Implementation of mean fill using Pandas

Missing data (NaN values) is a common problem during data analysis and processing. Missing data may lead to incorrect analysis results or model predictions. In Pandas, we can handle missing data in a variety of ways, one of the common methods is mean padding. This article will explain in detail how to use Pandas for mean filling and provide actual code examples.

What is mean padding?

Mean filling is a simple and commonly used method to deal with missing data. It calculates the mean of each feature and fills that mean at the position of the missing value. This method is suitable for situations where data is missing randomly and not much.

Why choose mean padding?

Simple and easy to do: Calculating and filling the mean is very simple and does not require complex calculations.
Keep data size: Mean padding does not change the size of the dataset, only replaces missing values.
Suitable for numerical data: Mean fill is suitable for processing missing values ​​for numerical data.

Steps to fill the mean

• Loading data
• Check for missing values
• Calculate the mean
• Fill in missing values
• Verify the fill results

Actual code examples

Suppose we have a dataset containing student grades with some missing values. We will use Pandas for mean padding.

• Loading data
  First, we import the necessary libraries and load the data.

import pandas as pd
import numpy as np

# Create a sample datasetdata = {
    'Math': [85, 78, , 90, 95, , 88],
    'Science': [, 88, 92, 85, , 95, 90],
    'English': [78, , 85, 90, 87, 88, ]
}

df = (data)
print("Raw Data:")
print(df)

Output:

Raw data:
   Math  Science  English
0  85.0      NaN     78.0
1  78.0     88.0      NaN
2   NaN     92.0     85.0
3  90.0     85.0     90.0
4  95.0      NaN     87.0
5   NaN     95.0     88.0
6  88.0     90.0      NaN

• Check for missing values
  We can use the isnull() and sum() methods to check for missing values ​​in the dataset.

print("Missing value statistics:")
print(().sum())

Output:

Missing value statistics:
Math       2
Science    2
English    2
dtype: int64

• Calculate the mean
  Use the mean() method to calculate the mean of each column.

means = ()
print("Mean value per column:")
print(means)

Output:

Mean per column:
Math       87.2
Science    90.0
English    85.6
dtype: float64

• Fill in missing values
  Use the fillna() method to replace the missing values ​​with the mean of the corresponding column.

df_filled = (means)
print("Filled data:")
print(df_filled)

Output:

Populated data:
   Math  Science  English
0  85.0     90.0     78.0
1  78.0     88.0     85.6
2  87.2     92.0     85.0
3  90.0     85.0     90.0
4  95.0     90.0     87.0
5  87.2     95.0     88.0
6  88.0     90.0     85.6

• Verify the fill results
  We can check again whether there are missing values ​​to ensure the filling is successful.

print("Statistics of missing values ​​after filling:")
print(df_filled.isnull().sum())

Output:

Statistics of missing values ​​after filling:
Math       0
Science    0
English    0
dtype: int64

Summarize

Mean fill is a simple and effective way to deal with missing data. With Pandas' fillna() method, we can easily achieve this. When dealing with missing data, it is crucial to choose the appropriate method, and mean fill is suitable for situations where numerical data and missing values ​​are not particularly large.

In practical applications, other methods for processing missing data need to be selected according to specific circumstances, such as median filling, mode filling, interpolation method, etc. Hope this article helps you better understand and apply Pandas for mean filling.

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