df.loc["Row_Total"] = df.sum() df.loc[:,"Column_Total"] = df.sum(axis=1) 2、如果有文字 import pandas as pd data = [('a',1,2,3),('b',4,5,6),('c',7,8,9),('d',10,11,12)] df = pd.DataFrame(data,columns=('col1', 'col2', 'col3','col4')) df.loc['Col...
pl.sum('value2').alias('sum_value2') ]) group_time_pl = time.time() - start # 打印结果...
In [53]: A, rows, columns = ss.sparse.to_coo( ...: row_levels=["A", "B", "C"], column_levels=["D"], sort_labels=False ...: ) ...: In [54]: A Out[54]: <3x2 sparse matrix of type '<class 'numpy.float64'>' with 3 stored elements in COOrdinate format> In [55]...
values="E", ...: index=["B", "C"], ...: columns=["A"], ...: aggfunc=["sum", "mean"], ...: ) ...: Out[14]: sum mean A one three two one three two B C A bar -0.471593 -2.008182 NaN -0.235796 -1.004091 NaN foo 0.761726 NaN -1.067650 0.380863 NaN -0.533825 B bar...
当axis=0时,对每列columns执行指定函数;当axis=1时,对每行row执行指定函数。无论axis=0还是axis=1,其传入指定函数的默认形式均为Series,可以通过设置raw=True传入numpy数组。对每个Series执行结果后,会将结果整合在一起返回(若想有返回值,定义函数时需要return相应的值)。当然,DataFrame的apply和Series的apply一样,...
row_mean = people[columns_name].mean(axis=1) row_sum = people[columns_name].sum(axis=1) total = "总分" average = "平均分" people[total] = row_sum people[average] = row_mean columns_name += [total, average] # 对列求平均值 ...
['total'] =df.select_dtypes(include=['int']).sum(1)df['total'] =df.loc[:,'Q1':'Q4'].apply(lambda x: sum(x), axis='columns')df.loc[:, 'Q10'] = '我是新来的' # 也可以# 增加一列并赋值,不满足条件的为NaNdf.loc[df.num >= 60, '成绩...
(f, axis="columns") File ~/work/pandas/pandas/pandas/core/frame.py:10374, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs) 10360 from pandas.core.apply import frame_apply 10362 op = frame_apply( 10363 self, 10364 func=func, ...
步骤6 该数据集中一共有多少列(columns)? 步骤7 将数据集中的列Team, Yellow Cards和Red Cards单独存为一个名叫discipline的数据框 步骤8 对数据框discipline按照先Red Cards再Yellow Cards进行排序 步骤9 计算每个球队拿到的黄牌数的平均值 步骤10 找到进球数Goals超过6的球队数据 步骤11 选取以字母G开头的球队数...
row=row.to_dict() new_answer="xxxxxx"forfieldinfields[:-1]: data_out[field].append(row[field]) data_out[fields[-1]].append(new_answer)exceptException as error:print"Error line", idx, error df_out= pd.DataFrame(data_out, columns=fields) ...