How to handle missing numerical data in a dataset using the pandas Python library?

by | Nov 12, 2022 | Data Preprocessing, Machine Learning Using Python, Python Pandas

We often see missing values in a dataset. Missing values are those values in a dataset that does not contain any data. These missing values, if not handled properly, can change data patterns. So, it is extremely important to handle missing values in a dataset.

A dataset column can contain numerical data or categorical data. When a dataset column contains numerical data and the column has missing values, we can use statistical techniques to handle those missing data. Using statistical techniques to handle missing numerical values is also called imputation.

Let’s look into a dataset first. Let’s read the titanic data set and see what all columns contain missing values.

import pandas

df = pandas.read_csv("titanic.csv")
print(df.info())
print("Percentage of missing values: \n", df.isnull().mean()*100)

The output is like the following:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   survived     891 non-null    int64  
 1   pclass       891 non-null    int64  
 2   sex          891 non-null    object 
 3   age          714 non-null    float64
 4   sibsp        891 non-null    int64  
 5   parch        891 non-null    int64  
 6   fare         891 non-null    float64
 7   embarked     889 non-null    object 
 8   class        891 non-null    object 
 9   who          891 non-null    object 
 10  adult_male   891 non-null    bool   
 11  deck         203 non-null    object 
 12  embark_town  889 non-null    object 
 13  alive        891 non-null    object 
 14  alone        891 non-null    bool   
dtypes: bool(2), float64(2), int64(4), object(7)
memory usage: 92.4+ KB
None
Percentage of missing values: 
survived        0.000000
pclass          0.000000
sex             0.000000
age            19.865320
sibsp           0.000000
parch           0.000000
fare            0.000000
embarked        0.224467
class           0.000000
who             0.000000
adult_male      0.000000
deck           77.216611
embark_town     0.224467
alive           0.000000
alone           0.000000
dtype: float64

So, the dataset contains numerous columns. Out of all the columns, the age column contains floating point values and it has …

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Amrita Mitra

Author

Ms. Amrita Mitra is an author, who has authored the books “Cryptography And Public Key Infrastructure“, “Web Application Vulnerabilities And Prevention“, “A Guide To Cyber Security” and “Phishing: Detection, Analysis And Prevention“. She is also the founder of Asigosec Technologies, the company that owns The Security Buddy.

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