http://pandas.pydata.org/pandas-docs/stable/api.html#dataframe
构造函数
方法 | 描述 | DataFrame([data, index, columns, dtype, copy]) 构造数据框属性和数据
方法 | 描述 | Axes index: row labels；columns: column labels DataFrame.as_matrix([columns]) 转换为矩阵 DataFrame.dtypes 返回数据的类型 DataFrame.ftypes Return the ftypes (indication of sparse/dense and dtype) in this object. DataFrame.get_dtype_counts() 返回数据框数据类型的个数 DataFrame.get_ftype_counts() Return the counts of ftypes in this object. DataFrame.select_dtypes([include, exclude]) 根据数据类型选取子数据框 DataFrame.values Numpy的展示方式 DataFrame.axes 返回横纵坐标的标签名 DataFrame.ndim 返回数据框的纬度 DataFrame.size 返回数据框元素的个数 DataFrame.shape 返回数据框的形状 DataFrame.memory_usage([index, deep]) Memory usage of DataFrame columns.类型转换
方法 | 描述 | DataFrame.astype(dtype[, copy, errors]) 转换数据类型 DataFrame.copy([deep]) 复制数据框 DataFrame.isnull() 以布尔的方式返回空值 DataFrame.notnull() 以布尔的方式返回非空值索引和迭代
方法 | 描述 | DataFrame.head([n]) 返回前n行数据 DataFrame.at 快速标签常量访问器 DataFrame.iat 快速整型常量访问器 DataFrame.loc 标签定位 DataFrame.iloc 整型定位 DataFrame.insert(loc, column, value[, …]) 在特殊地点插入行 DataFrame.iter() Iterate over infor axis DataFrame.iteritems() 返回列名和序列的迭代器 DataFrame.iterrows() 返回索引和序列的迭代器 DataFrame.itertuples([index, name]) Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple. DataFrame.lookup(row_labels, col_labels) Label-based “fancy indexing” function for DataFrame. DataFrame.pop(item) 返回删除的项目 DataFrame.tail([n]) 返回最后n行 DataFrame.xs(key[, axis, level, drop_level]) Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. DataFrame.isin(values) 是否包含数据框中的元素 DataFrame.where(cond[, other, inplace, …]) 条件筛选 DataFrame.mask(cond[, other, inplace, axis, …]) Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other. DataFrame.query(expr[, inplace]) Query the columns of a frame with a boolean expression.二元运算
方法 | 描述 | DataFrame.add(other[, axis, level, fill_value]) 加法，元素指向 DataFrame.sub(other[, axis, level, fill_value]) 减法，元素指向 DataFrame.mul(other[, axis, level, fill_value]) 乘法，元素指向 DataFrame.div(other[, axis, level, fill_value]) 小数除法，元素指向 DataFrame.truediv(other[, axis, level, …]) 真除法，元素指向 DataFrame.floordiv(other[, axis, level, …]) 向下取整除法，元素指向 DataFrame.mod(other[, axis, level, fill_value]) 模运算，元素指向 DataFrame.pow(other[, axis, level, fill_value]) 幂运算，元素指向 DataFrame.radd(other[, axis, level, fill_value]) 右侧加法，元素指向 DataFrame.rsub(other[, axis, level, fill_value]) 右侧减法，元素指向 DataFrame.rmul(other[, axis, level, fill_value]) 右侧乘法，元素指向 DataFrame.rdiv(other[, axis, level, fill_value]) 右侧小数除法，元素指向 DataFrame.rtruediv(other[, axis, level, …]) 右侧真除法，元素指向 DataFrame.rfloordiv(other[, axis, level, …]) 右侧向下取整除法，元素指向 DataFrame.rmod(other[, axis, level, fill_value]) 右侧模运算，元素指向 DataFrame.rpow(other[, axis, level, fill_value]) 右侧幂运算，元素指向 DataFrame.lt(other[, axis, level]) 类似Array.lt DataFrame.gt(other[, axis, level]) 类似Array.gt DataFrame.le(other[, axis, level]) 类似Array.le DataFrame.ge(other[, axis, level]) 类似Array.ge DataFrame.ne(other[, axis, level]) 类似Array.ne DataFrame.eq(other[, axis, level]) 类似Array.eq DataFrame.combine(other, func[, fill_value, …]) Add two DataFrame objects and do not propagate NaN values, so if for a DataFrame.combine_first(other) Combine two DataFrame objects and default to non-null values in frame calling the method.函数应用&分组&窗口
方法 | 描述 | DataFrame.apply(func[, axis, broadcast, …]) 应用函数 DataFrame.applymap(func) Apply a function to a DataFrame that is intended to operate elementwise, i.e. DataFrame.aggregate(func[, axis]) Aggregate using callable, string, dict, or list of string/callables DataFrame.transform(func, *args, **kwargs) Call function producing a like-indexed NDFrame DataFrame.groupby([by, axis, level, …]) 分组 DataFrame.rolling(window[, min_periods, …]) 滚动窗口 DataFrame.expanding([min_periods, freq, …]) 拓展窗口 DataFrame.ewm([com, span, halflife, alpha, …]) 指数权重窗口描述统计学
方法 | 描述 | 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, …]) 返回无偏误差从新索引&选取&标签操作
方法 | 描述 | DataFrame.add_prefix(prefix) 添加前缀 DataFrame.add_suffix(suffix) 添加后缀 DataFrame.align(other[, join, axis, level, …]) Align two object on their axes with the DataFrame.drop(labels[, axis, level, …]) 返回删除的列 DataFrame.drop_duplicates([subset, keep, …]) Return DataFrame with duplicate rows removed, optionally only DataFrame.duplicated([subset, keep]) Return boolean Series denoting duplicate rows, optionally only DataFrame.equals(other) 两个数据框是否相同 DataFrame.filter([items, like, regex, axis]) 过滤特定的子数据框 DataFrame.first(offset) Convenience method for subsetting initial periods of time series data based on a date offset. DataFrame.head([n]) 返回前n行 DataFrame.idxmax([axis, skipna]) Return index of first occurrence of maximum over requested axis. DataFrame.idxmin([axis, skipna]) Return index of first occurrence of minimum over requested axis. DataFrame.last(offset) Convenience method for subsetting final periods of time series data based on a date