Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsPass the list of dictionaries directly to pd.DataFrame(). Each dictionary becomes a row, and its keys become column names:
import pandas as pd
records = [
{"name": "Ada", "age": 36},
{"name": "Linus", "age": 55},
]
df = pd.DataFrame(records)
print(df)This creates a two-dimensional table with a default integer index. The examples below show how to control column order, handle missing fields, and choose the related from_records() method.
Use pd.DataFrame() for a list of dictionaries
For ordinary row-oriented data, pass the list directly to the DataFrame constructor. Each dictionary represents one row; each dictionary key identifies a column. See the pandas.DataFrame API reference and the pandas getting-started tutorial for the constructor and table-oriented model.
import pandas as pd
records = [
{"name": "Ada", "age": 36},
{"name": "Linus", "age": 55},
]
df = pd.DataFrame(records)
print(df)The result has columns name and age, with one row per dictionary. Unless you provide an index, pandas assigns integer row labels using a RangeIndex.
#1 Best Overall
Choose and order the columns explicitly
For list-of-dictionaries input, the constructor uses the keys’ insertion order for column order. If the output needs a stable, deliberately chosen schema, pass columns=:
df = pd.DataFrame(records, columns=["name", "age"])The argument selects and orders the requested columns. It controls the output columns; it does not check whether every input dictionary contains every required field.
Handle records with missing keys
When dictionaries contain different keys, pandas includes the fields represented in the data and leaves a missing value where a row has no value for a column. This is normal pandas missing-data behavior, described in the pandas introduction to data structures.
If your application requires every record to contain specific fields, define the expected columns and validate the dictionaries separately. Choosing columns shapes the resulting table, but it is not business-rule validation.
Understand inferred types and the index
By default, pandas infers data types from the values it receives. The constructor’s dtype= argument can request a dtype for construction, but it is a single dtype argument—not a way to specify a different dtype for each column. For per-column requirements, construct the DataFrame and then cast the relevant columns explicitly.
With no index supplied, the rows receive a default integer RangeIndex. The constructor reference documents both the inferred dtypes and default index behavior: DataFrame API reference.
When to use DataFrame.from_records()
pd.DataFrame(records) is the clearest default for an ordinary list of dictionaries. pd.DataFrame.from_records(records) is a supported alternative when record-specific options make your intent clearer:
df = pd.DataFrame.from_records(
records,
columns=["name", "age"],
)from_records() supports options including index, exclude, and columns. If a requested column is absent from the records, it appears as a column of missing values. Consult the DataFrame.from_records API reference for the available parameters.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
