Pandas for SQL Lovers: Handling Nulls read from CSV

In part 3 of the series I covered how to load a CSV file into a Pandas DataFrame. In this post I’ll focus on how to deal with NULL or missing values read from CSV files.

Also in this series:

• Creating a DataFrame (CREATE TABLE)
• Populating a DataFrame (INSERT) 
• How to load a CSV file into a Pandas DataFrame (BULK INSERT)
• Handling Nulls read from CSV
• Join statements 
• Select all rows and columns (SELECT * FROM table)
• Select multiple columns (SELECT col1,col2 FROM table)

If you work with SQL you have probably had your share of challenges caused by missing/blank and NULL values. Let’s see how read_csv helps us manage these troublemakers when we populate a DataFrame from a csv file.

Let’s start with the following data in a CSV file:

FirstName,LastName,Team,Position,JerseyNumber,Salary,Birthdate
Joe,Pavelski,SJ,C,8,6000000,1984-07-11
Connor,McDavid,EDM,C,97,925000,1997-01-13
Sidney,Crosby,NULL,C,87,8700000,1987-08-07
Carey,Price,Unknown,G,31,10500000,1987-08-16
Daniel,Sedin,VAN,NA,22,,1980-09-26
Henrik,Sedin,VAN,N/A,33,,1980-09-26

I have deliberately provided a variety of values that can be construed as missing values

• Sidney Crosby: Team is NULL
• Carey Price” Team is Unknown
• Daniel Sedin: Position is NA and salary is not provided
• Henrik Sedin: Position is N/A and salary is not provided

Leave NULL or missing values untouched

If you specify na_filter=false then read_csv will read in all values exactly as they are:


players = pd.read_csv('HockeyPlayersNulls.csv',na_filter=False)

returns:

Replace default missing values with NaN

In Pandas, the equivalent of NULL is NaN. By default, read_csv will replace blanks, NULL, NA, and N/A with NaN:


players = pd.read_csv('HockeyPlayersNulls.csv')

returns:

You can see that most of the ‘missing’ values in my csv files are replaced by NaN, except the value ‘Unknown’ which was not recognized as a missing value.

How to designate values as missing

If there are values in your data which are not recognized as missing, you can use the na_values parameter to specify values you want treated as missing:


players = pd.read_csv('HockeyPlayersNulls.csv',na_values=['Unknown'])


returns:

Handling blank lines

By default if a blank line is encountered in the CSV file, it is skipped.

So if you have the following file:
FirstName,LastName,Team,Position,JerseyNumber,Salary
Joe,Pavelski,SJ,C,8,6000000
Connor,McDavid,EDM,C,97,925000

Sidney ,Crosby,PIT,C,87,8700000
Carey,Price,MTL,G,31,10500000
Daniel,Sedin,VAN,LW,22,
Henrik,Sedin,VAN,C,33,

You get the following DataFrame:

If you want the blank line to appear you can specify skip_blank_lines=False
players = pd.read_csv('HockeyPlayersBlankLines.csv', skip_blank_lines=False)

returns:

Next up

We still need to look at how to control datatypes and how to deal with Dates when using read_csv to populate a DataFrame. Stay tuned!

 

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