5 Easy Ways to Get Pandas DataFrame Row Count

Pandas is a popular open-source data manipulation and analysis library in Python. It offers a wide range of functionalities to work with structured and tabular data, including the DataFrame class, which is a two-dimensional table-like data structure that stores data in rows and columns. One of the most common operations performed on a DataFrame is to get the number of rows it contains. In this blog post, we will explore all the ways to get the row count of a Pandas DataFrame and discuss their advantages and disadvantages. You can also use Pandas with more computing power for free on Saturn Cloud.

Method 1: Using len() function

The simplest and most straightforward way to get the row count of a Pandas DataFrame is to use the built-in len() function, which returns the length of an object. Since a DataFrame is a collection of rows, len() function returns the number of rows in the DataFrame.

Example:

import pandas as pd

df = pd.read_csv('data.csv')
row_count = len(df)

print(f'The DataFrame has {row_count} rows.')

Output:

The DataFrame has 1000 rows.

Advantages:

This method is easy to use and requires only one line of code. It works for all types of DataFrames, including empty ones.

Disadvantages:

It may not be the most efficient method for large DataFrames since it creates a temporary list of all the rows.

Method 2: Using shape attribute

Another way to get the row count of a Pandas DataFrame is to use the shape attribute, which returns a tuple of the number of rows and columns in the DataFrame. We can extract the row count by accessing the first element of the tuple.

Example:

import pandas as pd

df = pd.read_csv('data.csv')
row_count = df.shape[0]

print(f'The DataFrame has {row_count} rows.')

Output:

The DataFrame has 1000 rows.

Advantages:

This method is efficient and does not create any temporary objects. It works for all types of DataFrames, including empty ones.

Disadvantages:

It requires two lines of code to extract the row count. It may not be the most intuitive method for beginners.

Method 3: Using index attribute

The index attribute of a Pandas DataFrame contains the row labels of the DataFrame. We can get the row count by getting the length of the index attribute.

Example:

import pandas as pd

df = pd.read_csv('data.csv')
row_count = len(df.index)

print(f'The DataFrame has {row_count} rows.')

Output:

The DataFrame has 1000 rows.

Advantages:

This method is efficient and does not create any temporary objects. It works for all types of DataFrames, including empty ones.

Disadvantages:

It requires two lines of code to extract the row count. It may not be the most intuitive method for beginners.

Method 4: Using count() function

The count() function of a Pandas DataFrame returns the number of non-null values in each column. We can get the row count by selecting any column and getting its count.

Example:

import pandas as pd

df = pd.read_csv('data.csv')
row_count = df['column_name'].count()

print(f'The DataFrame has {row_count} rows.')

Output:

The DataFrame has 1000 rows.

Advantages:

This method is efficient and does not create any temporary objects. It works for all types of DataFrames, including empty ones.

Disadvantages:

It requires the selection of a column that has no missing values. It may not be the most intuitive method for beginners.

Method 5: Using info() function

The info() function of a Pandas DataFrame provides a summary of the DataFrame, including the number of non-null values in each column and the memory usage. We can extract the row count from the summary.

Example:

import pandas as pd

df = pd.read_csv('data.csv')
df.info()

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 5 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   column1     1000 non-null   float64
 1   column2     1000 non-null   int64  
 2   column3     1000 non-null   object 
 3   column4     1000 non-null   bool   
 4   column5     1000 non-null   object 
dtypes: bool(1), float64(1), int64(1), object(2)
memory usage: 32.4+ KB

In this example, we can see that the DataFrame has 1000 entries, which corresponds to the number of rows.

Advantages:

This method provides additional information about the DataFrame, such as the data types and memory usage. It works for all types of DataFrames, including empty ones.

Disadvantages:

It requires the printing of the summary, which may not be desirable in some cases. It may not be the most efficient method for large DataFrames.

Conclusion:

In this blog post, we have explored all the ways to get the row count of a Pandas DataFrame and discussed their advantages and disadvantages. Depending on the specific use case, some methods may be more suitable than others. For small DataFrames or one-time operations, the len() function or the shape attribute may be the most convenient. For larger DataFrames or more complex operations, the count() function or the info() function may be more appropriate. Regardless of the method chosen, it is important to have a clear understanding of how to manipulate and analyze data using Pandas, as it is a powerful tool for data science and machine learning applications.