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Last Updated: Mar 27, 2024

Data Cleaning

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


Clean and accurate data beats fancy algorithms. We may tune the algorithm’s hyper-parameters and add as much complexity as possible, but our results will not make sense if our data is not clean. 

Data cleaning is crucial in data preprocessing as incorrect information, missing data, redundant rows, and so on can make analyzing the data very difficult. Our machine learning models will give errors or inaccurate results if our data is not clean. This article will discuss almost everything you need to know about data cleaning.

Handling missing values in the dataset

In massive datasets, there could be a lot of missing values as some data is either never recorded or gets lost. There are a lot of fields that are filled with NaN (Not a Number), n/a, or nan. This usually means that the value is either undefined or unrepresented. Dealing with these NaN, null or missing values are an integral part of data cleaning.

Let us see this in code. You can download that dataset from here.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

print(placement_df.head(5)) #printing the first five entries


#To find columns with null values


We notice that there are 215 entries in all columns. The Salary column has 148 non-null entries of data type float. 

We can replace null values with mean, mode, and median. But which one should we use when? We can find this out using plots! If the data distribution is skewed, outliers are present in the dataset. This means that replacing null values with mean would make our model biased towards weighted data. 


print(sns.boxplot(x=placement_df.salary))# Box plot


From the above box plot, we see that the density of salary between 200000 and 300000 is very high. However, we also see that there are some outliers too, like the salaries above 600000. 

print(sns.displot(x=placement_df.salary,kde=True))# Distribution plot


From the above histogram, we can see that the Salary distribution is skewed. Hence, using the mean to replace the null values would not be a good idea.

Some of the methods to handle null values are given below:

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Dropping Columns or Rows with missing values

Sometimes, some columns have missing values, and there is usually nothing we can do about it because either the column of type string, date, object, or any non-numeric value. In such cases, we could drop that row. When we have a lot of rows, and we know that deleting a few rows won’t affect the output, we can drop the rows. Moreover, we can drop the whole column if it is not relevant for making predictions or does not correlate with any other dataset features. We saw earlier that the Salary column has null values. If the Salary column is not relevant to you, you can drop the whole column. However, you can just drop the rows with NaN values if the column is needed.

#handling NaN values by dropping column


#handling NaN values by dropping rows
df_after_dropping_nan_rows=placement_df.dropna() print(df_after_dropping_nan_rows.head(5))


Filling missing values with mean

As discussed earlier, when the data distribution is skewed, using the mean to fill missing values is not a good decision. However, if a dataset doesn’t have outliers, you can use the column’s mean to fill the NaN values.


#filling null values with mean


Filling missing values with the median

Using this technique to handle the missing values in data cleaning is a good option for skewed data distribution when dealing with numerical data. 


#filling null values with median


Filling missing values using the mode

When the data distribution is skewed, the mode is an excellent choice to replace the missing values. Replacing the NaN values with the data with the highest frequency in the salary column is a great alternative to using the median.

#filling null values with mode


Filling the missing values with values that come before or after them

When observations have some logical order, replacing the NaN values with those before or after them in the column is a good substitute.

# replace all NA's the value that comes directly after it in the same column, 
# then replace all the remaining na's with 0
placement_df_bfill["salary"]=placement_df["salary"].fillna(method='bfill', axis=0).fillna(0)


# replace all NA's the value that comes directly before it in the same column, 
# then replace all the remaining na's with 0
placement_df_ffill["salary"]=placement_df["salary"].fillna(method='ffill', axis=0).fillna(0)


Removing duplicate rows

We should remove any duplicate rows from the dataset. But first, we need to find the duplicate rows:

#show all the duplicate values as True except for the first
print(placement_df.duplicated(subset=None, keep='first'))


We can see that in our dataset, there are no duplicate rows. Now, let us assume that the first two rows are duplicate, then we can drop the rows using the code below:

#dropping second-row assuming that it is the duplicate of the first row 


Getting relevant or usable form of data from columns

In a dataset, sometimes the values of the features will be stored in a format that you won’t be able to use directly to analyze your data. For instance, 

  • a feature can have values in the form of an object, 
  • numbers can be stored in the form of strings, 
  • date is not stored in datetime format, and so on. 


How to deal with it? Let’s look at this with the help of a few examples in code. You can download the CSV file from here.



Parsing dates

movie_df_copy=movie_df.dropna() #dropping all rows with NaN values
print(movie_df_copy["release_date"].dtypes) #printing the datatype of date


We want to convert the release_date column from object to datetime64[ns] type so that we can easily access the day, month and year. 

##making a new column with datetime64[ns] type
movie_df_copy['parsed_release_date'] = movie_df_copy['release_date'].astype('datetime64[ns]')
print("Datatype of parsed_released_date column")
print(movie_df_copy['parsed_release_date'].dtypes) #printing the datatype 


Now, if we want, we can save the day, month, year in separate columns of the data frame and then use it to make analyzing the data convenient.

