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How to filter for a specific value in a dataframe in pandas

Are you using pandas with a dataframe, possibly with a huge amount of data, and you want to filter a dataframe for rows where the column value is equal to something? If so, you can learn how to do this with pandas in this article.

Assume we have the following dataframe:

employees_df = pd.DataFrame({'salary': [20000, 50000, 100000, 30000, 70000, 50000, 70000, 45000], 'name': ['John', 'Linda', 'Sam', 'Albert', 'Francis', 'Tara', 'Susan', 'Wayne'], 'age': [23, 35, 27, 44, 52, 25, 32, 35]})

# Outputting the dataframe

employees_df
salary     name  age
0   20000     John   23
1   50000    Linda   35
2  100000      Sam   27
3   30000   Albert   44
4   70000  Francis   52
5   50000     Tara   25
6   70000    Susan   32
7   45000    Wayne   35

Let’s say we want to find all the rows in employees_df
where the salary is 70000.

We can do that with the following command:

employees_df[employees_df['salary'] == 70000]

# below is the output we would get

salary     name  age
4   70000  Francis   52
6   70000    Susan   32

We could do a variation of the above with:

employees_df[employees_df.salary == 70000]

And we would have gotten the same output. Using either dot notation or bracket notation will work for filtering.

Now, let’s say that we want to find all the rows in employees_df where age is 35. We can find that with:

employees_df[employees_df['age'] == 35]

We’d get the following output:

salary   name  age
1   50000  Linda   35
7   45000  Wayne   35

We could have also done

employees_df[employees_df.age == 35]

and gotten the same output.

Basic Pattern For Filtering A Dataframe For A Specific Column Being Equal To Some Value

Let’s call a general dataframe df. The basic pattern for filtering df for some column’s value being equal to something would be:

df[df['column_name'] == some_value]

Or

df[df.column_name == some_value]

Do you have anything to add? Let’s discuss it in the comments below.

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