@@ -241,17 +241,47 @@ class DataFrame(NDFrame):
241241 Column labels to use for resulting frame. Will default to
242242 np.arange(n) if no column labels are provided
243243 dtype : dtype, default None
244- Data type to force, otherwise infer
244+ Data type to force. Only a single dtype is allowed. If None, infer
245245 copy : boolean, default False
246246 Copy data from inputs. Only affects DataFrame / 2d ndarray input
247247
248248 Examples
249249 --------
250- >>> d = {'col1': ts1, 'col2': ts2}
251- >>> df = DataFrame(data=d, index=index)
252- >>> df2 = DataFrame(np.random.randn(10, 5))
253- >>> df3 = DataFrame(np.random.randn(10, 5),
254- ... columns=['a', 'b', 'c', 'd', 'e'])
250+ Constructing DataFrame from a dictionary.
251+
252+ >>> d = {'col1': [1, 2], 'col2': [3, 4]}
253+ >>> df = pd.DataFrame(data=d)
254+ >>> df
255+ col1 col2
256+ 0 1 3
257+ 1 2 4
258+
259+ Notice that the inferred dtype is int64.
260+
261+ >>> df.dtypes
262+ col1 int64
263+ col2 int64
264+ dtype: object
265+
266+ To enforce a single dtype:
267+
268+ >>> df = pd.DataFrame(data=d, dtype=np.int8)
269+ >>> df.dtypes
270+ col1 int8
271+ col2 int8
272+ dtype: object
273+
274+ Constructing DataFrame from numpy ndarray:
275+
276+ >>> df2 = pd.DataFrame(np.random.randint(low=0, high=10, size=(5, 5)),
277+ ... columns=['a', 'b', 'c', 'd', 'e'])
278+ >>> df2
279+ a b c d e
280+ 0 2 8 8 3 4
281+ 1 4 2 9 0 9
282+ 2 1 0 7 8 0
283+ 3 5 1 7 1 3
284+ 4 6 0 2 4 2
255285
256286 See also
257287 --------
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