how to Drop Rows in Pandas DataFrame Based on Condition

---

We can use the following syntax to drop rows in a pandas DataFrame based on condition:

Method 1: Drop Rows Based on One Condition

df = df[df.col1 > 8]

Method 2: Drop Rows Based on Multiple Conditions

df = df[(df.col1 > 8) & (df.col2 != 'A')]

Note: We can also use the drop() function to drop rows from a DataFrame, but this function has been shown to be much slower than just assigning the DataFrame to a filtered version of itself.

The following examples show how to use this syntax in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'pos': ['G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'],
                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
df

	team	pos	assists	rebounds
0	A	G	5	11
1	A	G	7	8
2	A	F	7	10
3	A	F	9	6
4	B	G	12	6
5	B	G	9	5
6	B	F	9	9
7	B	F	4	12

Method 1: Drop Rows Based on One Condition

The following code shows how to drop rows in the DataFrame based on one condition:

#drop rows where value in 'assists' column is less than or equal to 8
df = df[df.assists > 8] 

#view updated DataFrame
df

	team	pos	assists	rebounds
3	A	F	9	6
4	B	G	12	6
5	B	G	9	5
6	B	F	9	9

Any row that had a value less than or equal to 8 in the ‘assists’ column was dropped from the DataFrame.

Method 2: Drop Rows Based on Multiple Conditions

The following code shows how to drop rows in the DataFrame based on multiple conditions:

#only keep rows where 'assists' is greater than 8 and rebounds is greater than 5
df = df[(df.assists > 8) & (df.rebounds > 5)]

#view updated DataFrame
df

	team	pos	assists	rebounds
3	A	F	9	6
4	B	G	12	6
5	B	G	9	5
6	B	F	9	9

The only rows that we kept in the DataFrame were the ones where the assists value was greater than 8 and the rebounds value was greater than 5.

Note that we can also use the | operator to apply an “or” filter:

#only keep rows where 'assists' is greater than 8 or rebounds is greater than 10
df = df[(df.assists > 8) | (df.rebounds > 10)]

#view updated DataFrame
df

	team	pos	assists	rebounds
0	A	G	5	11
3	A	F	9	6
4	B	G	12	6
5	B	G	9	5
6	B	F	9	9
7	B	F	4	12

The only rows that we kept in the DataFrame were the ones where the assists value was greater than 8 or the rebounds value was greater than 10.

Any rows that didn’t meet one of these conditions was dropped.