Converting Data into a NumPy Array

Introduction

In pandas, a type of list called a series can include integer, string, double, and other types of data. In contrast, Pandas Series returns an object in the form of a list, where n is the length of the series data, and the index ranges from 0 to n. We'll talk about pandas data frames and series as well as how to convert them to NumPy arrays. A data frame can be formed from more than one series, or we could say that a data frame is a collection of series that can be used to analyze the data. Series can only include a single list with an index.

Why the NumPy Format?

Python lists are slower than NumPy arrays, which are also smaller. The usage of an array saves memory and is simple. NumPy offers a means for selecting the data types and utilizes significantly less RAM to store data. This enables even more code optimization.

Numpy uses little memory. As soon as there are 500K or more rows, Pandas perform better. Numpy performs better when there are 50K or fewer rows. Comparing the indexing of the pandas series to NumPy arrays, it is considerably sluggish.

Converting Pandas DataFrame to NumPy array

The resulting NumPy Array has elements of a single datatype when your DataFrame contains columns of several data types. The NumPy Array's datatype is based on the minimum DataFrame datatype.

Link to the dataset we are going to use in this article- Dataset-Salary.csv

Using to_records()

An array of NumPy records is created from a DataFrame using the to_records() method. If desired, the index will be put as the record array's first field. Include the index, which is kept in the "index" field or by using the index label, if set, in the final record array. The data type to store all columns, if a string or type.

Syntax:

Parameters: 

• column_dtypes: str, type, dict, default None The data type to store all columns if a string or type. A mapping of column names and indices (zero-indexed), if a dictionary, to particular data types.
• index_dtypes: str, type, dict, default None The data type to store all index levels, if a string or other kind. A mapping of index level names and indices (zero-indexed) to certain data types, if a dictionary.

Returns: numpy.recarray Each row of the DataFrame is represented as an entry in a NumPy ndarray field by the labels from the DataFrame.

Example 1:

Output:

We import the pandas library and initialize the dataframe by reading through a CSV file. We drop the missing values rows we then convert the column data to a dataframe, and then print the values along with the index.

Using to_numpy()

The common NumPy dtype of all types in the DataFrame will, by default be used as the dtype of the returned array. For instance, the resulting dtype will be float32 if the dtypes are float16 and float32. Coercing values and copying data may be necessary, both of which might be costly.

Syntax:

Parameters: 

• dtype: Data type that we are supplying, such as str.
• copy: [bool, default False] Checks to make sure the returned value is not a view on another array.

Returns: numpy.ndarray

Example 1

A simple example with a pandas data frame.

Output:

Example 2

To use the dataFrame to_numpy() function, we are converting the Dataframe, in this case, into a Numpy array using a CSV file from Salary Prediction Dataset. The df.head() function is then used to output the first five values of the fnlwgt column.

Output:

Example 3:

To supply the dtype in this example, we are just giving the arguments in the same function.

Output:

Converting Pandas Series to NumPy Array

Using to_numpy()

A NumPy ndarray representing the values in a particular Series or Index is returned by the Series.to_numpy() method in the Pandas library.

We can transform the pandas Series to a NumPy Array using this function. Despite being relatively simple, this strategy has a really unique concept. We are aware that the Series output has an index. In contrast, NumPy arrays simply contain their elements.

Syntax:

Parameters:

• dtype: Data type that we are supplying, such as str.
• copy: [bool, default False] Checks to make sure the returned value is not a view on another array.

Example 1:

Utilizing the Series.to_numpy function to convert a Series into a NumPy array. Always keep in mind that cleaning the data before using it will ensure excellent accuracy when working with large amounts of data. Even so, we utilize the .head() function to access the first five values of the Weight column in this code.

Output:

Example 2:

To supply the dtype in this example, we are just giving the arguments in the same function.

Output:

Using as_matrix()

To express a given series or data frame object as a Numpy-array, utilize the Pandas Series.as_matrix() method.

Syntax:

Parameter:

• columns : Return all columns if None is chosen; otherwise, return the specified columns.

Returns : ndarray

Example 1: To return the numpy-array representation of the specified series object, use the series.as_matrix() method.

Output: