python下的Pandas中DataFrame基本操作，基本函數整理

簡介

pandas作者Wes McKinney 在【PYTHON FOR DATA ANALYSIS】中對pandas的方方面面都有了一個權威簡明的入門級的介紹，但在實際使用過程中，我發現書中的內容還只是冰山一角。談到pandas數據的行更新、表合併等操作，一般用到的方法有concat、join、merge。但這三種方法對於很多新手來說，都不太好分清使用的場合與用途。

構造函數

方法	描述
DataFrame([data, index, columns, dtype, copy])	構造數據框

屬性和數據

方法	描述
Axes	index: row labels；columns: column labels
DataFrame.as_matrix([columns])	轉換爲矩陣
DataFrame.dtypes	返回數據的類型
DataFrame.ftypes	Return the ftypes (indication of sparse/dense and dtype) in this object.
DataFrame.get_dtype_counts()	返回數據框數據類型的個數
DataFrame.get_ftype_counts()	Return the counts of ftypes in this object.
DataFrame.select_dtypes([include, exclude])	根據數據類型選取子數據框
DataFrame.values	Numpy的展示方式
DataFrame.axes	返回橫縱座標的標籤名
DataFrame.ndim	返回數據框的緯度
DataFrame.size	返回數據框元素的個數
DataFrame.shape	返回數據框的形狀
DataFrame.memory_usage([index, deep])	Memory usage of DataFrame columns.

類型轉換

方法	描述
DataFrame.astype(dtype[, copy, errors])	轉換數據類型
DataFrame.copy([deep])	複製數據框
DataFrame.isnull()	以布爾的方式返回空值
DataFrame.notnull()	以布爾的方式返回非空值

索引和迭代

方法	描述
DataFrame.head([n])	返回前n行數據
DataFrame.at	快速標籤常量訪問器
DataFrame.iat	快速整型常量訪問器
DataFrame.loc	標籤定位
DataFrame.iloc	整型定位
DataFrame.insert(loc, column, value[, …])	在特殊地點插入行
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)	返回刪除的項目
DataFrame.tail([n])	返回最後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)	是否包含數據框中的元素
DataFrame.where(cond[, other, inplace, …])	條件篩選
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.

二元運算

方法	描述
DataFrame.add(other[, axis, level, fill_value])	加法，元素指向
DataFrame.sub(other[, axis, level, fill_value])	減法，元素指向
DataFrame.mul(other[, axis, level, fill_value])	乘法，元素指向
DataFrame.div(other[, axis, level, fill_value])	小數除法，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])	模運算，元素指向
DataFrame.pow(other[, axis, level, fill_value])	冪運算，元素指向
DataFrame.radd(other[, axis, level, fill_value])	右側加法，元素指向
DataFrame.rsub(other[, axis, level, fill_value])	右側減法，元素指向
DataFrame.rmul(other[, axis, level, fill_value])	右側乘法，元素指向
DataFrame.rdiv(other[, axis, level, fill_value])	右側小數除法，元素指向
DataFrame.rtruediv(other[, axis, level, …])	右側真除法，元素指向
DataFrame.rfloordiv(other[, axis, level, …])	右側向下取整除法，元素指向
DataFrame.rmod(other[, axis, level, fill_value])	右側模運算，元素指向
DataFrame.rpow(other[, axis, level, fill_value])	右側冪運算，元素指向
DataFrame.lt(other[, axis, level])	類似Array.lt
DataFrame.gt(other[, axis, level])	類似Array.gt
DataFrame.le(other[, axis, level])	類似Array.le
DataFrame.ge(other[, axis, level])	類似Array.ge
DataFrame.ne(other[, axis, level])	類似Array.ne
DataFrame.eq(other[, axis, level])	類似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.

函數應用&分組&窗口

方法	描述
DataFrame.apply(func[, axis, broadcast, …])	應用函數
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, …])	分組
DataFrame.rolling(window[, min_periods, …])	滾動窗口
DataFrame.expanding([min_periods, freq, …])	拓展窗口
DataFrame.ewm([com, span, halflife, alpha, …])	指數權重窗口

描述統計學

方法	描述
DataFrame.abs()	返回絕對值
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])	返回本數據框成對列的相關性係數
DataFrame.corrwith(other[, axis, drop])	返回不同數據框的相關性
DataFrame.count([axis, level, numeric_only])	返回非空元素的個數
DataFrame.cov([min_periods])	計算協方差
DataFrame.cummax([axis, skipna])	Return cumulative max over requested axis.
DataFrame.cummin([axis, skipna])	Return cumulative minimum over requested axis.
DataFrame.cumprod([axis, skipna])	返回累積
DataFrame.cumsum([axis, skipna])	返回累和
DataFrame.describe([percentiles, include, …])	整體描述數據框
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, …])	返回無偏峯度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, …])	返回中位數
DataFrame.min([axis, skipna, level, …])	返回最小值
DataFrame.mode([axis, numeric_only])	返回衆數
DataFrame.pct_change([periods, fill_method, …])	返回百分比變化
DataFrame.prod([axis, skipna, level, …])	返回連乘積
DataFrame.quantile([q, axis, numeric_only, …])	返回分位數
DataFrame.rank([axis, method, numeric_only, …])	返回數字的排序
DataFrame.round([decimals])	Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof, …])	返回無偏標準誤
DataFrame.skew([axis, skipna, level, …])	返回無偏偏度
DataFrame.sum([axis, skipna, level, …])	求和
DataFrame.std([axis, skipna, level, ddof, …])	返回標準誤差
DataFrame.var([axis, skipna, level, ddof, …])	返回無偏誤差

從新索引&選取&標籤操作

方法	描述
DataFrame.add_prefix(prefix)	添加前綴
DataFrame.add_suffix(suffix)	添加後綴
DataFrame.align(other[, join, axis, level, …])	Align two object on their axes with the
DataFrame.drop(labels[, axis, level, …])	返回刪除的列
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)	兩個數據框是否相同
DataFrame.filter([items, like, regex, axis])	過濾特定的子數據框
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, …])	返回隨機抽樣
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])	返回最後幾行
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.

處理缺失值

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’.

從新定型&排序&轉變形態

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, …])	追加數據
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.

時間序列

方法	描述
DataFrame.asfreq(freq[, method, how, …])	將時間序列轉換爲特定的頻次
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.

作圖

方法	描述
DataFrame.plot([x, y, kind, ax, ….])	DataFrame plotting accessor and method
DataFrame.plot.area([x, y])	面積圖Area plot
DataFrame.plot.bar([x, y])	垂直條形圖Vertical bar plot
DataFrame.plot.barh([x, y])	水平條形圖Horizontal bar plot
DataFrame.plot.box([by])	箱圖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])	直方圖Histogram
DataFrame.plot.kde(**kwds)	核密度Kernel Density Estimate plot
DataFrame.plot.line([x, y])	線圖Line plot
DataFrame.plot.pie([y])	餅圖Pie chart
DataFrame.plot.scatter(x, y[, s, c])	散點圖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.

轉換爲其他格式

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.

參考文獻：

http://pandas.pydata.org/pandas-docs/stable/api.html#dataframe