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Data cleaning by practice using Python

General Introduction

According to Wikipedia, Data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a recordset and refers to identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data. Then replacing, modifying, or deleting dirty or coarse data.

The implementation of this process goes through different stages that we will try to show around a practical project.

You will find the dataset on the Datacamp platform with the following name "Measles Data".

To be successful in this process one of the most important phases is to understand the data.

First of all, let's import our data to get an overview.

import pandas as pd
measles = pd.read_csv('data/measles.csv')

The dataset presents us with the following information.
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 46411 entries, 0 to 46410
Data columns (total 16 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   index     46411 non-null  int64  
 1   state     46411 non-null  object 
 2   year      41730 non-null  object 
 3   name      46411 non-null  object 
 4   type      19237 non-null  object 
 5   city      29072 non-null  object 
 6   county    41253 non-null  object 
 7   district  0 non-null      float64
 8   enroll    33567 non-null  float64
 9   mmr       46411 non-null  float64
 10  overall   46411 non-null  float64
 11  xrel      94 non-null     object 
 12  xmed      12972 non-null  float64
 13  xper      6411 non-null   float64
 14  lat       44859 non-null  float64
 15  lng       44859 non-null  float64
dtypes: float64(8), int64(1), object(7)
memory usage: 5.7+ MB

Data analysis

After this overview we can identify some major problems on our dataset which are the following:

  • There is missing data

index           0
state           0
year         4681
name            0
type        27174
city        17339
county       5158
district    46411
enroll      12844
mmr             0
overall         0
xrel        46317
xmed        33439
xper        40000
lat          1552
lng          1552
dtype: int64

We notice that the district column is unused.

Column Year

Year should be in the format datetime.dt.year.

We have to convert the year column into this format.

When we look at the following code.

array(['2018-19', '2017-18', nan, '2017'], dtype=object)

We notice that there is 2 year, the start year and the end year. So we should split the year column into two-column startYear and endYear.

Column state, name, city, type, and county

The previous column should be converted in a string type

let's look at the column type in detail.

array(['Public', 'Charter', 'Private', nan, 'Kindergarten', 'Nonpublic',
       'BOCES'], dtype=object)

This column should be converted into category type because we have several known values ​​that vary very little.

On the other hand for the other columns of string type, we cannot do it because we have too many distinct values.

The missing value for the type column should be replaced by Missing.

Column enroll, overall,xrel, xmed, and xper

The enroll column represents the number of people registered so the correct type for this column is an integer.

When we try to see the min value for the overall column we get.


When we try to see the min value for the mmr column we get.


When we try to see the max value for the xper column we get.


overall, mmr, xrel, xmed, and xper represent values ​​in percentages. Therefore, they must be between 0 and 100. This implies that the missing values ​​for these columns will have the value 0.

array([nan, True], dtype=object)

For the xrel column available value is in boolean type.

So we will delete this column because the data does not represent what it is supposed to represent.

Column lat and lng

for the following column, we will simply replace not available values with 0.

  • Checking duplicate values

Fortunately for us, this dataset does not present values ​​which are duplicated.


Cleaning Data

We will clean the data by columns

  • dropping columns

We will drop irrelevant columns.

measles.drop('xrel', axis=1, inplace=True)
measles.drop('district', axis=1, inplace=True)
Index(['index', 'state', 'year', 'name', 'type', 'city', 'county', 'district',
       'enroll', 'mmr', 'overall', 'xrel', 'xmed', 'xper', 'lat', 'lng'],

  • The column type

Let's handle missing value.

measles.loc[measles['type'].isna(), 'type'] = 'Missing'
array(['Public', 'Charter', 'Private', 'Missing', 'Kindergarten',
       'Nonpublic', 'BOCES'], dtype=object)

Let's change the data type of the current column.

count       46411
unique          7
top       Missing
freq        27174
Name: type, dtype: object

  • The column enroll

Let's handle missing value.

measles.loc[measles['enroll'].isna(), 'enroll'] = 0

Let's change the data type.

count    46411.000000
mean        51.571396
std         46.786130
min         -1.000000
25%         -1.000000
50%         83.330000
75%         95.612018
max        100.000000
Name: overall, dtype: float64

  • The column overall, xmed, xper, lat and lng

Let's correct the data

measles.loc[measles.overall < 0, 'overall'] = 0
measles.loc[measles.xmed < 0, 'xmed'] = 0
measles.loc[measles.xper > 100, 'xper'] = 0
measles.loc[measles.xmed.isna(), 'xmed'] = 0
measles.loc[measles.xper.isna(), 'xper'] = 0
measles.loc[, 'lat'] = 0
measles.loc[measles.lng.isna(), 'lng'] = 0

  • The column state, name, city, and county

Let's fix the data type.


  • Creation of startYear and endYear columns

Let's create new columns startYear and endYear.

measles[['startYear', 'endYear']] = measles['year'].str.split('-', expand=True)
measles.endYear = '20'+measles.endYear

Let's drop the year column.

measles.drop('year', axis=1, inplace=True)

Let's change the column's data type.

measles.startYear = pd.to_datetime(measles.startYear, format='%Y')
measles.endYear = pd.to_datetime(measles.endYear, format='%Y')

Let's try to get an overview of the result of our data cleansing.



In conclusion, we have tried to perform the data cleansing on the measles data.

In our article, we have explained some techniques in a more practical than theoretical way.

We remember that cleaning remains one of the most important steps in a data study, it should not be neglected and it should be taken seriously.

If done well, it can improve the performance of the study to follow.

This process aims to correct inconsistent data or not used for our study that could have slipped into our dataset.


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