How to drop NA rows from pandas dataframe?
This recipe helps you drop NA rows from pandas dataframe
Recipe Objective
While operating with pandas, we might be having Nan values in dataframe. So its better to deal with the first.
So this recipe is a short example on how to drop NA rows from pandas dataframe. Let's get started.
Step 1 - Import the library
import pandas as pd
import seaborn as sb
Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays. Seaborn is just for importing dataset for now.
Step 2 - Setup the Data
df = sb.load_dataset('tips')
df.to_csv('tips.csv')
df['tip'][2:7]=None
print(df.head(10))
Here we have simply imported tips dataset from seaborn library. Now we have modified the dataset a bit to introduce 'None' values in out dataset. Finally, we have printed the first 10 rows of our dataset.
Step 3 - Removing nan values
df = df[df['tip'].notna()]
print(df.head(10))
Now we use notna function to find the rows that dont have 'None' value in tip column. Finally, we have printed down our modified dataframe.
Step 4 - Let's look at our dataset now
Once we run the above code snippet, we will see:
Scroll down the ipython file to visualize our dataset.
What Users are saying..
Savvy Sahai
As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More