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Iterating Over Rows in Pandas to Check the Condition

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by Abdul Rawoof A R dot icon Updated: Mar 1, 2023

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In Pandas, iteration is nothing but a general term for taking each item of something one after another. This Pandas Dataframe consists of rows and columns, and we have to iterate a dataframe like a dictionary. 


The basic concept of iteration is the repetition of a process to generate a possibly unbounded sequence of outcomes or results; each repetition of the process is a single iteration. The outcome or result of every iteration is the starting point of the following iteration. To iterate through a row in a column, we can use the iloc() method to iterate rows. This method is used to access the rows and columns of the DataFrame by using their integer value locations in the DataFrame. Therefore, by specifying the integer value of the row and column index, we can iterate over the rows of the pandas DataFrame. 


Iterate over rows in a Pandas DataFrame: 

  • There are different ways to iterate over rows of a DataFrame or Series in Pandas, each with pros and cons. 
  • Iterating over rows with the iterrows() method is the most straightforward. 
  • Returns a row index and the row itself using the iterrows() method. 


Here is an example of how to over rows in Pandas to check the condition: 

Fig : Preview of the output that you will get on running this code from your IDE.

Code

In this solution we're using Pandas library.

Iterating over rows in pandas to check the condition

PythondotLines of Code : 21dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
df = pd.DataFrame({'A': [1234, 6267, 6364, 573, 0, 838, 92, 3221]})

idx = next((i for i, j in enumerate(df['A']) if j == 0), len(df['A']))

df['B'] = ~(df.index < idx)

# more verbose alternative:
# df['B'] = np.where(df.index < idx, False, True)

print(df)

      A      B
0  1234  False
1  6267  False
2  6364  False
3   573  False
4     0   True
5   838   True
6    92   True
7  3221   True

Instructions

Follow the steps carefully to get the output easily.

  1. Install pandas on your IDE(Any of your favorite IDE).
  2. Copy the snippet using the 'copy' and paste it in your IDE.
  3. Add required dependencies and import them in Python file.
  4. Run the file to generate the output.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for 'how to Iterate over rows in pandas to check the condition' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2021.3.
  2. The solution is tested on Python 3.9.7.
  3. Pandas version-v1.5.2.


Using this solution, we are able to iterate over rows in pandas to check the condition with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to iterate over rows in pandas to check the condition.

Dependent Library

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support
    Quality
      Security
        License
          Reuse

            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      You can also search for any dependent libraries on kandi like 'pandas'.

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