In [ 20 ]: a = np.random.standard_normal(( 9 , 4 ))
a.round( 6 )
Out[20]: array([[-0.737304, 1.065173, 0.073406, 1.301174],
[-0.788818, -0.985819, 0.403796, -1.753784],
[-0.155881, -1.752672, 1.037444, -0.400793],
[-0.777546, 1.730278, 0.417114, 0.184079],
[-1.76366 , -0.375469, 0.098678, -1.553824],
[-1.134258, 1.401821, 1.227124, 0.979389],
[ 0.458838, -0.143187, 1.565701, -2.085863],
[-0.103058, -0.36617 , -0.478036, -0.03281 ],
[ 1.040318, -0.128799, 0.786187, 0.414084]])
Although you can construct DataFrame objects more directly (as we have seen before),
using an ndarray object is generally a good choice since pandas will retain the basic
structure and will “only” add meta-information (e.g., index values). It also represents a
typical use case for financial applications and scientific research in general. For example:
In [ 21 ]: df = pd.DataFrame(a)
df
Out[21]: 0 1 2 3
0 -0.737304 1.065173 0.073406 1.301174
1 -0.788818 -0.985819 0.403796 -1.753784
2 -0.155881 -1.752672 1.037444 -0.400793
3 -0.777546 1.730278 0.417114 0.184079
4 -1.763660 -0.375469 0.098678 -1.553824
5 -1.134258 1.401821 1.227124 0.979389
6 0.458838 -0.143187 1.565701 -2.085863
7 -0.103058 -0.366170 -0.478036 -0.032810
8 1.040318 -0.128799 0.786187 0.414084
Table 6-1 lists the parameters that the DataFrame function takes. In the table, “array-like”
means a data structure similar to an ndarray object — a list, for example. Index is an
instance of the pandas Index class.
Table 6-1. Parameters of DataFrame function
Parameter Format Description
data
ndarray/dict/DataFrame
Data for DataFrame; dict can contain Series, ndarrays, lists
index
Index/array-like
Index to use; defaults to range(n)
columns
Index/array-like
Column headers to use; defaults to range(n)
dtype
dtype, default None
Data type to use/force; otherwise, it is inferred
copy
bool, default None
Copy data from inputs
As with structured arrays, and as we have already seen, DataFrame objects have column
names that can be defined directly by assigning a list with the right number of elements.
This illustrates that you can define/change the attributes of the DataFrame object as you
go:
In [ 22 ]: df.columns = [[‘No1’, ‘No2’, ‘No3’, ‘No4’]]
df
Out[22]: No1 No2 No3 No4
0 -0.737304 1.065173 0.073406 1.301174
