This article outlines various approaches to finding the row count in a DataFrame when working with the pandas library.
💡 Problem Formulation: Determine the total number of rows in a Pandas DataFrame, a 2-dimensional labeled data structure, to understand the dataset’s size. For example, given a DataFrame df with sales data, find out how many sales records it contains.
Method Overview
First, I’ll give you a quick overview of the methods in this article, then we’ll dive into each next:
import pandas as pd
# Sample data for the DataFrame
data = {
'Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04'],
'Product': ['Apples', 'Bananas', 'Carrots', 'Dates'],
'Quantity Sold': [100, 150, 120, 80]
}
# Creating the DataFrame
df = pd.DataFrame(data)
# Method 1: Using len() Function
row_count_len = len(df)
# Method 2: The shape Attribute
row_count_shape = df.shape[0]
# Method 3: DataFrame.size with Columns Count
row_count_size = df.size // df.shape[1]
# Method 4: Using DataFrame.index
row_count_index = df.index.size
# Method 5: Using DataFrame.count()
row_count_count = df.count().max()
# Method 6: Using len() With DataFrame.axes
row_count_axes = len(df.axes[0])
# Printing the results
print(f"Row count using len(): {row_count_len}")
print(f"Row count using shape: {row_count_shape}")
print(f"Row count using size/shape[1]: {row_count_size}")
print(f"Row count using index.size: {row_count_index}")
print(f"Row count using count().max(): {row_count_count}")
print(f"Row count using len(axes[0]): {row_count_axes}")
⚙️ Output:
Row count using len(): 4
Row count using shape: 4
Row count using size/shape[1]: 4
Row count using index.size: 4
Row count using count().max(): 4
Row count using len(axes[0]): 4Method 1: Using the len() Function
The built-in Python function len() returns the number of rows in a DataFrame. This is one of the simplest and fastest methods to get the row count.
import pandas as pd df = pd.DataFrame(data) row_count = len(df)
In this code snippet data represents your dataset, which is converted into a DataFrame df. By passing df to the len() function, it returns the number of rows in the DataFrame and stores this value in the row_count variable.
Method 2: The shape Attribute
The shape attribute of the DataFrame contains a tuple representing the dimensions (rows, columns) of the DataFrame. The first element of this tuple is the number of rows.
row_count = df.shape[0]
Here, df.shape returns a tuple, and df.shape[0] accesses the first element (number of rows) which is assigned to row_count.
Method 3: DataFrame.size with Columns Count
Another way to find the number of rows is by dividing the total number of elements in the DataFrame (DataFrame.size) by the number of columns (DataFrame.shape[1]).
row_count = df.size // df.shape[1]
The // operator performs integer division, giving us the integer quotient that represents the number of rows which is stored in row_count.
Method 4: Using DataFrame.index
The index of a DataFrame contains information on the row labels. You can get the length of the index to count the number of rows.
row_count = df.index.size
This code obtains the size of the DataFrame’s index, which corresponds to the number of rows, and assigns it to row_count.
Method 5: Using DataFrame.count()
For a more granular count that can exclude NaN values, use the DataFrame.count() method. This method counts the number of non-NA/null entries in each column. If your DataFrame is consistent and has no missing values, you can use this to count rows.
row_count = df.count().max()
The method call df.count() returns a Series with counts of non-NA values per column. By taking the maximum of this Series, we assume data integrity and effectively count the number of rows, excluding rows with NaN entirely.
Bonus One-Liner Method 6: Using len() With DataFrame.axes
For those who prefer a concise approach, combining len() with the DataFrame.axes attribute provides a one-liner solution.
row_count = len(df.axes[0])
The DataFrame.axes[0] represents the DataFrame’s index, and taking its len() gives the number of rows contained in the DataFrame.
👉 How to Get the Last N Rows of a Pandas DataFrame?
Summary/Discussion
The choice of method for counting the number of rows in a DataFrame depends on the context of your task and your data’s integrity.
Simple methods like len() and the shape attribute are typically sufficient for most needs.
The DataFrame.size with columns count can be useful when you need a one-off calculation, whereas using DataFrame.index is more explicit.
If you need to consider the presence of NaN values, DataFrame.count() gives you a more accurate row count excluding any missing data rows.
The bonus one-liner is a quick and easy way for those looking for brevity without calling specific DataFrame attributes directly.
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