …])

Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

DataFrame.kurtosis(self[, axis, skipna, …])

Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).

DataFrame.mad(self[, axis, skipna, level])

Return the mean absolute deviation of the values for the requested axis.

DataFrame.max(self[, axis, skipna, level, …])

Return the maximum of the values for the requested axis.

DataFrame.mean(self[, axis, skipna, level, …])

Return the mean of the values for the requested axis.

DataFrame.median(self[, axis, skipna, …])

Return the median of the values for the requested axis.

DataFrame.min(self[, axis, skipna, level, …])

Return the minimum of the values for the requested axis.

DataFrame.mode(self[, axis, numeric_only, …])

Get the mode(s) of each element along the selected axis.

DataFrame.pct_change(self[, periods, …])

Percentage change between the current and a prior element.

DataFrame.prod(self[, axis, skipna, level, …])

Return the product of the values for the requested axis.

DataFrame.product(self[, axis, skipna, …])

Return the product of the values for the requested axis.

DataFrame.quantile(self[, q, axis, …])

Return values at the given quantile over requested axis.

DataFrame.rank(self[, axis, method, …])

Compute numerical data ranks (1 through n) along axis.

DataFrame.round(self[, decimals])

Round a DataFrame to a variable number of decimal places.

DataFrame.sem(self[, axis, skipna, level, …])

Return unbiased standard error of the mean over requested axis.

DataFrame.skew(self[, axis, skipna, level, …])

Return unbiased skew over requested axis Normalized by N-1.

DataFrame.sum(self[, axis, skipna, level, …])

Return the sum of the values for the requested axis.

DataFrame.std(self[, axis, skipna, level, …])

Return sample standard deviation over requested axis.

DataFrame.var(self[, axis, skipna, level, …])

Return unbiased variance over requested axis.

DataFrame.nunique(self[, axis, dropna])

Count distinct observations over requested axis.