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DataFrame.to_dict() converts DataFrame to dictionary. Example >>> df = pd.DataFrame( {'col1': [1, 2], 'col2': [0.5, 0.75]}, index=['a', 'b']) >>> df col1 col2 a 1 0.1 b 2 0.2 >>> df.to_dict() {'col1': {'a': 1, 'b': 2}, 'col2': {'a': 0.5, 'b': 0.75}}
Steps to Convert Pandas DataFrame to a Dictionary. Step 1: Create a DataFrame. For example, create a DataFrame with two columns: import pandas as pd. data = { "Product": ["Laptop", "Printer", "Monitor", "Tablet"], "Price": [1200, 100, 300, 150], } df = pd.DataFrame(data) print (df) print (type(df)) You’ll then get the following DataFrame:
DataFrame. to_dict (orient='dict', *, into=<class 'dict'>, index=True) [source] # Convert the DataFrame to a dictionary. The type of the key-value pairs can be customized with the parameters (see below).
9 lut 2024 · Pandas .to_dict () method is used to convert a DataFrame into a dictionary of series or list-like data type depending on the orient parameter. Example: Python3. importpandas as pd. data ={'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 28], 'City': ['New York', 'London', 'Paris']} df =pd.DataFrame (data) series_dict =df.to_dict ('series')
30 wrz 2022 · Convert a Pandas DataFrame to a Dictionary. By default, the Pandas DataFrame .to_dict () method will return a dictionary where the keys are the columns and the values are the index:record matches. This process is more informative when your indices are meaningful, rather than arbitrary numbers.
18 lut 2024 · Method 1: Using to_dict () with ‘dict’ orientation. In this method, the to_dict () method of a pandas DataFrame is used with the default ‘dict’ orientation. This converts the DataFrame into a dictionary where each column becomes a key and the corresponding values are lists of column data.
9 mar 2023 · Example to convert pandas DataFrame to dict. In the below example, we read the input from the StudentData.csv file 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 “Marks“.
