Answer: You can use the df.shape attribute to count the number of rows in the pandas dataframe.
Counting rows of a pandas dataframe is an important operation in Python. You can use various pre-defined methods like count(), and len() functions to count the rows in a dataframe. In this blog, let’s explore more about these methods with the help of examples.
Table of Contents:
Before we get started with the methods, let’s create a set of DataFrame with some values inserted to make it easier to count the number of rows in a set of DataFrame.
Example:
Output:
Now, we’ll use this data to count the number of rows using various pre-defined methods in Python:
Methods to Count the Number of Rows in Pandas Dataframe
Method 1: Using df.shape Attribute to Count Rows in Python
The df.shape attribute in Pandas is used to get the dimensions of a DataFrame. It returns a tuple.
Example:
Output:
Method 2: Using len( ) Function to Count Rows in Python
The len( ) function in Python is used to get the length of an object. When applied to a pandas dataframe, len( ) returns the number of rows in the dataframe.
Example:
Output:
len( ) function is a quick way to find how many rows are in a dataframe.
Method 3: Using the .count( ) Method to Get the Row Count in Python
The .count( ) method in Pandas counts the non-null values in each column or row of a dataframe.
Output:
df.count() method ignores any NaN (null) values and counts the non-null values in each row. If a column or row has missing (null/NaN) values, .count() will exclude those missing values from the count.
Method 4: Using Axes Attribute in Python
The axes attribute in Pandas is used to return the list of row and column labels (indices) of a DataFrame. It provides a list where it counts the row based on the axes like axes[0], and axes[1].
Example:
Output:
df.axes returns a list of two elements:
- Axes[0]: Row labels that indicate the index of the dataframe.
- Axes[1]: Column labels that indicate the columns of the dataframe.
The axes attribute is useful when you want to directly access the row and column labels or indices, especially when working with more complex dataframe tasks.
Method 5: Using index Attribute to Get Row Labels in Python
The index attribute in Pandas gives you the row labels of a DataFrame. By default, it’s a simple range, but you can also set custom labels for the rows.
Example:
Output:
Explanation: ‘df.index’ gives the index of the DataFrame. The range could be from index 0 and so on.
It returns the row labels, which can be helpful when performing operations based on the rows.
Conclusion
By now, you have learned about the built-in methods of Python that are used to get the number of rows in a pandas dataframe. You have learned the usage of these methods with the help of examples based on a pre-defined dataframe.
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