pd.eval("@a + b") File ~/work/pandas/pandas/pandas/core/computation/eval.py:325 in eval _check_for_locals(expr, level, parser) File ~/work/pandas/pandas/pandas/core/computation/eval.py:167 in _check_for_locals raise SyntaxError(msg) File <string> SyntaxError: The '@' prefix is not...
>>>np.allclose(df1+df2+df3+df4,pd.eval('df1+df2+df3+df4')) True 2、eval()支持的运算方式 eval()函数支持多种运算方式,如:算数运算、比较运算和布尔运算,同时也支持对象属性与索引的表达方式; eval()函数目前还不支持函数条用,if条件语句,循环语句及更为复杂的运算。 3、DataFrame.eval()实现列间...
eval()也可以处理未对齐的pandas对象。 In[19]:s=pd.Series(np.random.randn(50))In[20]:%timeitdf1+df2+df3+df4+s54.6ms+-1.92msperloop(mean+-std.dev.of7runs,10loopseach)In[21]:%timeitpd.eval('df1 + df2 + df3 + df4 + s')19.1ms+-1.53msperloop(mean+-std.dev.of7runs,100loops...
eval(str(row['计算过程'])) #将占比的列补充一个(%) dname = {} for c in df.columns: if str(c).find('占比') != -1 or str(c).find('率') != -1 or str(c).find('比率') != -1 or str(c).find('净值') != -1: if str(c).find('%') == -1: dname[str(c)] ...
pandas.eval跨DataFrame运算 当你有多个pandas DataFrame的时候,你可以用pandas.eval进行DataFrame objects相互间的运算,例如: import pandas as pd nrows, ncols = 1_000_000, 100 df1, df2, df3, df4 = (pd.DataFrame(rng.random((nrows, ncols))) for i in range(4)) 如果直接使用传统的pandas方式计...
if pd.isna(values[4]): fix.append(values[1]) continue lst = eval(values[4]) str = list(values[1]) for it in lst: str[it['position']] = list(it['correction'].values())[0] fix.append(''.join(str)) dic = {"fixed" : fix} ...
定期使用(memory_usage='deep')监控内存 对分类数据优先使用category数据类型 复杂转换操作优先使用pd.eval()实现向量化 通过掌握这些高级技巧,开发者可以构建出响应快速、数据精准的商业级数据仪表板。实际项目中建议结合Dask等扩展库处理超大规模数据集,并采用分层缓存策略优化高频查询。
import pandas as pdfuncs = [_ for _ in dir(pd) if not _.startswith('_')]types = type(pd.DataFrame), type(pd.array), type(pd)Names = 'Type','Function','Module','Other'Types = {}for f in funcs:t = type(eval("pd."+f))t = Names[-1 if t not in types else types.inde...
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 ...
equals() Returns True if two DataFrames are equal, otherwise False eval Evaluate a specified string explode() Converts each element into a row ffill() Replaces NULL values with the value from the previous row fillna() Replaces NULL values with the specified value filter() Filter the DataFram...