| DataFrame.abs() |
返回绝对值 |
| DataFrame.all([axis, bool_only, skipna, level]) |
Return whether all elements are True over requested axis |
| DataFrame.any([axis, bool_only, skipna, level]) |
Return whether any element is True over requested axis |
| DataFrame.clip([lower, upper, axis]) |
Trim values at input threshold(s). |
| DataFrame.clip_lower(threshold[, axis]) |
Return copy of the input with values below given value(s) truncated. |
| DataFrame.clip_upper(threshold[, axis]) |
Return copy of input with values above given value(s) truncated. |
| DataFrame.corr([method, min_periods]) |
返回本数据框成对列的相关性系数 |
| DataFrame.corrwith(other[, axis, drop]) |
返回不同数据框的相关性 |
| DataFrame.count([axis, level, numeric_only]) |
返回非空元素的个数 |
| DataFrame.cov([min_periods]) |
计算协方差 |
| DataFrame.cummax([axis, skipna]) |
Return cumulative max over requested axis. |
| DataFrame.cummin([axis, skipna]) |
Return cumulative minimum over requested axis. |
| DataFrame.cumprod([axis, skipna]) |
返回累积 |
| DataFrame.cumsum([axis, skipna]) |
返回累和 |
| DataFrame.describe([percentiles, include, …]) |
整体描述数据框 |
| DataFrame.diff([periods, axis]) |
1st discrete difference of object |
| DataFrame.eval(expr[, inplace]) |
Evaluate an expression in the context of the calling DataFrame instance. |
| DataFrame.kurt([axis, skipna, level, …]) |
返回无偏峰度Fisher’s (kurtosis of normal == 0.0). |
| DataFrame.mad([axis, skipna, level]) |
返回偏差 |
| DataFrame.max([axis, skipna, level, …]) |
返回最大值 |
| DataFrame.mean([axis, skipna, level, …]) |
返回均值 |
| DataFrame.median([axis, skipna, level, …]) |
返回中位数 |
| DataFrame.min([axis, skipna, level, …]) |
返回最小值 |
| DataFrame.mode([axis, numeric_only]) |
返回众数 |
| DataFrame.pct_change([periods, fill_method, …]) |
返回百分比变化 |
| DataFrame.prod([axis, skipna, level, …]) |
返回连乘积 |
| DataFrame.quantile([q, axis, numeric_only, …]) |
返回分位数 |
| DataFrame.rank([axis, method, numeric_only, …]) |
返回数字的排序 |
| DataFrame.round([decimals]) |
Round a DataFrame to a variable number of decimal places. |
| DataFrame.sem([axis, skipna, level, ddof, …]) |
返回无偏标准误 |
| DataFrame.skew([axis, skipna, level, …]) |
返回无偏偏度 |
| DataFrame.sum([axis, skipna, level, …]) |
求和 |
| DataFrame.std([axis, skipna, level, ddof, …]) |
返回标准误差 |
| DataFrame.var([axis, skipna, level, ddof, …]) |
返回无偏误差 |
