Hướng dẫn what is as_matrix python?

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.as_matrix() function is used to convert the given series or dataframe object to Numpy-array representation.

Syntax: Series.as_matrix(columns=None)

Parameter :
columns : If None, return all columns, otherwise, returns specified columns.

Returns : values : ndarray

Example #1: Use Series.as_matrix() function to return the numpy-array representation of the given series object.

import pandas as pd

sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio'])

index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5'] 

sr.index = index_

print(sr)

Output :

City 1    New York
City 2     Chicago
City 3     Toronto
City 4      Lisbon
City 5         Rio
dtype: object

Now we will use Series.as_matrix() function to return the numpy array representation of the given series object.

result = sr.as_matrix()

print(result)

Output :

['New York' 'Chicago' 'Toronto' 'Lisbon' 'Rio']

As we can see in the output, the Series.as_matrix() function has successfully returned the numpy array representation of the given series object.
 
Example #2 : Use Series.as_matrix() function to return the numpy-array representation of the given series object.

import pandas as pd

sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None])

index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y')

sr.index = index_

print(sr)

Output :

2010-12-31 08:45:00    11.0
2011-12-31 08:45:00    21.0
2012-12-31 08:45:00     8.0
2013-12-31 08:45:00    18.0
2014-12-31 08:45:00    65.0
2015-12-31 08:45:00    18.0
2016-12-31 08:45:00    32.0
2017-12-31 08:45:00    10.0
2018-12-31 08:45:00     5.0
2019-12-31 08:45:00    32.0
2020-12-31 08:45:00     NaN
Freq: A-DEC, dtype: float64

Now we will use Series.as_matrix() function to return the numpy array representation of the given series object.

result = sr.as_matrix()

print(result)

Output :

[ 11.  21.   8.  18.  65.  18.  32.  10.   5.  32.  nan]

As we can see in the output, the Series.as_matrix() function has successfully returned the numpy array representation of the given series object.

How to convert a list to an array in Python

During programming, there will be instances when you will need to convert existing lists to arrays in order to perform certain operations on them (arrays enable mathematical operations to be performed on them in ways that lists do not).

Lists can be converted to arrays using the built-in functions in the Python numpy library.

numpy provides us with two functions to use when converting a list into an array:

• numpy.array()

• numpy.asarray()

1. Using numpy.array()

This function of the numpy library takes a list as an argument and returns an array that contains all the elements of the list. See the example below:

import numpy as np my_list = [2,4,6,8,10] my_array = np.array(my_list) # printing my_array print my_array # printing the type of my_array print type(my_array)

2. Using numpy.asarray()

This function calls the numpy.array() function inside itself. See the definition below:

def asarray(a, dtype=None, order=None): return array(a, dtype, copy=False, order=order)

The main difference between np.array() and np.asarray() is that the copy flag is false in the case of np.asarray(), and true (by default) in the case of np.array().

This means that np.array() will make a copy of the object (by default) and convert that to an array, while np.asarray() will not.

The code below ​illustrates the usage of np.asarray():

import numpy as np my_list = [2,4,6,8,10] my_array = np.asarray(my_list) # printing my_array print my_array # printing the type of my_array print type(my_array)