Pandas中DataFrame基本函数整理(小结)
构造函数
DataFrame([data,index,columns,dtype,copy])#构造数据框
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 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()#Iterateoverinforaxis 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])#IterateoverDataFramerowsasnamedtuples,withindexvalueasfirstelementofthetuple. DataFrame.lookup(row_labels,col_labels)#Label-based“fancyindexing”functionforDataFrame. 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])#Returnsacross-section(row(s)orcolumn(s))fromtheSeries/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,…])#ReturnanobjectofsameshapeasselfandwhosecorrespondingentriesarefromselfwherecondisFalseandotherwisearefromother. DataFrame.query(expr[,inplace])#Querythecolumnsofaframewithabooleanexpression.
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,…])#AddtwoDataFrameobjectsanddonotpropagateNaNvalues,soiffora DataFrame.combine_first(other)#CombinetwoDataFrameobjectsanddefaulttonon-nullvaluesinframecallingthemethod.
函数应用&分组&窗口
DataFrame.apply(func[,axis,broadcast,…])#应用函数 DataFrame.applymap(func)#ApplyafunctiontoaDataFramethatisintendedtooperateelementwise,i.e. DataFrame.aggregate(func[,axis])#Aggregateusingcallable,string,dict,orlistofstring/callables DataFrame.transform(func,*args,**kwargs)#Callfunctionproducingalike-indexedNDFrame 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])#ReturnwhetherallelementsareTrueoverrequestedaxis DataFrame.any([axis,bool_only,skipna])#ReturnwhetheranyelementisTrueoverrequestedaxis DataFrame.clip([lower,upper,axis])#Trimvaluesatinputthreshold(s). DataFrame.clip_lower(threshold[,axis])#Returncopyoftheinputwithvaluesbelowgivenvalue(s)truncated. DataFrame.clip_upper(threshold[,axis])#Returncopyofinputwithvaluesabovegivenvalue(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])#Returncumulativemaxoverrequestedaxis. DataFrame.cummin([axis,skipna])#Returncumulativeminimumoverrequestedaxis. 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])#1stdiscretedifferenceofobject DataFrame.eval(expr[,inplace])#EvaluateanexpressioninthecontextofthecallingDataFrameinstance. DataFrame.kurt([axis,skipna,level,…])#返回无偏峰度Fisher's(kurtosisofnormal==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])#RoundaDataFrametoavariablenumberofdecimalplaces. 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])#Aligntwoobjectontheiraxeswiththe DataFrame.drop(labels[,axis,level,…])#返回删除的列 DataFrame.drop_duplicates([subset,keep,…])#ReturnDataFramewithduplicaterowsremoved,optionallyonly DataFrame.duplicated([subset,keep])#ReturnbooleanSeriesdenotingduplicaterows,optionallyonly 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)#Conveniencemethodforsubsettinginitialperiodsoftimeseriesdatabasedonadateoffset. DataFrame.head([n])#返回前n行 DataFrame.idxmax([axis,skipna])#Returnindexoffirstoccurrenceofmaximumoverrequestedaxis. DataFrame.idxmin([axis,skipna])#Returnindexoffirstoccurrenceofminimumoverrequestedaxis. DataFrame.last(offset)#Conveniencemethodforsubsettingfinalperiodsoftimeseriesdatabasedonadateoffset. DataFrame.reindex([index,columns])#ConformDataFrametonewindexwithoptionalfillinglogic,placingNA/NaNinlocationshavingnovalueinthepreviousindex. DataFrame.reindex_axis(labels[,axis,…])#Conforminputobjecttonewindexwithoptionalfillinglogic,placingNA/NaNinlocationshavingnovalueinthepreviousindex. DataFrame.reindex_like(other[,method,…])#Returnanobjectwithmatchingindicestomyself. DataFrame.rename([index,columns])#Alteraxesinputfunctionorfunctions. DataFrame.rename_axis(mapper[,axis,copy])#Alterindexand/orcolumnsusinginputfunctionorfunctions. DataFrame.reset_index([level,drop,…])#ForDataFramewithmulti-levelindex,returnnewDataFramewithlabelinginformationinthecolumnsundertheindexnames,defaultingto‘level_0',‘level_1',etc. DataFrame.sample([n,frac,replace,…])#返回随机抽样 DataFrame.select(crit[,axis])#Returndatacorrespondingtoaxislabelsmatchingcriteria DataFrame.set_index(keys[,drop,append])#SettheDataFrameindex(rowlabels)usingoneormoreexistingcolumns. DataFrame.tail([n])#返回最后几行 DataFrame.take(indices[,axis,convert])#Analogoustondarray.take DataFrame.truncate([before,after,axis])#TruncatesasortedNDFramebeforeand/oraftersomeparticularindexvalue.
