There are three methods you can use to quickly count the number of rows in a pandas DataFrame:

#count number of rows in index column of data frame len(df.index) #find length of data frame len(df) #find number of rows in data frame df.shape[0]

Each method will return the exact same answer.

For small datasets, the difference in speed between these three methods is negligible.

For extremely large datasets, it’s recommended to use **len(df.index)** since this has been shown to be the fastest method.

The following example shows how to use each of these methods in practice.

**Example: Count Number of Rows in Pandas DataFrame**

The following code shows how to use the three methods mentioned earlier to count the number of rows in a pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'y': [8, 12, 15, 14, 19, 23, 25, 29, 31, 30, 31, 31], 'x1': [5, 7, 7, 9, 12, 9, 9, 4, 5, 4, 7, 7], 'x2': [11, 8, 10, 6, 6, 5, 9, 12, 8, 8, 9, 9], 'x3': [2, 2, 3, 2, 5, 5, 7, 9, 11, 7, 7, 8]}) #view DataFrame df y x1 x2 x3 0 8 5 11 2 1 12 7 8 2 2 15 7 10 3 3 14 9 6 2 4 19 12 6 5 5 23 9 5 5 6 25 9 9 7 7 29 4 12 9 8 31 5 8 11 9 30 4 8 7 10 31 7 9 7 11 31 7 9 8 #count number of rows in index column of data frame len(df.index) 12 #find length of data frame len(df) 12 #find number of rows in data frame df.shape[0] 12

Notice that each method returns the exact same result. The DataFrame has **12** rows.

**Additional Resources**

How to Count Observations by Group in Pandas

How to Perform a COUNTIF Function in Pandas

How to Count Missing Values in a Pandas DataFrame