import pandas as pd # 创建一个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 result = df.to_dict()
into: class,可以传递一个实际的类或实例。例如,在 defaultdict 的情况下,可以传递类的实例。该参数的默认值为dict。 返回类型:Dataframe 转换成 Dictionary 要下载以下示例中使用的数据集,请单击这里。在以下示例中,使用的dataframe包含一些 NBA 球员的数据。下面附上未进行任何操作之前的数据帧图像。 示例#1:默认转...
DataFrame.to_dict(orient='dict',into=<class'dict' >) 参数 返回 它返回代表传递的 Dataframe 的字典。 示例代码:DataFrame.to_dict()方法将 DataFrame 转换为字典的字典 为了将 DataFrame 转换为字典的字典,我们将不传递任何参数。 importpandasaspddataframe=pd.DataFrame({'Attendance': {0:60,1:100,2:80,...
这里将pandas 的dataframe 转化为 dict 使用的是 to_dict() 方法 这里放一部分源码: defto_dict(self, orient="dict", into=dict):Convert the DataFrame to a dictionary.Thetypeofthe key-value pairs can be customized with theparameters(see below).Parameters---orient :str{'dict','list','series',...
This tutorial will show you how to convert a Pandas DataFrame into a dictionary with the index column elements as keys and the corresponding elements of other columns as values. We will use the following DataFrame in the article. importpandasaspddf=pd.DataFrame([["Jay",16,"BBA"], ["Jack"...
DataFrame.to_dict(self ,orient='dict',into= )--- 官方文档 函数种只需要填写一个参数：orient即可...
DataFrame.to_dict(self ,orient='dict',into= )--- 官方文档 函数种只需要填写一个参数：orient即可...
Pandas DataFrame to a dict and dropnaTo convert dataframe into a dictionary, we will use pandas.DataFrame.to_dict() method but since we need to drop the nan values simultaneously, we will use this method in a comprehension statement.
Pandas provide a method called pandas.DataFrame.to_dict() method which will allow us to convert a DataFrame into a dictionary but the generated output will have the index values in the form of a key. Sometimes we do not want the index values as the key, in that case, we follow another...
>>> df = pd.DataFrame(data) >>> df.set_index(keys='name', drop=False, inplace=True) >>> df age name name bob 20 bob jim 25 jim bob 30 bob >>> df.to_dict(orient='index') {'bob': {'age': 30, 'name': 'bob'}, 'jim': {'age': 25, 'name': 'jim'}} ...