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.
Whenever we want to perform some operation on the entire DataFrame, we either use apply method. It is used on the grouped objects in pandas DataFrame. The apply() method Theapply()method passes the columns of each group in the form of a DataFrame inside the function which is descri...
例如,可以通过在apply()中指定一个权重列来计算加权平均值。 代码语言:javascript 代码运行次数:0 运行 复制 In [8]: def weighted_mean(x): ...: arr = np.ones((1, x.shape[1])) ...: arr[:, :2] = (x[:, :2] * x[:, 2]).sum(axis=0) / x[:, 2].sum() ...: return arr...
6. Use apply() Function Split Column into Two Columns In Pandas 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, ...
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
df.columns() # 查看字段()名称 df.describe() # 查看汇总统计 s.value_counts() # 统计某个值出现次数 df.apply(pd.Series.value_counts) # 查看DataFrame对象中每列的唯值和计数 df.isnull().any() # 查看是否有缺失值 df[df[column_name].duplicated()] # 查看column_name字段数据重复的数据信息 ...
拆分操作是在对象的特定轴上执行的。例如,DataFrame可以在其行(axis=0)或列(axis=1)上进行分组。然后,将一个函数应用(apply)到各个分组并产生一个新值。最后,所有这些函数的执行结果会被合并(combine)到最终结果对象中。结果对象的形式一般取决于数据上所执行的操作。如下图大致说明了一个简单的分组聚合过程。
6 rows x 16 columns] Another aggregation example is to compute the number of unique values of each group. This is similar to thevalue_countsfunction, except that it only counts unique values. In [77]: ll = [['foo', 1], ['foo', 2], ['foo', 2], ['bar', 1], ['bar', 1]...
df = df.apply(pd.to_numeric, errors='coerce').fillna(0) df 1. 2. 仅需一行代码就完成了我们的目标,因为现在所有的数据类型都转换成float: AI检测代码解析 df.dtypes 1. AI检测代码解析 col_one float64 col_two float64 col_three float64 ...
df['修改的列'] = df['条件列'].apply(调用函数名) import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') def modify_value(x): if x < 5: return '是' elif x < 10: return '否' else: return 'x' # 插入列 for col_num in range(4, 9): df....