目录
1、pandas方法
2、Dataframe属性方法
3、Series属性/方法
3.1、str属性
3.2、tolist方法
以下基于python2.7.10、pandas0.24.2测试
| 分类 | 属性/函数 | 描述 | 示例 | |
| pandas | ||||
| pd.to_datetime(arg,errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin, cache, tz, convert_listlike, result, cache_array, Series, values) | 字符串转时间 | to_datetime使用示例 最好指定format，否者可能有些时间解析是对的，有些时间解析是错的 |
||
| Dataframe | 构造函数 | |||
| pd.DataFrame([data, index, columns, dtype, copy]) | 构造数据框 | Pandas 创建DataFrame | ||
| dataframe合并 | ||||
| pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=None, copy=True) | 按schema合并dataframe | concat函数示例 | ||
| 属性和数据 | ||||
| DataFrame.axes | index: 行标签；columns: 列标签 | |||
| DataFrame.as_matrix([columns]) | 转换为矩阵 | |||
| DataFrame.dtypes | 返回数据的类型 | |||
| DataFrame.ftypes | 返回每一列的 数据类型float64:dense | |||
| DataFrame.get_dtype_counts() | 返回数据框数据类型的个数 | |||
| DataFrame.get_ftype_counts() | 返回数据框数据类型float64:dense的个数 | |||
| DataFrame.select_dtypes([include, include]) | 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() | 每一列的存储 | |||
| 类型转换 | ||||
| DataFrame.astype(dtype[, copy, errors]) | 转换数据类型 | astype函数示例 | ||
| DataFrame.copy([deep]) | deep深度复制数据 | |||
| 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 | ||||
| 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，使用名称 | |||
| DataFrame.iloc | 整型定位，使用数字 | |||
| DataFrame.insert(loc, column, value) | 在特殊地点loc[数字]插入column[列名]某列数据 | |||
| 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, …]) | 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 | ||||
| DataFrame.add(other[,axis,fill_value]) | 加法，元素指向 | |||
| DataFrame.sub(other[,axis,fill_value]) | 减法，元素指向 | |||
| DataFrame.mul(other[, axis,fill_value]) | 乘法，元素指向 | |||
| DataFrame.div(other[, axis,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,fill_value]) | 模运算，元素指向 | |||
| DataFrame.pow(other[, axis,fill_value]) | 幂运算，元素指向 | |||
| DataFrame.radd(other[, axis,fill_value]) | 右侧加法，元素指向 | |||
| DataFrame.rsub(other[, axis,fill_value]) | 右侧减法，元素指向 | |||
| DataFrame.rmul(other[, axis,fill_value]) | 右侧乘法，元素指向 | |||
| DataFrame.rdiv(other[, axis,fill_value]) | 右侧小数除法，元素指向 | |||
| DataFrame.rtruediv(other[, axis, …]) | 右侧真除法，元素指向 | |||
| DataFrame.rfloordiv(other[, axis, …]) | 右侧向下取整除法，元素指向 | |||
| DataFrame.rmod(other[, axis,fill_value]) | 右侧模运算，元素指向 | |||
| DataFrame.rpow(other[, axis,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 | ||||
| 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, …]) | 指数权重窗口 | |||
| 描述统计学 | ||||
| DataFrame.abs() | 返回绝对值 | |||
| DataFrame.all([axis, bool_only, skipna]) | Return whether all elements are True over requested axis | |||
| DataFrame.any([axis, bool_only, skipna]) | 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 | 示例 | ||
| DataFrame.diff([periods, axis]) | 1st discrete difference of object | |||
| 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 | ||||
| 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 | subset指定的字段类型需一致； | ||
| 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. | rename函数示例 | ||
| 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]) | Analogous to ndarray.take | |||
| DataFrame.truncate([before, after, axis ]) | Truncates a sorted NDFrame before and/or after some particular index value. | |||
| 处理缺失值 | ||||
| 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’. | replace示例 | ||
| 从新定型&排序&转变形态 | ||||
| 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 | ||||
| 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. | assign中使用format格式化会报错，可以通过round、astype变通处理一下 | ||
| 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 | ||||
| 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]) | 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. | |||
| 文件 | ||||
| pd.read_csv(path,[, header, skiprows, …]) | 读取csv | read_csv函数示例 | ||
| pd.read_excel(path,[, header, skiprows, …]) | 读取excel | |||
| 作图 | ||||
| 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 | ||||
| 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_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. | |||
| Series | ||||
| 字符串操作 | ||||
| str属性 | ||||
| Series.str.split(pat=None, n=-1, expand=False) | 正序分割列 | |||
| Series.str.rsplit(pat=None, n=-1, expand=False) | 逆序分割列 | |||
| 集合 | ||||
| tolist() | Series转list | |||
参考文章
1、https://blog.csdn.net/u011995719/article/details/72598935
2、http://liao.cpython.org/pandas13/#132
注意：本文归作者所有，未经作者允许，不得转载
