建構函式

DataFrame([data,index,columns,dtype,copy]) #構造資料框

屬性和資料

DataFrame.axes                #index: 行標籤；columns: 列標籤
DataFrame.as_matrix([columns])        #轉換為矩陣
DataFrame.dtypes               #返回資料的型別
DataFrame.ftypes               #返回每一列的 資料型別float64:dense
DataFrame.get_dtype_counts()         #返回資料框資料型別的個數
DataFrame.get_ftype_counts()         #返回資料框資料型別float64:dense的個數
DataFrame.select_dtypes([include,include])  #根據資料型別選取子資料框
DataFrame.values               #Numpy的展示方式
DataFrame.axes                #返回橫縱座標的標籤名
DataFrame.ndim                #返回資料框的緯度
DataFrame.size                #返回資料框元素的個數
DataFrame.shape                #返回資料框的形狀
DataFrame.memory_usage()           #每一列的儲存

型別轉換

DataFrame.astype(dtype[,copy,errors])    #轉換資料型別
DataFrame.copy([deep])            #deep深度複製資料
DataFrame.isnull()              #以布林的方式返回空值
DataFrame.notnull()              #以布林的方式返回非空值

索引和迭代

DataFrame.head([n])              #返回前n行資料
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，使用名稱
DataFrame.iloc                #整型定位，使用數字
DataFrame.insert(loc,column,value)     #在特殊地點loc[數字]插入column[列名]某列資料
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)              #返回刪除的專案
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)            #是否包含資料框中的元素
DataFrame.where(cond[,other,inplace,…])  #條件篩選
DataFrame.mask(cond[,…])   #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

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

函式應用&分組&視窗

DataFrame.apply(func[,broadcast,…])  #應用函式
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,…])    #分組
DataFrame.rolling(window[,min_periods,…])  #滾動視窗
DataFrame.expanding([min_periods,freq,…])  #拓展視窗
DataFrame.ewm([com,span,halflife,…])   #指數權重視窗

描述統計學

DataFrame.abs()                #返回絕對值
DataFrame.all([axis,bool_only,skipna])   #Return whether all elements are True over requested axis
DataFrame.any([axis,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])     #返回本資料框成對列的相關性係數
DataFrame.corrwith(other[,drop])    #返回不同資料框的相關性
DataFrame.count([axis,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,…]) #整體描述資料框
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,…])   #返回無偏峰度Fisher's (kurtosis of normal == 0.0).
DataFrame.mad([axis,level])     #返回偏差
DataFrame.max([axis,…])    #返回最大值
DataFrame.mean([axis,…])   #返回均值
DataFrame.median([axis,…])  #返回中位數
DataFrame.min([axis,…])    #返回最小值
DataFrame.mode([axis,numeric_only])     #返回眾數
DataFrame.pct_change([periods,fill_method]) #返回百分比變化
DataFrame.prod([axis,…])   #返回連乘積
DataFrame.quantile([q,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,ddof])  #返回無偏標準誤
DataFrame.skew([axis,…])   #返回無偏偏度
DataFrame.sum([axis,…])    #求和
DataFrame.std([axis,ddof])  #返回標準誤差
DataFrame.var([axis,ddof])  #返回無偏誤差 

從新索引&選取&標籤操作

DataFrame.add_prefix(prefix)         #新增字首
DataFrame.add_suffix(suffix)         #新增字尾
DataFrame.align(other[,join,level])  #Align two object on their axes with the
DataFrame.drop(labels[,…])   #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)            #兩個資料框是否相同
DataFrame.filter([items,like,regex,axis]) #過濾特定的子資料框
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[,…])   #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[,…])  #Return an object with matching indices to myself.
DataFrame.rename([index,columns])      #Alter axes input function or functions.
DataFrame.rename_axis(mapper[,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[,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[,convert])   #Analogous to ndarray.take
DataFrame.truncate([before,after,axis ])  #Truncates a sorted NDFrame before and/or after some particular index value.

處理缺失值

DataFrame.dropna([axis,how,thresh,…])   #Return object with labels on given axis omitted where alternately any
DataFrame.fillna([value,…])  #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

DataFrame.pivot([index,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[,ascending]) #Sort by the values along either axis
DataFrame.sort_index([axis,…])    #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,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,…])  #追加資料
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,lsuffix,…]) #Join columns with other DataFrame either on index or on a key column.
DataFrame.merge(right[,left_on,…]) #Merge DataFrame objects by performing a database-style join operation by columns or indexes.
DataFrame.update(other[,overwrite,…]) #Modify DataFrame in place using non-NA values from passed DataFrame.

時間序列

DataFrame.asfreq(freq[,…])   #將時間序列轉換為特定的頻次
DataFrame.asof(where[,subset])        #The last row without any NaN is taken (or the last row without
DataFrame.shift([periods,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[,…])   #Convenience method for frequency conversion and resampling of time series.
DataFrame.to_period([freq,copy])    #Convert DataFrame from DatetimeIndex to PeriodIndex with desired
DataFrame.to_timestamp([freq,axis])   #Cast to DatetimeIndex of timestamps,at beginning of period
DataFrame.tz_convert(tz[,copy]) #Convert tz-aware axis to target time zone.
DataFrame.tz_localize(tz[,…])  #Localize tz-naive TimeSeries to target time zone.

作圖

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,s,c])     #散點圖Scatter plot
DataFrame.boxplot([column,by,…])    #Make a box plot from DataFrame column optionally grouped by some columns or
DataFrame.hist(data[,grid,…])  #Draw histogram of the DataFrame's series using matplotlib / pylab.

轉換為其他格式

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[,orient]) #Convert (key,value) pairs to DataFrame.
DataFrame.from_records(data[,…])   #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,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,…])  #Convert the object to a JSON string.
DataFrame.to_html([buf,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,…])     #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,…])    #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.

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