Python

Pandas to_records

Pandas DataFrame is a heterogeneous two-dimensional size-mutable tabular data format with named axes.  For mathematical operations, the row and column labels must always be aligned. The Pandas’ fundamental data structure is comparable to a dict in that it acts as a container for Series items.

DataFrame is converted to a NumPy record array using Pandas DataFrame.to records() function. The index is usually given as the initial field of the record. We’ll discuss the pandas to_records() method with examples in this article.

What Is a Data Frame?

Pandas is a popular Python data science package for a reason: it provides powerful, expressive, and flexible data structures that facilitate data manipulation and analysis, among other things. The DataFrame is one of those structures.

A data frame is a two-dimensional data structure in which the data is represented in the form of rows and columns. A Pandas DataFrame is made up of three primary components: data, rows, and columns.

Because they provide a flexible and easy manner of storing and working with data, DataFrames are one of the most often used data structures in modern data analytics.

To create a pandas DataFrame, Lists, series, dicts, Numpy ndarrays, and another DataFrame can all be used to create a pandas DataFrame.

What is the Syntax of Pandas.DataFrame.to_records() Functions?

The to_records() method is for converting a DataFrame to a relevant NumPy record array. The to_records() function in Pandas has the following syntax.

# DataFrame.to_records(index=True, column_dtypes=None, index_dtypes=None)

All the details about the parameters is in the following section.

Index

Include the index in the resulting record array, which can be stored in the ‘index’ field or accessed via the index label, if one is set.

Column_dtypes  

If the data type is a string or a type, it will be used to store all columns.

Index_dtypes

If the data type is a string or perhaps a type, it will be used to hold all index levels. If it’s a dictionary, index level names mapping, as well as indices, are used to specify data types. Only if index=True is this mapping used.

Let’s discuss the topic in-depth with different examples. You can implement these examples and see the to understand the overall concept in a better way.

Example 1:

The DataFrame.to_records() function of Python is to alter the specified Dataframe into a NumPy record array. Consider the given code below as an example.

We constructed a dataframe with values for three different qualities: ‘Salary, Employee Name, and Age’; 39000, 44000, 25000, and 55000 are the values for the property ‘Salary.’ Alex, Andrew, Zack, and Kim are among the values for the attribute ‘Employee name.’

In the same way, the age attribute comprises the values 39, 44, 25, and 55. The index was constructed and set. The dataframe is then printed.

import pandas

d_frame = pandas.DataFrame({'Salary':[39000, 44000, 25000, 55000],

   'Employee Name':['Alex', 'Andrew', 'Zack', 'Kim'],

   'Age':[39, 44, 25, 55]})

index_ = pandas.date_range('2020-08-05 07:30', periods = 4, freq ='H')

d_frame.index = index_

print(d_frame)

The salary, employee name, and age values, as well as the date and time, are shown in the screenshot below.

In the following screenshot, the specified dataframe to a relevant numpy record array representation is shown.

import pandas

d_frame = pandas.DataFrame({'Salary':[39000, 44000, 25000, 55000],
 
   'Employee Name':['Alex', 'Andrew', 'Zack', 'Kim'],

   'Age':[39, 44, 25, 55]})

index_ = pandas.date_range('2020-08-05 07:30', periods = 4, freq ='H')

d_frame.index = index_

print(d_frame)

outcome = d_frame.to_records()

print(outcome)

The next attached image explains that the DataFrame.to_records() function is utilized to alter the dataframe to a relevant NumPy record array representation.

Example 2:

We’ll look at how to use the DataFrame.to_records() function in the second example. The code is similar to the one mentioned above. We’ve imported the pandas module first this time, then generated the dataframe.

The index was then formed as shown in the code: index_ = [‘First Row’,’Second Row’,’Third Row’,’Fourth Row’, and ‘Fifth Row’]. Finally, we’ve set the index and printed the dataframe results. Look at the code’s last line.

import pandas

dta_frame = pandas.DataFrame({"1":[9, 3, 4, None, 6],

   "2":[8, 1, 22, 35, None],

   "3":[19, 9, 17, 6, 9],

   "4":[55, 2, None, 1, 9]})

index_ = ['First_Row', 'Second_Row', 'Third_Row', 'Fourth_Row', 'Fifth_Row']

dta_frame.index = index_

print(dta_frame)

You can see the result here, which includes the index and the dataframe.

By using the DataFrame.to records() function, we converted the given dataframe. You can view it in the following screenshot attached.

import pandas

dta_frame = pandas.DataFrame({"1":[9, 3, 4, None, 6],

  "2":[8, 1, 22, 35, None],

  "3":[19, 9, 17, 6, 9],

  "4":[55, 2, None, 1, 9]})

index_ = ['First_Row', 'Second_Row', 'Third_Row', 'Fourth_Row', 'Fifth_Row']

dta_frame.index = index_

print(dta_frame)

outcome = dta_frame.to_records()

print(outcome)

After making minimal changes to the above reference code, this is the result. The DataFrame.to_records() function alters a specific dataframe to a relevant NumPy record array representation.

Example 3:

In our final example, we’ll look at how to convert a Pandas dataframe using the numpy technique—the pandas. The to_numpy method can be used to transform this data structure into a NumPy array.

You can see in the code that we have imported the pandas and numpy modules, which are essential for the code to run. We built the dataframe after importing the modules. There are 5 rows and 3 columns of data in the dataframe.

After that, the dataframe is transformed to a numpy array. The transformed dataframe and its type are displayed.

import pandas

import numpy

data_frame = pandas.DataFrame(

   data = numpy.random.randint(

     0, 10, (5,3)),

   columns =["a", "b", "c"])

cls_res = data_frame.to_numpy()

print(cls_res)

print(type(cls_res))

The output is in the next screenshot. The data values within the constructed dataframe’s 5 rows and 3 columns can be seen here. The class is also displayed in the output’s last line. As illustrated above, Pandas DataFrame to NumPy() method turns a DataFrame into a NumPy array.

Conclusion:

The to_record() function in Python is described in this article. The Pandas dataframe is also well taught with examples. The steps for converting a Dataframe by utilizing the DataFrame to_records() method is also explained.

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Kalsoom Bibi

Hello, I am a freelance writer and usually write for Linux and other technology related content