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Create a DataFrame from a list of dictionaries
Import pandas, put your row records in a list, and pass that list to the DataFrame constructor:
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import pandas as pd
records = [
{"name": "Ada", "age": 36},
{"name": "Linus", "age": 55},
]
df = pd.DataFrame(records)
print(df)
The result has one row per dictionary and columns named name and age. Values under each key fill the corresponding column. This is the ordinary, clearest pattern for row-oriented records. See the pandas.DataFrame constructor reference and the pandas table-oriented tutorial.
Choose and order the columns explicitly
For list-of-dictionaries input, pandas uses dictionary key insertion order for column order. If your output needs a fixed schema or a particular order, pass columns=:
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df = pd.DataFrame(records, columns=["age", "name"])
The argument selects and orders the output columns. It does not check that every record contains every required field; validate such business rules separately.
Handle records with missing keys
Records can have different keys. pandas includes the supplied fields as columns and leaves a missing value in a row where its dictionary has no value for a column. For example:
records = [
{"name": "Ada", "age": 36},
{"name": "Linus"},
]
df = pd.DataFrame(records)
Here, the second row has no age value, so that cell is represented as missing data. If you specify a column that is absent from all records, it is also created with missing values. The from_records reference documents this behavior for explicitly requested columns.
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Understand inferred data types and the index
By default, pandas infers data types from the supplied values. The constructor’s dtype= parameter requests a single dtype for construction; it is not a mapping for assigning separate types to different columns. For per-column type requirements, construct the DataFrame first and cast the relevant columns explicitly.
Unless you provide an index, pandas assigns a default integer RangeIndex to the rows. The constructor reference describes both dtype inference and the default index.
When to use DataFrame.from_records()
pd.DataFrame(records) is usually the most readable choice for a simple list of dictionaries. pd.DataFrame.from_records(records) is a supported alternative when its record-oriented options clarify what you need, including index, exclude, or columns.
df = pd.DataFrame.from_records(
records,
columns=["name", "age"],
)
Both forms accept iterable dictionaries. Use the constructor form by default; choose from_records when its additional options are useful to the conversion.
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Quick checklist
- Use
pd.DataFrame(records)for one row per dictionary. - Use
columns=when output fields or their order must be explicit. - Validate required keys yourself; selecting columns does not validate input records.
- Expect pandas to infer data types and assign a
RangeIndexunless you specify otherwise.
For the underlying data structure and missing-data context, see pandas’ intro to data structures.
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