The size of the dataframe is a very important factor to determine the kind of manipulations and processes that can be applied to it. For example, if you have limited resources and working with large datasets, it is important to use processes that are not compute-heavy. In this tutorial, we’ll look at how to quickly get the number of rows in a pandas dataframe.

## How to get the number of rows in a dataframe?

There are a number of ways to get the number of rows of a pandas dataframe. You can determine it using the shape of the dataframe. Or, you can use the `len()`

function. Let’s look at each of these methods with the help of an example.

First, we’ll load the rain in Australia dataset as a pandas dataframe from a locally saved CSV file.

import pandas as pd # read the dataset df = pd.read_csv("weatherAUS.csv") # display the dataframe head df.head()

Output:

You can see that the data has a number of features. Let’s go through some the methods that you can use to determine the number of rows in the dataframe.

### 1. Using `.shape`

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The `.shape`

property gives you the shape of the dataframe in form of a `(rows, column)`

tuple. That is, the first element of the tuple gives you the row count of the dataframe. Let’s get the shape of the above dataframe:

# number of rows using .shape[0] print(df.shape) print(df.shape[0])

Output:

(145460, 23) 145460

You can see that `df.shape`

gives the tuple (145460, 23) denoting that the dataframe df has 145460 rows and 23 columns. If you specifically want just the number of rows, use `df.shape[0]`

### 2. Using the `len()`

function

You can also use the built-in python `len()`

function to determine the number of rows. This function is used to get the length of iterable objects. Let’s use this function to get the length of the above dataframe.

# number of rows using len() print(len(df))

Output:

145460

We get 145460 as the length which is equal to the number of rows in the dataframe.

Note that both of the above methods, `.shape[0]`

or `len()`

are constant time operations and are thus pretty fast. Both involve a lookup operation and there isn’t much difference between their execution speeds so you can use either of the methods that you’re comfortable with.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

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