Several use cases exist for the Python Pandas module when it comes to skipping numerous rows. Here are a few illustrations:
- Cleaning up the data: If your data file has extra rows, such as a header or footer, you can skip those rows and only read the necessary information.
- Data subsetting: The iloc property or drop method can be used to pick or drop the rows you don't require if you only want to work with a particular subset of your data.
- Data exploration: To quickly examine the data when working with huge datasets without having to load the complete dataset into memory, it will help to skip a predetermined number of rows.
- Data pre-processing: In some cases, we have to pre-process the data before analyzing it; for example, we have to skip the rows with missing values or the rows that have non-valid entries.
- Data manipulation: You may want to remove or keep only certain rows based on certain criteria, such as only keeping rows where a certain column has a specific value.
Here is how you can skip multiple rows using Pandas:
Preview of the output that you will get on running this code from your IDE.
In this solution we used pandas library of python.
ro = list(range(0, lengthOfFile + 10, 10)) d = [j + 1 for i in range(1, len(ro), 2) for j in range(ro[i], ro[i + 1])] # print(ro) print(d) pd.read_csv('../input/train.csv',chunksize=10, dtype=dtypes,skiprows=d) lengthOfFile = 100 ro = list(range(0, lengthOfFile + 10, 10)) d = [j for i in range(1, len(ro), 2) for j in range(ro[i], ro[i + 1])] print(d)
Follow the steps carefully to get the output easily.
- Download and Install the PyCharm Community Edition on your computer.
- Install pandas on your IDE from python interpreter in setting options.
- Create new python file on your IDE.
- Copy the snippet using the 'copy' button and paste it in your python file.
- import the pandas library.
- Run the current 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 ' Skip multiple rows using pandas' in kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2022.3.
- The solution is tested on Python 3.11.1
- Pandas version-1.5.2.
Using this solution, we are able to Skip multiple rows using pandas 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 Skip multiple rows using pandas.
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)