Create a Pandas DataFrame from List of Dicts

To convert your list of dicts to a pandas dataframe use the following methods:

  1. pd.DataFrame(data)
  2. pd.DataFrame.from_dict(data)
  3. pd.DataFrame.from_records(data)

Depending on the structure and format of your data, there are situations where either all three methods work, or some work better than others, or some don't work at all.

pd.DataFrame(data)

This method creates a DataFrame from a list of dictionaries, where each dictionary represents a row in the DataFrame. The keys of the dictionaries become column names, and the values become the row values.

import pandas as pd # Sample list of dictionaries data = [ {'Name': 'John', 'Age': 25, 'City': 'New York'}, {'Name': 'Alice', 'Age': 30, 'City': 'Los Angeles'}, {'Name': 'Bob', 'Age': 28, 'City': 'Chicago'} ] # Convert list of dictionaries to DataFrame df = pd.DataFrame(data) print(df)

pd.DataFrame.from_dict(data)

This method creates a DataFrame from a dictionary where keys represent column names, and values represent column data. The dictionary should be in the format {column_name: column_data}.

import pandas as pd # Sample dictionary with column data data = { 'Name': ['John', 'Alice', 'Bob'], 'Age': [25, 30, 28], 'City': ['New York', 'Los Angeles', 'Chicago'] } # Convert dictionary to DataFrame df = pd.DataFrame.from_dict(data) print(df)

pd.DataFrame.from_records(data)

This method creates a DataFrame from a list of tuples or namedtuples. The tuples represent rows in the DataFrame, and the names of the fields in the tuples correspond to the column names.

import pandas as pd # Sample list of tuples data = [ ('John', 25, 'New York'), ('Alice', 30, 'Los Angeles'), ('Bob', 28, 'Chicago') ] # Convert list of tuples to DataFrame df = pd.DataFrame.from_records(data, columns=['Name', 'Age', 'City']) print(df)

Conclusion

All three methods can be useful depending on the format of your data. If your data is already in the form of a list of dictionaries, using pd.DataFrame(data) is straightforward. If your data is in the form of a dictionary with column data, pd.DataFrame.from_dict(data) is suitable. If your data is in the form of a list of tuples, pd.DataFrame.from_records(data) can be used with the appropriate column names specified in the columns parameter.






Related Topics
  1. Creating an empty Pandas DataFrame
  2. How to Check if a Pandas DataFrame is Empty
  3. How to check if a column exists in Pandas Dataframe
  4. How to delete column from pandas DataFrame
  1. How to select multiple columns from Pandas DataFrame
  2. Selecting multiple columns in a Pandas dataframe based on condition
  3. Selecting rows in pandas DataFrame based on conditions
  4. How to Drop rows in DataFrame by conditions on column values
  5. Rename column in Pandas DataFrame
  6. Get a List of all Column Names in Pandas DataFrame
  7. How to add new columns to Pandas dataframe?
  8. Change the order of columns in Pandas dataframe
  9. Concatenate two columns into a single column in pandas dataframe
  10. How to count the number of rows and columns in a Pandas DataFrame
  11. Use a list of values to select rows from a pandas dataframe
  12. How to iterate over rows in a DataFrame in Pandas
  13. How to drop rows/columns of Pandas DataFrame whose value is NaN
  14. How to Export Pandas DataFrame to a CSV File
  15. How to set a particular cell value in pandas DataFrame