处理缺失值
DataFrame.dropna([axis,how,thresh,…])#Returnobjectwithlabelsongivenaxisomittedwherealternatelyany DataFrame.fillna([value,method,axis,…])#填充空值 DataFrame.replace([to_replace,value,…])#Replacevaluesgivenin‘to_replace'with‘value'.
从新定型&排序&转变形态
DataFrame.pivot([index,columns,values])#Reshapedata(producea“pivot”table)basedoncolumnvalues. DataFrame.reorder_levels(order[,axis])#Rearrangeindexlevelsusinginputorder. DataFrame.sort_values(by[,axis,ascending])#Sortbythevaluesalongeitheraxis DataFrame.sort_index([axis,level,…])#Sortobjectbylabels(alonganaxis) DataFrame.nlargest(n,columns[,keep])#GettherowsofaDataFramesortedbythenlargestvaluesofcolumns. DataFrame.nsmallest(n,columns[,keep])#GettherowsofaDataFramesortedbythensmallestvaluesofcolumns. DataFrame.swaplevel([i,j,axis])#SwaplevelsiandjinaMultiIndexonaparticularaxis DataFrame.stack([level,dropna])#Pivotalevelofthe(possiblyhierarchical)columnlabels,returningaDataFrame(orSeriesinthecaseofanobjectwithasinglelevelofcolumnlabels)havingahierarchicalindexwithanewinner-mostlevelofrowlabels. DataFrame.unstack([level,fill_value])#Pivotalevelofthe(necessarilyhierarchical)indexlabels,returningaDataFramehavinganewlevelofcolumnlabelswhoseinner-mostlevelconsistsofthepivotedindexlabels. DataFrame.melt([id_vars,value_vars,…])#“Unpivots”aDataFramefromwideformattolongformat,optionally DataFrame.T#Transposeindexandcolumns DataFrame.to_panel()#Transformlong(stacked)format(DataFrame)intowide(3D,Panel)format. DataFrame.to_xarray()#Returnanxarrayobjectfromthepandasobject. DataFrame.transpose(*args,**kwargs)#Transposeindexandcolumns
Combining&joining&merging
DataFrame.append(other[,ignore_index,…])#追加数据 DataFrame.assign(**kwargs)#AssignnewcolumnstoaDataFrame,returninganewobject(acopy)withalltheoriginalcolumnsinadditiontothenewones. DataFrame.join(other[,on,how,lsuffix,…])#JoincolumnswithotherDataFrameeitheronindexoronakeycolumn. DataFrame.merge(right[,how,on,left_on,…])#MergeDataFrameobjectsbyperformingadatabase-stylejoinoperationbycolumnsorindexes. DataFrame.update(other[,join,overwrite,…])#ModifyDataFrameinplaceusingnon-NAvaluesfrompassedDataFrame.
