Remove rows from pandas DataFrame based on condition

Question

I am a newbie to pandas so please forgive the newbie question!

I have the following code;

import pandas as pd

pet_names = ["Name","Species"
"Jack","Cat"
"Jill","Dog"
"Tom","Cat"
"Harry","Dog"
"Hannah","Dog"]

df = pd.DataFrame(pet_names)

df = df[df['Species']!='Cat']

print(df)

I would like to remove all the rows that contain "Cat" in the "Species" column, leaving all the dogs behind. How do I do this? Unfortunately, this code is currently returning errors.

Answer 1

General boolean indexing

df[df['Species'] != 'Cat']
# df[df['Species'].ne('Cat')]

  Index    Name Species
1     1    Jill     Dog
3     3   Harry     Dog
4     4  Hannah     Dog

---

df.query

df.query("Species != 'Cat'")

  Index    Name Species
1     1    Jill     Dog
3     3   Harry     Dog
4     4  Hannah     Dog

For information on the pd.eval() family of functions, their features and use cases, please visit Dynamic Expression Evaluation in pandas using pd.eval().

---

df.isin

df[~df['Species'].isin(['Cat'])]

  Index    Name Species
1     1    Jill     Dog
3     3   Harry     Dog
4     4  Hannah     Dog

Answer 2

Your code df[df['Species']!='Cat'] is correct. It's your dataframe initialization code that is wrong. See the other comment by user cs95.

While the other answer is correct, I prefer to use drop() when deleting rows because it is more straightforward than using inverse logic (keeping rows that are not Cats). There's no difference for a simple example like this, but if you starting having more complex logic for which rows to drop, then it matters. For example, delete rows where A=1 AND (B=2 OR C=3).

Here's how you use drop() with conditional logic:

df.drop( df.query(" `Species`=='Cat' ").index)

This is a more scalable syntax for more complicated logic.