offset. DataFrame.reindex([index, columns]) Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. DataFrame.reindex_axis(labels[, axis, …]) Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. DataFrame.reindex_like(other[, method, …]) Return an object with matching indices to myself. DataFrame.rename([index, columns]) Alter axes input function or functions. DataFrame.rename_axis(mapper[, axis, copy, …]) Alter index and / or columns using input function or functions. DataFrame.reset_index([level, drop, …]) For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ‘level_0’, ‘level_1’, etc. DataFrame.sample([n, frac, replace, …]) 返回随机抽样 DataFrame.select(crit[, axis]) Return data corresponding to axis labels matching criteria DataFrame.set_index(keys[, drop, append, …]) Set the DataFrame index (row labels) using one or more existing columns. DataFrame.tail([n]) 返回最后几行 DataFrame.take(indices[, axis, convert, is_copy]) Analogous to ndarray.take DataFrame.truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular index value.处理缺失值
方法 | 描述 | DataFrame.dropna([axis, how, thresh, …]) Return object with labels on given axis omitted where alternately any DataFrame.fillna([value, method, axis, …]) 填充空值 DataFrame.replace([to_replace, value, …]) Replace values given in ‘to_replace’ with ‘value’.从新定型&排序&转变形态
方法 | 描述 | DataFrame.pivot([index, columns, values]) Reshape data (produce a “pivot” table) based on column values. DataFrame.reorder_levels(order[, axis]) Rearrange index levels using input order. DataFrame.sort_values(by[, axis, ascending, …]) Sort by the values along either axis DataFrame.sort_index([axis, level, …]) Sort object by labels (along an axis) DataFrame.nlargest(n, columns[, keep]) Get the rows of a DataFrame sorted by the n largest values of columns. DataFrame.nsmallest(n, columns[, keep]) Get the rows of a DataFrame sorted by the n smallest values of columns. DataFrame.swaplevel([i, j, axis]) Swap levels i and j in a MultiIndex on a particular axis DataFrame.stack([level, dropna]) Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels. DataFrame.unstack([level, fill_value]) Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. DataFrame.melt([id_vars, value_vars, …]) “Unpivots” a DataFrame from wide format to long format, optionally DataFrame.T Transpose index and columns DataFrame.to_panel() Transform long (stacked) format (DataFrame) into wide (3D, Panel) format. DataFrame.to_xarray() Return an xarray object from the pandas object. DataFrame.transpose(*args, **kwargs) Transpose index and columnsCombining& joining&merging
方法 | 描述 | DataFrame.append(other[, ignore_index, …]) 追加数据 DataFrame.assign(**kwargs) Assign new columns to a DataFrame, returning a new object (a copy) with all the original columns in addition to the new ones. DataFrame.join(other[, on, how, lsuffix, …]) Join columns with other DataFrame either on index or on a key column. DataFrame.merge(right[, how, on, left_on, …]) Merge DataFrame objects by performing a database-style join operation by columns or indexes. DataFrame.update(other[, join, overwrite, …]) Modify DataFrame in place using non-NA values from passed DataFrame.时间序列
方法 | 描述 | DataFrame.asfreq(freq[, method, how, …]) 将时间序列转换为特定的频次 DataFrame.asof(where[, subset]) The last row without any NaN is taken (or the last row without DataFrame.shift([periods, freq, axis]) Shift index by desired number of periods with an optional time freq DataFrame.first_valid_index() Return label for first non-NA/null value DataFrame.last_valid_index() Return label for last non-NA/null value DataFrame.resample(rule[, how, axis, …]) Convenience method for frequency conversion and resampling of time series. DataFrame.to_period([freq, axis, copy]) Convert DataFrame from DatetimeIndex to PeriodIndex with desired DataFrame.to_timestamp([freq, how, axis, copy]) Cast to DatetimeIndex of timestamps, at beginning of period DataFrame.tz_convert(tz[, axis, level, copy]) Convert tz-aware axis to target time zone. DataFrame.tz_localize(tz[, axis, level, …]) Localize tz-naive TimeSeries to target time zone.作图
方法 | 描述 | DataFrame.plot([x, y, kind, ax, ….]) DataFrame plotting accessor and method DataFrame.plot.area([x, y]) 面积图Area plot DataFrame.plot.bar([x, y]) 垂直条形图Vertical bar plot DataFrame.plot.barh([x, y]) 水平条形图Horizontal bar plot DataFrame.plot.box([by]) 箱图Boxplot DataFrame.plot.density(**kwds) 核密度Kernel Density Estimate plot DataFrame.plot.hexbin(x, y[, C, …]) Hexbin plot DataFrame.plot.hist([by, bins]) 直方图Histogram DataFrame.plot.kde(**kwds) 核密度Kernel Density Estimate plot DataFrame.plot.line([x, y]) 线图Line plot DataFrame.plot.pie([y]) 饼图Pie chart DataFrame.plot.scatter(x, y[, s, c]) 散点图Scatter plot DataFrame.boxplot([column, by, ax, …]) Make a box plot from DataFrame column optionally grouped by some columns or DataFrame.hist(data[, column, by, grid, …]) Draw histogram of the DataFrame’s series using matplotlib / pylab.
