*39*

Often you may be interested in only counting the number of rows in a pandas DataFrame that meet some criteria.

Fortunately this is easy to do using the following basic syntax:

sum(df.column_name == some_value)

The following examples show how to use this syntax in practice on the following data frame:

**import pandas as pd
#create DataFrame
df = pd.DataFrame({'x': [3, 4, 5, 6, 7, 8, 9, 10, 10, 12, 13],
'y': [3, 4, 5, 7, 9, 13, 15, 19, 23, 24, 29]})
#view head of DataFrame
df.head()
x y
0 3 3
1 4 4
2 5 5
3 6 7
4 7 9
**

**Example 1: Count Rows Equal to Some Value**

The following code shows how to count the number of rows where the x variable is equal to 10:

sum(df.x == 10) 2

The following code shows how to count the number of rows where the x variable is equal to 10 *or* the y variable is equal to 5:

sum((df.x == 10) | (df.y == 5)) 3

The following code shows how to count the number of rows where the x variable is *not* equal to 10:

sum(df.x != 10) 9

**Example 2: Count Rows Greater or Equal to Some Value**

The following code shows how to count the number of rows where x is greater than 10:

sum(df.x > 10) 2

The following code shows how to count the number of rows where x is less than or equal to 7:

sum(df.x 7) 5

**Example 3: Count Rows Between Two Values**

The following code shows how to count the number of rows where x is between 10 and 20:

sum((df.x >= 5) & (df.x 10)) 7

**Additional Resources**

Pandas: How to Find the Difference Between Two Rows

Pandas: How to Drop Rows that Contain a Specific String

Pandas: How to Drop Duplicate Rows in a DataFrame