Get the row number of a specific value in a Pandas dataframe can be challenging, especially for beginners. In this article, we will provide you with an easy-to-follow guide on how to get the row number in Pandas, step by step.

By the end of this tutorial, you’ll have learned:

  • How to get the row number(s) for rows matching a condition,
  • How to get only a single row number
  • How to count the number of rows matching a particular condition

Loading a Sample Pandas Dataframe

In this tutorial, we’ll be using Pandas, a popular library for data manipulation and analysis in Python. Pandas provides two main data structures – Series and DataFrame – that allow you to easily manipulate and analyze data.

To begin, let’s import the Pandas library and create our sample DataFrame. You can copy the code below to create the sample DataFrame:

				
					import pandas as pd

# create sample data
data = {'Name': ['John', 'Emma', 'Peter', 'Lisa', 'David'],
        'Age': [28, 24, 32, 27, 29],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
        'Salary': [55000, 48000, 67000, 52000, 60000]}

# create pandas dataframe
df = pd.DataFrame(data)

# print dataframe
print(df)

Output:

				
					#    Name  Age  Gender  Salary
#0   John   28    Male   55000
#1   Emma   24  Female   48000
#2  Peter   32    Male   67000
#3   Lisa   27  Female   52000
#4  David   29    Male   60000

In this example, the dataframe contains information about employees, including their names, ages, genders, and salaries.

Get Row Numbers that Match a Condition in a Pandas Dataframe

To return the row numbers for rows matching multiple conditions in a Pandas DataFrame, you can use the loc function in combination with the & (and) and | (or) operators.

For example, let’s say we want to get the row numbers where the Age column is greater than or equal to 30 AND the Gender column is ‘M’. Here’s how you can do it:

				
					import pandas as pd

# create sample data
data = {'Name': ['John', 'Emma', 'Peter', 'Lisa', 'David'],
        'Age': [28, 24, 32, 27, 29],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
        'Salary': [55000, 48000, 67000, 52000, 60000]}
df = pd.DataFrame(data)

# Using loc function to get row numbers where Age is greater than or equal to 30
row_nums = df.loc[df['Age'] >= 30].index.tolist()

print(row_nums)

Output:

				
					# [2]

Here’s a breakdown of what’s happening in the code:

  • We use the loc function to locate the rows where the Age column is greater than or equal to 30.
  • We then use the index attribute to get the index of the rows that match the condition.
  • Finally, we convert the index to a list using the tolist() method and store it in the row_nums variable.

You can modify the condition to match your specific use case. Additionally, you can use other Pandas functions and methods to further manipulate and analyze the data in your DataFrame.

Get Row Numbers that Match Multiple Condition in a Pandas Dataframe

To get the first row number that matches a condition in a Pandas DataFrame, you can use the idxmax() method in combination with the loc function.

For example, let’s say we want to get the first row number where the Age column is greater than or equal to 30. Here’s how you can do it:

				
					import pandas as pd

# create sample data
data = {'Name': ['John', 'Emma', 'Peter', 'Lisa', 'David'],
        'Age': [28, 24, 32, 27, 29],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
        'Salary': [55000, 48000, 67000, 52000, 60000]}
df = pd.DataFrame(data)

# Get row numbers where Age is greater than or equal to 30 AND Gender is 'Male'
row_nums = df.loc[(df['Age'] >= 30) & (df['Gender'] == 'Male')].index.tolist()

print(row_nums)

Output:

				
					# [2]

Here’s a breakdown of what’s happening in the code:

  • First, we import the Pandas library and create the sample DataFrame.
  • Next, we use the loc function to select the rows where the Age column is greater than or equal to 30 AND the Gender column is ‘Male’.
  • We use the & (and) operator to combine the two conditions.
  • Then, we use the index attribute to get the index labels of the selected rows.
  • Finally, we use the tolist() method to convert the index labels to a list of row numbers.

You can modify the conditions in the loc function to match your specific use case. Additionally, you can use other operators such as | (or) and ~ (not) to combine and negate conditions.

Get the First Row Number that Matches a Condition in a Pandas Dataframe

To drop missing data in a Pandas DataFrame, you can use the dropna() method. The dropna() method removes any row or column that contains missing data (i.e., NaN values).

Here’s an example of how to drop missing data in a Pandas DataFrame:

				
					import pandas as pd

# create sample data
data = {'Name': ['John', 'Emma', 'Peter', 'Lisa', 'David'],
        'Age': [28, 24, 32, 27, 29],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
        'Salary': [55000, 48000, 67000, 52000, 60000]}
df = pd.DataFrame(data)

# Get first row number where Age is greater than or equal to 30
row_num = df.loc[df['Age'] >= 30].index[0]

print(row_num)

Output:

				
					# 2

Here’s a breakdown of what’s happening in the code:

  • First, we import the Pandas library and create the sample DataFrame.
  • Next, we use the loc function to select the rows where the Age column is greater than or equal to 30.
  • Then, we use the idxmax() method to get the index label of the first row that matches the condition.
  • Finally, we use the [0] index to get the row number from the index label.

You can modify the condition in the loc function to match your specific use case. Additionally, if there are no rows that match the condition, the above code will raise a IndexError. To handle this, you can add an if statement to check if the selected rows are not empty before accessing the first row number.

Count the Number of Rows Matching a Condition

To count the number of rows matching a condition in a Pandas DataFrame, you can use the sum() method in combination with a Boolean condition.

For example, let’s say we want to count the number of rows where the Age column is greater than or equal to 30. Here’s how you can do it:

				
					import pandas as pd

# create sample data
data = {'Name': ['John', 'Emma', 'Peter', 'Lisa', 'David'],
        'Age': [28, 24, 32, 27, 29],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
        'Salary': [55000, 48000, 67000, 52000, 60000]}
df = pd.DataFrame(data)

# Count number of rows where Age is greater than or equal to 30
count = (df['Age'] >= 30).sum()

print(count)

Output:

				
					# 1

Here’s a breakdown of what’s happening in the code:

  • First, we import the Pandas library and create the sample DataFrame.
  • Next, we use a Boolean condition df['Age'] >= 30 to select the rows where the Age column is greater than or equal to 30. This creates a Boolean mask that is True for rows matching the condition and False for rows that don’t.
  • Then, we use the sum() method on the Boolean mask to count the number of True values. This gives us the number of rows that match the condition.

Wrap up

You learned in this tutorial how to use Pandas to retrieve the row numbers of a Pandas Dataframe that match a specified condition. You’ve also learned how to obtain the row numbers of rows that satisfy multiple conditions. You have finally learned how to use Pandas to count the number of rows that satisfy a given condition.

Here you find Pandas’ Official Documentation
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pop.html


Thanks for reading. Happy coding!