Theapply()method, combined with alambda function, offers a versatile approach to achieve similar concatenation results. By replacingdf[[Courses, Duration]] with any column slice of your DataFrame, this method can be generalized to concatenate an arbitrary number of string columns. # Using DataFrame...
In Pandas, theapply()function is used to execute a function that can be used to split one column value into multiple columns. For that, we have to pass the lambda function andSeries.str.split()intopandas apply() function, then call the DataFrame column, which we want to split into two ...
f = lambda x: x.max() - x.min() print('行上计算:\n', frame.apply(f)) #默认在行上进行计算 frame.apply(f, axis = 'columns') #传入columns在列上计算 利用apply不仅可以返回标量值,也可以返回Series对象。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 def f(x): return pd.Series(...
In [88]: iris.assign(sepal_ratio=lambda x: (x["SepalWidth"] / x["SepalLength"])).head() Out[88]: SepalLength SepalWidth PetalLength PetalWidth Name sepal_ratio 0 5.1 3.5 1.4 0.2 Iris-setosa 0.686275 1 4.9 3.0 1.4 0.2 Iris-setosa 0.612245 2 4.7 3.2 1.3 0.2 Iris-setosa 0.680851 3...
df.applymap(lambda x: x * 2) 输出结果为: name age height 0 AliceAlice 50 3.24 1 BobBob 60 3.56 2 CharlieCharlie 70 3.44 注意到,因为name这一列的元素是字符串,所以applymap将函数应用到了每个字符上,而不是对整个字符串进行乘法操作。 applymap方法也可以传入自定义函数: def format_age(age): ...
In [88]: iris.assign(sepal_ratio=lambda x: (x["SepalWidth"] / x["SepalLength"])).head() 根据一定条件生成点状图 In [89]: ( ...: iris.query("SepalLength > 5") ...: .assign( ...: SepalRatio=lambda x: x.SepalWidth / x.SepalLength, ......
two three d4.0NaN b2.0NaN a1.0NaN 行和列标签可以分别通过访问index和columns属性来访问: 注意 当传递一组特定列以及数据字典时,传递的列将覆盖字典中的键。 In [43]: df.index Out[43]: Index(['a','b','c','d'], dtype='object')
by_year.apply(lambda g: g['AAPL'].corr(g['MSFT']))#计算按年分组的相关系数 三、数据透视表(pivot_table) obj.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All') values:需要聚合的数据列 index:需要聚合的索...
df = pd.DataFrame([['A',1],['A',3],['A',2],['B',5],['B',9]], columns = ['name','score']) 介绍两种高效地组内排序的方法。 df.sort_values(['name','score'], ascending = [True,False]) df.groupby('name').apply(lambda x: x.sort_values('score', ascending=False)).re...
df2 = pd.DataFrame(np.random.randn(3,3),columns = ['a','b','c'], index = ['app','win','mac']) print(df2) df2.apply(np.mean) applymap函数的用法 print(df2) df2.applymap(lambda x:'%.3f'%x) 二、数据汇总与统计 1. 数据汇总 在DataFrame中,可以通过sum方法对每列进行求和汇总...