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pandas.DataFrame() ⚬｜Documentation｜1st｜202510210006-00-⌔

pandas.DataFrame — pandas 2.3.3 documentation#pandas.DataFrame

class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None)

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.

Parameters:

data: ndarray (structured or homogeneous), Iterable, dict, or DataFrame

Dict can contain Series, arrays, constants, dataclass or list-like objects. If data is a dict, column order follows insertion-order. If a dict contains Series which have an index defined, it is aligned by its index. This alignment also occurs if data is a Series or a DataFrame itself. Alignment is done on Series/DataFrame inputs.

If data is a list of dicts, column order follows insertion-order.

index: Index or array-like

Index to use for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided.

columns: Index or array-like

Column labels to use for resulting frame when data does not have them, defaulting to RangeIndex(0, 1, 2, …, n). If data contains column labels, will perform column selection instead.

dtype: dtype, default None

Data type to force. Only a single dtype is allowed. If None, infer. If data is DataFrame then is ignored.

copy: bool or None, default None

Copy data from inputs. For dict data, the default of None behaves like copy=True. For DataFrame or 2d ndarray input, the default of None behaves like copy=False. If data is a dict containing one or more Series (possibly of different dtypes), copy=False will ensure that these inputs are not copied.

See also:

DataFrame.from_records

Constructor from tuples, also record arrays.

DataFrame.from_dict

From dicts of Series, arrays, or dicts.

read_csv

Read a comma-separated values (csv) file into DataFrame.

read_table

Read general delimited file into DataFrame.

read_clipboard

Read text from clipboard into DataFrame.

Notes:

Please reference the User Guide for more information.

Examples:

Constructing DataFrame from a dictionary.

>>> d = {"col1": [1, 2], "col2": [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
  col1  col2
0     1     3
1     2     4

Notice that the inferred dtype is int64.

>>> df.dtypes
col1    int64
col2    int64
dtype: object

To enforce a single dtype:

>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1    int8
col2    int8
dtype: object

Constructing DataFrame from a dictionary including Series:

>>> d = {"col1": [0, 1, 2, 3], "col2": pd.Series([2, 3], index=[2, 3])}
>>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
  col1  col2
0     0   NaN
1     1   NaN
2     2   2.0
3     3   3.0

Constructing DataFrame from numpy ndarray:

>>> df2 = pd.DataFrame(
...     np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=["a", "b", "c"]
... )
>>> df2
  a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

Constructing DataFrame from a numpy ndarray that has labeled columns:

>>> data = np.array(
...     [(1, 2, 3), (4, 5, 6), (7, 8, 9)],
...     dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")],
... )
>>> df3 = pd.DataFrame(data, columns=["c", "a"])
>>> df3
  c  a
0  3  1
1  6  4
2  9  7

Constructing DataFrame from dataclass:

>>> from dataclasses import make_dataclass
>>> Point = make_dataclass("Point", [("x", int), ("y", int)])
>>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
  x  y
0  0  0
1  0  3
2  2  3

Constructing DataFrame from Series/DataFrame:

>>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> df = pd.DataFrame(data=ser, index=["a", "c"])
>>> df
  0
a  1
c  3

>>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
>>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
>>> df2
  x
a  1
c  3

Attributes:

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Methods:

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Printed 2026-06-28.

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