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])#ThelastrowwithoutanyNaNistaken(orthelastrowwithout DataFrame.shift([periods,freq,axis])#Shiftindexbydesirednumberofperiodswithanoptionaltimefreq DataFrame.first_valid_index()#Returnlabelforfirstnon-NA/nullvalue DataFrame.last_valid_index()#Returnlabelforlastnon-NA/nullvalue DataFrame.resample(rule[,how,axis,…])#Conveniencemethodforfrequencyconversionandresamplingoftimeseries. DataFrame.to_period([freq,axis,copy])#ConvertDataFramefromDatetimeIndextoPeriodIndexwithdesired DataFrame.to_timestamp([freq,how,axis])#CasttoDatetimeIndexoftimestamps,atbeginningofperiod DataFrame.tz_convert(tz[,axis,level,copy])#Converttz-awareaxistotargettimezone. DataFrame.tz_localize(tz[,axis,level,…])#Localizetz-naiveTimeSeriestotargettimezone.
作图
DataFrame.plot([x,y,kind,ax,….])#DataFrameplottingaccessorandmethod DataFrame.plot.area([x,y])#面积图Areaplot DataFrame.plot.bar([x,y])#垂直条形图Verticalbarplot DataFrame.plot.barh([x,y])#水平条形图Horizontalbarplot DataFrame.plot.box([by])#箱图Boxplot DataFrame.plot.density(**kwds)#核密度KernelDensityEstimateplot DataFrame.plot.hexbin(x,y[,C,…])#Hexbinplot DataFrame.plot.hist([by,bins])#直方图Histogram DataFrame.plot.kde(**kwds)#核密度KernelDensityEstimateplot DataFrame.plot.line([x,y])#线图Lineplot DataFrame.plot.pie([y])#饼图Piechart DataFrame.plot.scatter(x,y[,s,c])#散点图Scatterplot DataFrame.boxplot([column,by,ax,…])#MakeaboxplotfromDataFramecolumnoptionallygroupedbysomecolumnsor DataFrame.hist(data[,column,by,grid,…])#DrawhistogramoftheDataFrame'sseriesusingmatplotlib/pylab.
转换为其他格式
DataFrame.from_csv(path[,header,sep,…])#ReadCSVfile(DEPRECATED,pleaseusepandas.read_csv()instead). DataFrame.from_dict(data[,orient,dtype])#ConstructDataFramefromdictofarray-likeordicts DataFrame.from_items(items[,columns,orient])#Convert(key,value)pairstoDataFrame. DataFrame.from_records(data[,index,…])#ConvertstructuredorrecordndarraytoDataFrame DataFrame.info([verbose,buf,max_cols,…])#ConcisesummaryofaDataFrame. DataFrame.to_pickle(path[,compression,…])#Pickle(serialize)objecttoinputfilepath. DataFrame.to_csv([path_or_buf,sep,na_rep])#WriteDataFrametoacomma-separatedvalues(csv)file DataFrame.to_hdf(path_or_buf,key,**kwargs)#WritethecontaineddatatoanHDF5fileusingHDFStore. DataFrame.to_sql(name,con[,flavor,…])#WriterecordsstoredinaDataFrametoaSQLdatabase. DataFrame.to_dict([orient,into])#ConvertDataFrametodictionary. DataFrame.to_excel(excel_writer[,…])#WriteDataFrametoanexcelsheet DataFrame.to_json([path_or_buf,orient,…])#ConverttheobjecttoaJSONstring. DataFrame.to_html([buf,columns,col_space])#RenderaDataFrameasanHTMLtable. DataFrame.to_feather(fname)#writeoutthebinaryfeather-formatforDataFrames DataFrame.to_latex([buf,columns,…])#Renderanobjecttoatabularenvironmenttable. DataFrame.to_stata(fname[,convert_dates,…])#AclassforwritingStatabinarydtafilesfromarray-likeobjects DataFrame.to_msgpack([path_or_buf,encoding])#msgpack(serialize)objecttoinputfilepath DataFrame.to_sparse([fill_value,kind])#ConverttoSparseDataFrame DataFrame.to_dense()#ReturndenserepresentationofNDFrame(asopposedtosparse) DataFrame.to_string([buf,columns,…])#RenderaDataFrametoaconsole-friendlytabularoutput. DataFrame.to_clipboard([excel,sep])#AttempttowritetextrepresentationofobjecttothesystemclipboardThiscanbepastedintoExcel,forexample.
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