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.
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.
If data is a list of dicts, column order follows insertion-order.
Index to use for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided.
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.
Data type to force. Only a single dtype is allowed. If None, infer.
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
.
Two-dimensional, size-mutable, potentially heterogeneous tabular data.
DataFrame.from_dict
From dicts of Series, arrays, or dicts.
DataFrame.from_records
Constructor from tuples, also record arrays.
read_clipboard
Read text from clipboard into DataFrame.
read_csv
Read a comma-separated values (csv) file into DataFrame.
read_table
Read general delimited file into DataFrame.
Constructing DataFrame from a dictionary.
This example is valid syntax, but we were not able to check execution>>> 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.
This example is valid syntax, but we were not able to check execution>>> df.dtypes col1 int64 col2 int64 dtype: object
To enforce a single dtype:
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame(data=d, dtype=np.int8)
... df.dtypes col1 int8 col2 int8 dtype: object
Constructing DataFrame from a dictionary including Series:
This example is valid syntax, but we were not able to check execution>>> 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:
This example is valid syntax, but we were not able to check execution>>> 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:
This example is valid syntax, but we were not able to check execution>>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],This example is valid syntax, but we were not able to check execution
... 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:
This example is valid syntax, but we were not able to check execution>>> from dataclasses import make_dataclassSee :
... 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
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.measure._regionprops.regionprops_table
pandas.core.frame.DataFrame.join
pandas.plotting
pandas.core.indexes.multi.MultiIndex.to_frame
pandas.core.frame.DataFrame.count
pandas
pandas.plotting._misc.radviz
networkx.convert_matrix.from_pandas_edgelist
pandas.io.formats.format.get_dataframe_repr_params
matplotlib.pyplot.plot
skimage.measure._regionprops._props_to_dict
matplotlib.axes._axes.Axes.plot
pandas.io.excel._base.read_excel
networkx.convert_matrix.to_pandas_edgelist
networkx.convert_matrix.from_pandas_adjacency
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