20,30],'C':['pandasdataframe.com','modify','columns']})# 定义一个函数,如果数值大于10,加10defadd_ten(x):returnx+10ifx>10elsex# 对'A'和'B'列应用条件函数df[['A','B']]=df[['A','B']].applymap(add_ten)print(df)
示例代码 2: 使用 apply 返回多列 importpandasaspd# 创建一个 DataFramedf=pd.DataFrame({'A':range(1,6),'B':['pandasdataframe.com'for_inrange(5)]})# 定义一个函数,返回多个新的列值defmultiple_columns(row):returnpd.Series([row['A']*2,row['A']*3],index=['double','triple'])# 应用...
for col in dp_data.columns: dp_data[col] = dp_data.parallel_apply(lambda x: apply_md5(x[col]), axis=1) 查看运行结果: 5. pySpark测试 Spark资料很多了,可以参考: 安装:pip3 install pyspark -i https://pypi.mirrors.ustc.edu.cn/simple/ 读取数据集,记录耗时: from pyspark.sql import Spark...
To apply a function to multiple columns of a Pandas DataFrame, you can simply use the DataFrame.apply() method by specifying the column names. The method itself takes a function as a parameter that has to be applied on the columns.
In Pandas, the apply() function can indeed be used to return multiple columns by returning a pandas Series or DataFrame from the applied function. In this
def gimmeMultiple(group): x1 = 1 x2 = 2 return array([[1, 2]]) def gimmeMultipleDf(group): x1 = 1 x2 = 2 return pd.DataFrame(array([[1,2]]), columns=['x1', 'x2']) df['size'].astype(int).apply(gimmeMultiple) df['size'].astype(int).apply(gimmeMultipleDf) 返回一个...
() 执行步骤: 将数据按照size进行分组 在分组内进行聚合操作 grouping multiple columns dogs.groupby...(['type', 'size']) groupby + multi aggregation (dogs .sort_values('size') .groupby('size')['height...values='price') melting dogs.melt() pivoting dogs.pivot(index='size', columns='kids...
Pandas: Sum up multiple columns into one column without last column Advertisement Advertisement Related Tutorials How to delete all columns in DataFrame except certain ones? How to Merge a Series and DataFrame? Pandas: Convert index to datetime ...
# Apply function NumPy.square() to square the values of two rows 'A'and'B df2 = df.apply(lambda x: np.square(x) if x.name in ['A','B'] else x) print("After applying a lambda function for multiple columns:\n", df)
调试提示: 如果你在编写样式函数时遇到困难,尝试将其直接传递给DataFrame.apply。在内部,Styler.apply使用DataFrame.apply,因此结果应该是相同的,而使用DataFrame.apply,您将能够检查每个单元格中预期函数的 CSS 字符串输出。 对索引和列标题进行操作 通过使用以下方式实现标题的类似应用: .map_index()(逐元素):接受一...