Here are five ways to remove rows from DataFrame in Python:
- Using drop() method
- Using boolean indexing
- Using the query() method
- Using the iloc[] indexer
- Using the drop() method with the index attribute
Method 1: Using the drop() method
To remove single or multiple rows from a DataFrame in Pandas, you can use the drop() method by specifying the index labels of the rows you want to remove.
import pandas as pd
# Create a sample DataFrame
df = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12]}
df = pd.DataFrame(df)
# Remove row with index label 1
df = df.drop(1)
print("Removing a single row")
print(df)
# Remove rows with index labels 2 and 3
df = df.drop([2, 3])
print("Removing multiple rows")
print(df)Output
Removing a single row
A B C
0 1 5 9
2 3 7 11
3 4 8 12
Removing multiple rows
A B C
0 1 5 9Method 2: Using boolean indexing
You can use boolean indexing to filter out the rows you want to remove based on a condition.
import pandas as pd
# Create a sample DataFrame
df = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12]}
df = pd.DataFrame(df)
# Remove rows where the 'B' column is less than or equal to 6
df = df[df['B'] > 6]
print(df)Output
A B C
2 3 7 11
3 4 8 12Method 3: Using the query() method
You can use the query() method to remove rows based on a query expression.
import pandas as pd
# Create a sample DataFrame
df = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12]}
df = pd.DataFrame(df)
# Remove rows where the 'C' column is greater than than 9
df = df.query('C > 9')
print(df)Output
A B C
1 2 6 10
2 3 7 11
3 4 8 12Method 4: Using the iloc[] indexer
Use the iloc[] indexer to select rows based on their integer locations.
import pandas as pd
# Create a sample DataFrame
df = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12]}
df = pd.DataFrame(df)
# Remove the three rows
df = df.iloc[1:]
print(df)Output
A B C
1 2 6 10
2 3 7 11
3 4 8 12Method 5: Using the drop() method with the index attribute
You can remove rows by their index label(s) with the drop() method combined with the index attribute.
import pandas as pd
# Create a sample DataFrame
df = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12]}
df = pd.DataFrame(df)
# Remove the row with the index label '0'
df = df.drop(df.index[0])
# Remove the rows with index labels '1' and '2'
df = df.drop(df.index[[1, 2]])
print(df)Output
A B C
1 2 6 10That’s it.
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.
