@label:数据导入

pd.DataFrame() # 自己创建数据框，用于练习

pd.read_csv(filename) # 从CSV⽂件导⼊数据

pd.read_table(filename) # 从限定分隔符的⽂本⽂件导⼊数据

pd.read_excel(filename) # 从Excel⽂件导⼊数据

pd.read_sql(query,connection_object) # 从SQL表/库导⼊数据

pd.read_json(json_string) # 从JSON格式的字符串导⼊数据

pd.read_html(url) # 解析URL、字符串或者HTML⽂件，抽取其中的tables表格

@label:数据导出

df.to_csv(filename) #导出数据到CSV⽂件

df.to_excel(filename) #导出数据到Excel⽂件

df.to_sql(table_name,connection_object) #导出数据到SQL表

df.to_json(filename) #以Json格式导出数据到⽂本⽂件

writer=pd.ExcelWriter('test.xlsx',index=False) 
df1.to_excel(writer,sheet_name='单位')和writer.save()，将多个数据帧写⼊同⼀个⼯作簿的多个sheet(⼯作表)

@label:查看数据

df.head(n) # 查看DataFrame对象的前n⾏

df.tail(n) # 查看DataFrame对象的最后n⾏

df.shape() # 查看⾏数和列数

df.info() # 查看索引、数据类型和内存信息

df.columns() # 查看字段（⾸⾏）名称

df.describe() # 查看数值型列的汇总统计

s.value_counts(dropna=False) # 查看Series对象的唯⼀值和计数

df.apply(pd.Series.value_counts) # 查看DataFrame对象中每⼀列的唯⼀值和计数

df.isnull().any() # 查看是否有缺失值

df[df[column_name].duplicated()] # 查看column_name字段数据重复的数据信息

df[df[column_name].duplicated()].count() # 查看column_name字段数据重复的个数

@label:数据选取

df[col] # 根据列名，并以Series的形式返回列

df[[col1,col2]] # 以DataFrame形式返回多列

s.iloc[0] # 按位置选取数据

s.loc['index_one'] # 按索引选取数据

df.iloc[0,:] # 返回第⼀⾏

df.iloc[0,0] # 返回第⼀列的第⼀个元素

df.loc[0,:] # 返回第⼀⾏（索引为默认的数字时，⽤法同df.iloc），但需要注意的是loc是按索引,iloc参数只接受数字参数

df.ix[[:5],["col1","col2"]] # 返回字段为col1和col2的前5条数据，可以理解为loc和
iloc的结合体。

df.at[5,"col1"] # 选择索引名称为5，字段名称为col1的数据

df.iat[5,0] # 选择索引排序为5，字段排序为0的数据

@label:数据处理

df.columns= ['a','b','c'] # 重命名列名（需要将所有列名列出，否则会报错）

pd.isnull() # 检查DataFrame对象中的空值，并返回⼀个Boolean数组

pd.notnull() # 检查DataFrame对象中的⾮空值，并返回⼀个Boolean数组

df.dropna() # 删除所有包含空值的⾏

df.dropna(axis=1) # 删除所有包含空值的列

df.dropna(axis=1,thresh=n) # 删除所有⼩于n个⾮空值的⾏

df.fillna(value=x) # ⽤x替换DataFrame对象中所有的空值，⽀持

df[column_name].fillna(x)

s.astype(float) # 将Series中的数据类型更改为float类型

s.replace(1,'one') # ⽤‘one’代替所有等于1的值

s.replace([1,3],['one','three']) # ⽤'one'代替1，⽤'three'代替3

df.rename(columns=lambdax:x+1) # 批量更改列名

df.rename(columns={'old_name':'new_ name'}) # 选择性更改列名

df.set_index('column_one') # 将某个字段设为索引，可接受列表参数，即设置多个索引

df.reset_index("col1") # 将索引设置为col1字段，并将索引新设置为0,1,2...

df.rename(index=lambdax:x+1) # 批量重命名索引

@label:数据合并

df1.append(df2) # 将df2中的⾏添加到df1的尾部

df.concat([df1,df2],axis=1,join='inner') # 将df2中的列添加到df1的尾部,值为空的对应⾏与对应列都不要
# 对df1的列和df2的列执⾏SQL形式的join，默认按照索引来进⾏合并，如果df1和df2有共同字段时，会报错，
# 可通过设置lsuffix,rsuffix来进⾏解决，如果需要按照共同列进⾏合并，就要⽤到set_index(col1)
df1.join(df2.set_index(col1),on=col1,how='inner') 

pd.merge(df1,df2,on='col1',how='outer') # 对df1和df2合并，按照col1，⽅式为outer

pd.merge(df1,df2,left_index=True,right_index=True,how='outer') # 与 df1.join(df2, how='outer')效果相同

@label:数据分组统计

df.sort_index().loc[:5] # 对前5条数据进⾏索引排序

df.sort_values(col1) # 按照列col1排序数据，默认升序排列

df.sort_values(col2,ascending=False) # 按照列col1降序排列数据

df.sort_values([col1,col2],ascending=[True,False]) # 先按列col1升序排列，后按col2降序排列数据

df.groupby(col) # 返回⼀个按列col进⾏分组的Groupby对象

df.groupby([col1,col2]) # 返回⼀个按多列进⾏分组的Groupby对象

df.groupby(col1)[col2].agg(mean) # 返回按列col1进⾏分组后，列col2的均值,agg可以接受列表参数，agg([len,np.mean])

df.pivot_table(index=col1,values=[col2,col3],aggfunc={col2:max,col3:[ma,min]}) # 创建⼀个按列col1进⾏分组，计算col2的最⼤值和col3的最⼤值、最⼩值的数据透视表

df.groupby(col1).agg(np.mean) # 返回按列col1分组的所有列的均值,⽀持

df.groupby(col1).col2.agg(['min','max'])

data.apply(np.mean) # 对DataFrame中的每⼀列应⽤函数np.mean

data.apply(np.max,axis=1) # 对DataFrame中的每⼀⾏应⽤函数np.max

df.groupby(col1).col2.transform("sum") # 通常与groupby连⽤，避免索引更改

#

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

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

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.

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.

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

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 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

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]) #Analogous to ndarray.take DataFrame.truncate([before, after, axis ]) #Truncates a sorted NDFrame before and/or after some particular index value.

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’.

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

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.

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.

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.

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.