# 3 Powerful Ways to Count Rows in Pandas DataFrame

## Counting Rows in a Pandas DataFrame

Pandas is an open-source Python library that is widely used for data manipulation and analysis. One of the most common operations performed on a Pandas DataFrame is counting the number of rows.

Counting rows is a simple but essential operation when working with data, and Pandas provides several ways of doing it. In this article, we will explore how to count rows in a Pandas DataFrame using different approaches.

## Syntax for Counting Rows

Before we dive into the examples, let’s first take a look at the basic syntax for counting rows in a Pandas DataFrame. The following code snippet shows how to count the number of rows in a DataFrame:

“`python

## import pandas as pd

# create a DataFrame

data = {

‘name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’, ‘Emily’],

‘age’: [25, 46, 32, 19, 27],

‘country’: [‘USA’, ‘UK’, ‘Canada’, ‘Australia’, ‘USA’]

}

df = pd.DataFrame(data)

# count rows

count = len(df)

print(f”The DataFrame has {count} rows.”)

“`

The `len()` function returns the number of rows in the DataFrame, and the `print()` function displays the result. Example 1: Count Rows Equal to Some Value

Suppose we want to count the number of rows in a DataFrame where the age is equal to 27.

We can achieve this by using the `.loc[]` method to filter the DataFrame and then count the number of rows using the `len()` function. The following code snippet shows how to do this:

“`python

# count rows where age is equal to 27

count = len(df.loc[df[‘age’] == 27])

print(f”There are {count} rows where the age is equal to 27.”)

“`

The `df.loc[df[‘age’] == 27]` part of the code returns a subset of the DataFrame where the age is equal to 27, and the `len()` function counts the number of rows in the subset.

Example 2: Count Rows Greater or Equal to Some Value

Suppose we want to count the number of rows in a DataFrame where the age is greater than or equal to 25 and less than or equal to 30. We can achieve this by using the `.loc[]` method to filter the DataFrame and then count the number of rows using the `len()` function.

## The following code snippet shows how to do this:

“`python

# count rows where age is between 25 and 30

count = len(df.loc[(df[‘age’] >= 25) & (df[‘age’] <= 30)])

print(f”There are {count} rows where the age is between 25 and 30.”)

“`

The `(df[‘age’] >= 25) & (df[‘age’] <= 30)` part of the code returns a subset of the DataFrame where the age is greater than or equal to 25 and less than or equal to 30, and the `len()` function counts the number of rows in the subset. Example 3: Count Rows Between Two Values

Suppose we want to count the number of rows in a DataFrame where the age is either less than 20 or greater than 40.

We can achieve this by using the `.loc[]` method to filter the DataFrame and then count the number of rows using the `len()` function. The following code snippet shows how to do this:

“`python

# count rows where age is less than 20 or greater than 40

count = len(df.loc[(df[‘age’] < 20) | (df['age'] > 40)])

print(f”There are {count} rows where the age is either less than 20 or greater than 40.”)

“`

The `(df[‘age’] < 20) | (df['age'] > 40)` part of the code returns a subset of the DataFrame where the age is either less than 20 or greater than 40, and the `len()` function counts the number of rows in the subset.

Counting rows in a Pandas DataFrame is a basic but essential operation that you will perform frequently when working with data. In this article, we have covered three different examples of how to count rows using different criteria.

– Pandas documentation: https://pandas.pydata.org/docs/

– Pandas cheat sheet: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

– Pandas tutorial on DataCamp: https://www.datacamp.com/courses/pandas-foundations

## Conclusion

In this article, we have seen how to count rows in a Pandas DataFrame using different criteria. We used the `.loc[]` method to filter the DataFrame based on specific conditions and then counted the number of rows using the `len()` function.

Pandas provides many other ways of counting rows, and we encourage you to explore the Pandas documentation to learn more. Counting rows in a Pandas DataFrame is a fundamental operation that is crucial for data analysis and manipulation.

In this article, we have explored the syntax and different ways to count rows in a Pandas DataFrame, including counting rows that match specific criteria using the `.loc[]` method. We have also highlighted additional resources for learning more about Pandas and working with DataFrames.

Counting rows is essential for gaining insight into a dataset, and it forms the foundation for more complex data analysis tasks. By mastering the techniques covered in this article, you will be on your way to becoming a proficient data analyst.