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() print(result) 输出结果如下: 代码...
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',...
转换后的字典形式如下：{column:{index:value}}。字典的键是DataFrame 列名，字典的值是一个{DataFrame...
Suppose, we are given a DataFrame with multiple columns. These columns contain integer values as well as some nan values. We need to convert this DataFrame into a dictionary and at the same time, we need to drop all the nan values as well. As a result, we need a dictionary with no ...
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...
例如,“list”将返回一个包含 Key=Column name 和 Value=List (Converted series) 的列表字典。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
Python Pandas DataFrame.to_dict() 函数将给定的 DataFrame 转换为字典。 pandas.DataFrame.to_dict()的语法 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 ...
>>> 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'}} ...
Exampleto convert pandas DataFrame to dict In the below example, we read the input from theStudentData.csvfile and create a DataFrame object. It is then converted into the Python dictionary object. Input CSV file contains a simple dataset of student data with two columns, “Name” and “Mark...
您可以将pandas.DataFrame.to_dict与下面的列表comprehension.See一起使用: import pandas as pd d=df.to_dict('list') res=[{'heading':i, 'values':k} for i, k in d.items()] Example: df=pd.DataFrame({'a':[10,20,30,40], 'b':[100,200,300,400]}) >>>print(df) a b 0 10 10...