*'broadcast': results will be broadcast to the original shape of the DataFrame, the original indexandcolumns will be retained.Thedefaultbehaviour(None) dependsonthereturnvalueof the applied function: list-like results will be returnedasa Series of those. Howeverifthe apply function returns a Series...
通过类型声明,我们能够清晰地指定函数接收的 DataFrame 中所需的列及数据类型。 以下代码展示了一个函数,它接受一个 DataFrame 作为参数并计算指定列的平均值: importpandasaspdfromtypingimportOptionaldefcalculate_average(df:pd.DataFrame,column_name:str)->Optional[float]:ifcolumn_nameindf.columns:returndf[column...
DataFrame.itertuples([index, name]) #Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple. DataFrame.lookup(row_labels, col_labels) #Label-based “fancy indexing” function for DataFrame. DataFrame.pop(item) #返回删除的项目 DataFrame.tail([n]) #返回最后...
DataFrame.iterrows() 返回索引和序列的迭代器 DataFrame.itertuples([index, name]) Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple. DataFrame.lookup(row_labels, col_labels) Label-based “fancy indexing” function for DataFrame. ...
from pandasimportDataFrame 要知道,pandas中有一个DataFrame模块,它类似与excel的表格形式。 在这里,我们需要知道将文件保存为excel格式使用的命令是: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 df.to_excel(文件名) 其中df就是DataFrame类型。
fromnumbaimportjit@jitdefoptimized_function(x):# 优化后的代码returnresult df['OptimizedResult'] = df['Value'].apply(optimized_function) 在实际应用中,需要根据具体情况综合运用这些技巧来最大程度地优化 DataFrame 的性能。通过不断的实践和调整,我们能够使数据处理过程更加高效,为我们的数据分析和应用提供有...
X_test = pd.DataFrame(scaler.transform(dataset_test), columns=dataset_test.columns, index=dataset_test.index) tf.random.set_seed(10) act_func ='relu' # Input layer: model=Sequential() # First hidden layer, connected to input vecto...
DataFrame.apply(func[, axis, broadcast, …])应用函数 DataFrame.applymap(func)Apply a function to a DataFrame that is intended to operate elementwise, i.e. DataFrame.aggregate(func[, axis])Aggregate using callable, string, dict, or list of string/callables ...
import numpy as npdef sigmoid(z):"""Sigmoid函数"""return 1 / (1 + np.exp(-z))def cost_function(X, y, weights):"""逻辑回归的代价函数(负对数似然函数)"""m = len(y)h = sigmoid(X @ weights)cost = (-y.T @ np.log(h) - (1-y).T @ np.log(1-h)) / mreturn costdef ...
b. 应用:E列中包含a、c的所有的行再组成dataframe df[df.E.isin(['a','c'])] c. 特定值的行 df[(df==0).any(axis=1)] 11. map 的用法: 替代for循环,辅助加速 map(function, list) 简写 map(lambda x: x ** 2, [1, 2, 3, 4, 5]) # 使用 lambda 匿名函数 [1, 4, 9, 16, ...