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Predict a Value using Pandas Dataframe

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by Abdul Rawoof A R dot icon Updated: Feb 20, 2023

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In Python, predict() function enables us to predict the labels of the data values based on the trained mode, and the syntax for predict function is the model.predict(data). It accepts only a single argument, usually the data to be tested.

 

Predict function passes the input vector through the model and returns the output tensor for each data point, and since the last layer in your model is a single Dense neuron, then the output for any data point is a single value. We did not specify an activation for the last layer, and it will default to linear activation in Python. The predicted value is nothing but simulations that consider the estimation uncertainty and the fundamental uncertainty. For example, for any given of x, we go straight in the line and then move horizontally to the left of the data to discover the value of Y. The anticipated value of Y is called the predicted value of Y, and it is denoted Y'. Dependent variables are those we want to predict, whereas independent variables are variables we use to predict the other variable's value. 


Libraries used to predict a value using Pandas DataFrame: 

  • Pandas: It is an open-source library that provides high-performance data manipulation in Python. 
  • Matplotlib: a comprehensive library for creating animated, interactive visuals and static. 
  • NumPy: It is a python library used for work with arrays. 


Here is an example of how to predict a value using Pandas DataFrame: 

Fig : Preview of the output that you will get on running this code from your IDE.

Code

In this solution we're using Pandas, Matplotlib and NumPy libraries.

How to predict a value using pandas data frame?

PythondotLines of Code : 32dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
% matplotlib inline

import pandas as pd
import numpy as np

# Generate some test data with a trend.

data = pd.DataFrame(
    {
        'year': list(range(1900, 2000)),
        'life_exp': np.linspace(50, 80, 100) * ((np.random.randn(100, ) * 0.1) + 1)
    }
)

data[['life_exp']].plot()

# Fit coefficents.

coef = np.polyfit(data['year'], data['life_exp'], 1)

# Generate predictions for entire series.

data['predicted'] = pd.Series(np.polyval(coef, data['year']))

data[['life_exp', 'predicted']].plot()

# Passing in a single year.

x = 1981

print('Predicted life expectancy for {}: {:.2f} years'.format(x, np.polyval(coef, x)))

Instructions

Follow the steps carefully to get the output easily.

  1. Install pandas on your IDE(Any of your favorite IDE).
  2. Copy the snippet using the 'copy' and paste it in your IDE.
  3. Add required dependencies and import them in Python file.
  4. Run the file to generate the output.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for 'how to predict a value using pandas dataframe' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2021.3.
  2. The solution is tested on Python 3.9.7.
  3. Pandas version-v1.5.2.
  4. numpy version-v1.24.0.
  5. Matplotlib version-v3.6.2.


Using this solution, we are able to predict a value using pandas dataframe with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to predict a value using pandas dataframe.

Dependent Libraries

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support
    Quality
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            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

                      Support
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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
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                                    Security
                                      License
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                                          matplotlibby matplotlib

                                          Python doticonstar image 17559 doticonVersion:v3.7.1doticon
                                          no licences License: No License (null)

                                          matplotlib: plotting with Python

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                                                    matplotlibby matplotlib

                                                    Python doticon star image 17559 doticonVersion:v3.7.1doticonno licences License: No License

                                                    matplotlib: plotting with Python
                                                    Support
                                                      Quality
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                                                              You can also search for any dependent libraries on kandi like 'pandas', 'matplotlib' and 'numpy'.

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