How to Remove the Different Rows In Pandas?

7 minutes read

To remove different rows in pandas, you can use various methods. One way is to filter the DataFrame using boolean indexing based on specific conditions. For example, you can drop rows that meet certain criteria by using the drop method with a condition that filters out those rows. Another approach is to use the drop_duplicates method to remove duplicate rows from the DataFrame. Additionally, you can drop rows by their index labels using the drop method with the index parameter specified. These are some of the ways to remove different rows in pandas based on your specific requirements and criteria.

Best Python Books to Read in November 2024

1
Fluent Python: Clear, Concise, and Effective Programming

Rating is 5 out of 5

Fluent Python: Clear, Concise, and Effective Programming

2
Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Rating is 4.9 out of 5

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

3
Learning Python: Powerful Object-Oriented Programming

Rating is 4.8 out of 5

Learning Python: Powerful Object-Oriented Programming

4
Python Practice Makes a Master: 120 ‘Real World’ Python Exercises with more than 220 Concepts Explained (Mastering Python Programming from Scratch)

Rating is 4.7 out of 5

Python Practice Makes a Master: 120 ‘Real World’ Python Exercises with more than 220 Concepts Explained (Mastering Python Programming from Scratch)

5
Python Programming for Beginners: The Complete Python Coding Crash Course - Boost Your Growth with an Innovative Ultra-Fast Learning Framework and Exclusive Hands-On Interactive Exercises & Projects

Rating is 4.6 out of 5

Python Programming for Beginners: The Complete Python Coding Crash Course - Boost Your Growth with an Innovative Ultra-Fast Learning Framework and Exclusive Hands-On Interactive Exercises & Projects

6
The Big Book of Small Python Projects: 81 Easy Practice Programs

Rating is 4.5 out of 5

The Big Book of Small Python Projects: 81 Easy Practice Programs

7
Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming

Rating is 4.4 out of 5

Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming

8
Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners

Rating is 4.3 out of 5

Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners


How to drop the first row in pandas?

You can drop the first row in a pandas DataFrame by using the drop method with the index of the row you want to drop. Here is an example:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

# Drop the first row
df = df.drop(df.index[0])

print(df)


This will remove the first row from the DataFrame df and print the updated DataFrame without the first row.


How to drop rows based on index in pandas?

To drop rows based on index in pandas, you can use the drop method specifying the index label (or labels) that you want to drop.


Here is an example:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3, 4],
        'B': ['a', 'b', 'c', 'd']}
df = pd.DataFrame(data)

# Drop rows based on index label
df.drop([1, 3], inplace=True)

# Output the DataFrame after dropping rows
print(df)


In this example, rows with index labels 1 and 3 are dropped from the DataFrame df. The inplace=True parameter is used to modify the original DataFrame.


How to drop rows based on conditions in multiple columns in pandas?

To drop rows based on conditions in multiple columns in pandas, you can use boolean indexing.


Here's an example code to drop rows where a condition is met in multiple columns:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
import pandas as pd

# Create a sample dataframe
data = {'A': [1, 2, 3, 4], 'B': [10, 20, 30, 40], 'C': [100, 200, 300, 400]}
df = pd.DataFrame(data)

# Drop rows where values in column 'A' and column 'B' are greater than 2 and 20 respectively
df = df[~((df['A'] > 2) & (df['B'] > 20))]

print(df)


In this example, the ~ operator is used to negate the condition. The resulting dataframe will have rows dropped where both conditions are met in columns 'A' and 'B'. You can modify the conditions and columns according to your specific requirements.

Facebook Twitter LinkedIn Telegram Whatsapp Pocket

Related Posts:

To create heatmaps for different rows in pandas, you can use the seaborn library in conjunction with pandas. First, you need to import both libraries. Then, you can select the rows you want to visualize from your pandas DataFrame and pass them to the seaborn h...
To transform a JSON file into multiple dataframes with pandas, you can use the pd.read_json() function to load the JSON file into a pandas dataframe. Once the data is loaded, you can then manipulate and extract different parts of the data into separate datafra...
To color rows in Excel using Pandas, you can first create a Pandas DataFrame with the data you want to display. Then, you can use the Styler object in Pandas to apply custom formatting to the DataFrame. By specifying a conditional formatting rule based on the ...
To append columns as additional rows in Pandas, you can use the pd.melt() function. This function allows you to reshape your data frame by converting columns into rows. By specifying the id_vars parameter as the primary key columns and value_vars parameter as ...
In pandas, you can check the start and end rows of a dataframe using the head() and tail() functions. The head() function returns the first n rows of the dataframe, where n is the number of rows you specify as an argument (default is 5). This allows you to see...
To sum rows containing specific targets in pandas, you can use the filter method along with the sum method. First, create a filter that checks for the specific targets in each row using boolean indexing. Then, apply the filter to the DataFrame and use the sum ...