简介
pandas作者Wes McKinney 在【PYTHON FOR DATA ANALYSIS】中对pandas的方方面面都有了一个权威简明的入门级的介绍，但在实际使用过程中，我发现书中的内容还只是冰山一角。谈到pandas数据的行更新、表合并等操作，一般用到的方法有concat、join、merge。但这三种方法对于很多新手来说，都不太好分清使用的场合与用途。
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 columns |
Combining& 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. |
转换为其他格式
| 方法 | 描述 |
|---|---|
| DataFrame.from_csv(path[, header, sep, …]) | Read CSV file (DEPRECATED, please use pandas.read_csv() instead). |
| DataFrame.from_dict(data[, orient, dtype]) | Construct DataFrame from dict of array-like or dicts |
| DataFrame.from_items(items[, columns, orient]) | Convert (key, value) pairs to DataFrame. |
| DataFrame.from_records(data[, index, …]) | Convert structured or record ndarray to DataFrame |
| DataFrame.info([verbose, buf, max_cols, …]) | Concise summary of a DataFrame. |
| DataFrame.to_pickle(path[, compression, …]) | Pickle (serialize) object to input file path. |
| DataFrame.to_csv([path_or_buf, sep, na_rep, …]) | Write DataFrame to a comma-separated values (csv) file |
| DataFrame.to_hdf(path_or_buf, key, **kwargs) | Write the contained data to an HDF5 file using HDFStore. |
| DataFrame.to_sql(name, con[, flavor, …]) | Write records stored in a DataFrame to a SQL database. |
| DataFrame.to_dict([orient, into]) | Convert DataFrame to dictionary. |
| DataFrame.to_excel(excel_writer[, …]) | Write DataFrame to an excel sheet |
| DataFrame.to_json([path_or_buf, orient, …]) | Convert the object to a JSON string. |
| DataFrame.to_html([buf, columns, col_space, …]) | Render a DataFrame as an HTML table. |
| DataFrame.to_feather(fname) | write out the binary feather-format for DataFrames |
| DataFrame.to_latex([buf, columns, …]) | Render an object to a tabular environment table. |
| DataFrame.to_stata(fname[, convert_dates, …]) | A class for writing Stata binary dta files from array-like objects |
| DataFrame.to_msgpack([path_or_buf, encoding]) | msgpack (serialize) object to input file path |
| DataFrame.to_gbq(destination_table, project_id) | Write a DataFrame to a Google BigQuery table. |
| DataFrame.to_records([index, convert_datetime64]) | Convert DataFrame to record array. |
| DataFrame.to_sparse([fill_value, kind]) | Convert to SparseDataFrame |
| DataFrame.to_dense() | Return dense representation of NDFrame (as opposed to sparse) |
| DataFrame.to_string([buf, columns, …]) | Render a DataFrame to a console-friendly tabular output. |
| DataFrame.to_clipboard([excel, sep]) | Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example. |