'Salary': [5000, 6000, 7000, 5500, 6500]} df = pd.DataFrame(data) # 分组并计算每个分组的平均薪资 grouped = df.groupby('Name') result = grouped['Salary'].mean() # 转换为字典形式 dict_result = result.to_dict() # 创建字典列表 dict_list = [] for key, value in dict_result.i...
[5]: df.to_dict(orient='dict')18Out[5]:19{'colA': {0:'A', 1:'A', 2:'B', 3:'C', 4:'A'},20'colB': {0:'X', 1: nan, 2:'Ya', 3:'Xb', 4:'Xa'},21'colC': {0: 100, 1: 50, 2: 30, 3: 50, 4: 20},22'colD': {0: 90, 1: 60, 2: 60, 3: 80,...
df= df.dropna(axis=1) 去除某一列: df= df.drop(['one'],axis=1) 去除含有某一个数的行: row_list = df[df.one == 2].index.tolist()#获得含有该值的行的行号df = df.drop(row_list) 六. DataFrame的修改 修改数据类型 df['one']=pd.DataFrame(df['one'],dtype=np.float) 修改列名(需...
dict1 = dict(zip(data['key'],data['value'])) sales = [{"Fruits":"apple","Numbers":5}, {"Fruits":"banana","Numbers":8}, {"Fruits":"pear","Numbers":9}] df = pd.DataFrame(sales) df.to_dict(orient='records') 结果是上面的格式 sales = {"Fruits":["apple","banana","pear"...
比如,三天后减去当前,通常使用shift;idx_df['closeIndex'].shift(-window) / idx_df['closeIndex'] - 1.0 python 的list 的表达式写法 return [ x for x in cols if x not in filterOut] ,for 循环里面直接写条件 对dict 按照值排序 s_res = sorted(res.items(), key=lambda x: x[1], reverse...
import pandas as pd # 创建一个示例DataFrame data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'London', 'Paris']} df = pd.DataFrame(data) # 将DataFrame转换为字典 dict_data = df.to_dict(orient='dict') print(dict_data) # 输出结果...
})# 将DataFrame转换为字典,按列名作为键result = df.to_dict(orient='dict')print(result)# 输出:{'name': {0: 'Alice', 1: 'Bob', 2: 'Charlie'}, 'age': {0: 23, 1: 45, 2: 57}, 'gender': {0: 'F', 1: 'M', 2: 'M'}} ...
]], columns = ['Name','Age','Course']) d1 = df.to_dict() print(d1)输出：{'Nam...
二、DataFrame转换为Dict DataFrame转换为Dict主要用到to_dict(orient)函数。参数orient的值可以取dict、list、series、split、records、index等。这里我们主要讲解list、records两个参数值。假设DataFrame数据结构df如下: 1、参数为list df.to_dict(orient='list') ...
df_dict = df.to_dict('records') temp=[] forrowindf_dict: name_new = row['name'].strip temp.append(name_new) 对数据集的字典格式进行处理后耗时「25.5秒」,这比iterrows函数快77倍。 使用apply apply 是内置的Pandas函数,它允许传递一个函数并将其应用于Pandas系列的每个值。apply函数本身并不快,但...