Converting string to object

The genres column stores values in a string of array of objects format. Usually, we would like to work with only the genre name to analyze the dataset. To get just the genre names, we code the following:

import json #importing json
print(movie_df["genres"][0]) #printing the first entry in genres column
print(type(movie_df["genres"][0])) #printing the datatype of genres entry
for json_obj_arr_str in movie_df["genres"]:
    genre_obj_arr=json.loads(json_obj_arr_str) # converts the string to array of json objects
    for j in range(len(genre_obj_arr)):
        genre_arr.append(genre_obj_arr[j]["name"]) #getting just the name of the genre
genre_df=pd.DataFrame(genre_arr, columns = ['genres']) #making dataframe for all the genres


Apart from these, the data may be stored in formats that won’t be convenient to use. Sometimes there could be different formats for the same column. So, parsing the data or converting it to a usable form is very crucial in data cleaning.

Filtering outliers

Real-world data will always have outliers. Sometimes, these outliers will help us better analyze the data or train our machine learning model. However, the data collected can often be factually wrong, resulting in inaccurate outputs. In such cases, finding the false outliers and removing them is essential in data cleaning. 

How do we find these outliers? We can find them using the Seaborn displots and histograms!

sns.displot(x=movie_df["runtime"]) #displot to see the data distribution and outliers


The plot shows that around 40 movies have a runtime of 0 minutes. This is not factually correct. 

movie_df[movie_df['runtime']<50].head(3) #output first three movies with runtime less than 50


We need to remove all the movies with runtime 0 minutes.

movie_df_filtered=movie_df[movie_df["runtime"]>0] #removing wrong outliers
print(movie_df_filtered[movie_df_filtered["runtime"]==0]) #checking if outliers were removed


Removing inconsistency in data

When people fill survey forms online, some type their name in all capital letters while others fill it with only the first letter in capital. From this survey data, if we try to search for a name, say “Rita”, we won’t be able to find it if it is stored as "rita” or “RITA”. Analyzing such inconsistent data becomes difficult. Thus, we do data cleaning to remove these inconsistencies. 

Let’s look at an example of inconsistent data through code. Download the dataset from here.



zomato_df["city"].unique()#printing the unique values of city column


Removing white spaces

We see that there are white spaces in the city names like Mumbai, Pune. Let’s remove it:

#removing whitespaces from the city names


Removing capitalization inconsistencies 

Looking at the unique values, we find cities like Agra have inconsistent capitalization. Let’s correct it: 

# Lower case cities column
zomato_df_letter['city'] = zomato_df_letter['city'].str.lower()

# Capitalize first letter 
zomato_df_letter['city'] = zomato_df_letter['city'].str.capitalize()


Frequently Asked Questions

Is it always necessary to filter outliers?

There will frequently be data that don't appear to fit into the dataset you are studying. If you have a good reason to delete outliers, such as incorrect data entry, doing so will make the data you're working with perform better. The presence of an outlier, on the other hand, can sometimes prove a theory you're working on. Just because an outlier exists doesn't mean it is wrong. Consider deleting an outlier only if it appears irrelevant for analysis or is a mistake.

What may cause data duplication?

Duplicate data are most likely to occur during data collection. Duplicate data can be created when you integrate data sets from numerous sources, scrape data, or get data from clients or multiple departments.

What are the various validity constraints?

Validity constraints are:

  • Data types constraints: values in a column must be of a specific datatype, such as boolean, numeric, or date.
  • Range Constraints: Numbers or dates should typically fall within a specific range.
  • Mandatory Constraints: Some columns can't be left blank.
  • Unique Constraints: Within a dataset, a field, or a combination of fields, must be unique.
  • Set-Membership constraints: A column's values are drawn from a collection of discrete values. A person's gender, for example, can be male or female.
  • Foreign-key constraints: A foreign key column can't have a value that doesn't exist in the referenced main key, just like in relational databases.
  • Text fields that must follow a specific pattern are known as regular expression patterns. Phone numbers, for example, may be required to follow a specific pattern.
  • Validation across fields: specific conditions must be met across numerous fields. The date of discharge from the hospital, for example, cannot be earlier than the date of admission.


In this article, we learned about data cleaning. We learned how to handle missing values in the dataset, remove redundant data, bring consistency to the data, filter outliers and convert the data into a usable form. We learned how vital data cleaning is to make analyzing the data easier. 

Check out this problem - Duplicate Subtree In Binary Tree

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Topics covered
Handling missing values in the dataset
Dropping Columns or Rows with missing values
Filling missing values with mean
Filling missing values with the median
Filling missing values using the mode
Filling the missing values with values that come before or after them
Removing duplicate rows
Getting relevant or usable form of data from columns
Parsing dates
Converting string to object
Filtering outliers
Removing inconsistency in data
Removing white spaces
Removing capitalization inconsistencies 
Frequently Asked Questions
Is it always necessary to filter outliers?
What may cause data duplication?
What are the various validity constraints?