# 打印透视表print(pivot_table)# 使用 margins 进行总计pivot_table_with_margins = pd.pivot_table( df, values='薪资', # 要汇总的列名 index='部门', # 行索引的列名 columns='职位', # 列索引的列名 aggfunc='sum', # 聚合函数 margins=True, # 添加总计行和列 marg...
接下来,我们使用 Pandas 的 pivot_table() 函数创建一个透视表,按照班级和科目对分数进行分组,并计算平均分数:pivot_table=df.pivot_table(index="Class",columns="Subject",values="Score",aggfunc="mean") print(pivot_table) 在这个例子中,我们使用了 Pandas 提供的 pivot_table() 函数,它可以帮助我们根据...
pd.pivot_table(df,index=["Manager","Rep"],values=["Price"], columns=["Product"],aggfunc=[np.sum],fill_value=0) 其实,我觉得添加“Quantity”列将对我们有所帮助,所以将“Quantity”添加到“values”列表中。 pd.pivot_table(df,index=["Manager","Rep"],values=["Price","Quantity"], columns=...
Using the Pandaspivot_table()function we can reshape the DataFrame on multiple columns in the form of an Excel pivot table. To group the data in a pivot table we will need to pass aDataFrameinto this function and the multiple columns you wanted to group as an index. Here, I will take ...
table = pivot_table(df, values='D', index=['A', 'B'], ... columns=['C'], aggfunc=np.sum, fill_value=0) table C large small A B bar one 4 5 two 7 6 foo one 4 1 two 0 6 The next example aggregates by taking the mean across multiple columns. ...
经常做报表的小伙伴对数据透视表应该不陌生，在excel中利用透视表可以快速地进行分类汇总，自由组合字段...
pyc in wrapper(*args, **kwargs) 86 else: 87 kwargs[new_arg_name] = new_arg_value ---> 88 return func(*args, **kwargs) 89 return wrapper 90 return _deprecate_kwarg /pandas/tools/pivot.pyc in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna) 145 ...
DataFrame.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True) 看之前建议先看pd.pivot的介绍,pivot_table就是在pivot的基础上加上了透视后聚合统计,排序等功能 ...
pivot_table = pd.pivot_table(df, values=['Salary', 'Name'], index='Age', columns='Salary', aggfunc=np.size) print(pivot_table) 这将输出以下内容: Salary Name 50000 60000 70000 80000 Alice Bob Charlie David Age 25 1.0 1.0 1.0 1.0 1 1 1 1 30 1.3 1.3 1.3 1.3 1 1 1 1 35 1.7...
Python program for pivot table with aggfunc=count unique distinct # Importing pandas packageimportpandasaspd# Creating a Dictionaryd={'A': ['Amit','Amit','Ashish','Ashish'],'B': ['Bablu','Bablu','Bobby','Bhanu'],'C': ['Chetan','Chirag','Chiranjeev','Chetna'] ...