Question

How do you filter via a column value in a panda in python

ie grab all rows where column_1 = some_value

Answer 1

Often, you want to subset a pandas data frame based on one or more values of a specific column. Essentially, we would like to select rows based on one value or multiple values present in a column.

Here are multiple examples of using pandas dataframe to filter rows or select rows. Let us first load a dataframe into pandas.

data_url = 'something you can enter here'

# read data from url as pandas dataframe

gapminder = pd.read_csv(data_url)

print(gapminder.head(3))

This dataframe has over 6000 rows and 6 columns. One of the columns is year. Let us say we want to filter the data frame such that we get a smaller data frame with year values equal to 2002. That is, we want to subset the data frame based on values of year column. We keep the rows if its year value is 2002, otherwise we don’t.

Select Rows of Pandas Data frame Based on a Single Value of a Column

One way to filter by rows in Pandas is to use Boolean expression. We first create a variable by taking the column of interest and checking if its value equals to the specific value that we want to select/keep.

For example, let us filter the dataframe or subset the dataframe based on year’s value 2002. This conditional results in a boolean variable that has true when the value of year equals 2002, false otherwise.

# does year equals to 2002?

# is_2002 is a boolean variable with True or False in it

>is_2002 = gapminder['year']==2002

>print(is_2002.head())

We can then use this boolean variable to filter the dataframe. After subsetting we can see that new dataframe is much smaller in size.

# filter rows for year 2002 using the boolean variable

>gapminder_2002 = gapminder[is_2002]

>print(gapminder_2002.shape)

(142, 6)

We have successfully filtered pandas dataframe based on values of a column. Here, all the values of the variable year will be 2002.

>print(gapminder_2002.head())

        country year         pop continent lifeExp    gdpPercap

10 Afghanistan 2002 25268405.0      Asia   42.129   726.734055

22      Albania 2002   3508512.0    Europe   75.651 4604.211737

34      Algeria 2002 31287142.0    Africa   70.994 5288.040382

46       Angola 2002 10866106.0    Africa   41.003 2773.287312

58    Argentina 2002 38331121.0 Americas   74.340 8797.640716

Note that we don’t really have to create a new boolean variable and save it. Instead, we can diectly give the boolean expression to subset the dataframe by column value as follows.

# filter rows for year 2002 using the boolean expression

>gapminder_2002 = gapminder[gapminder['year']==2002]

>print(gapminder_2002.shape)

(142, 6)

We can also use chain operation, to access a dataframe’s column, to select rows like previous example

# filter rows for year 2002 using the boolean expression

>gapminder_2002 = gapminder[gapminder.year == 2002]

>print(gapminder_2002.shape)

(142, 6)

In the above example, we check of equality and kept the rows matching a specific value. We can use any other comparison operator like “less than” and “greater than” and create boolean expression to filter rows of pandas dataframe.

All the syntax I have taken has examples please check it