# Master Data Manipulation in Pandas with These Simple Methods

Dropping rows based on condition in Pandas DataFrame has become a common task for many data analysis and manipulation projects. With the help of Pandas, a popular data manipulation library in Python, users can easily filter and remove rows based on certain conditions.

This article discusses two methods for dropping rows based on conditions and provides an example DataFrame to illustrate the concepts. Method 1: Drop Rows Based on One Condition

The first method for dropping rows based on condition involves dropping rows that meet one specified condition.

In Pandas, users can use the `drop` function to remove rows based on a single condition.

To illustrate this method, consider the following example DataFrame:

“`

## import pandas as pd

df = pd.DataFrame({

‘name’: [‘John’, ‘Bob’, ‘Alice’, ‘Sam’],

‘age’: [25, 30, 28, 22],

‘gender’: [‘M’, ‘M’, ‘F’, ‘M’]

})

## df)

“`

This will create and display the following table:

“`

name age gender

## 3 Sam 22 M

“`

Suppose the user wants to drop rows where the age of the individual is less than 25. To accomplish this, they can use the `loc` function, as follows:

“`

df.drop(

df.loc[

df[‘age’] < 25].index, inplace=True)

## df)

“`

The output will be the following table:

“`

name age gender

## 2 Alice 28 F

“`

In this method, `

df[‘age’] < 25` filters the DataFrame based on the condition of age being less than 25. `

df.loc[

df[‘age’] < 25].index` returns the index of all the rows where the condition is true, and `

df.drop()` removes the rows by index.

Finally, `inplace=True` updates the DataFrame with the new result after removing the rows. Method 2: Drop Rows Based on Multiple Conditions

The second method for dropping rows based on condition involves dropping rows that meet multiple specified conditions.

In this method, users can drop rows based on multiple conditions using the `&` operator for AND and the `|` operator for OR. To illustrate this method, consider a different example DataFrame:

“`

## import pandas as pd

df = pd.DataFrame({

‘name’: [‘John’, ‘Bob’, ‘Alice’, ‘Sam’],

‘age’: [25, 30, 28, 22],

‘gender’: [‘M’, ‘M’, ‘F’, ‘M’],

‘city’: [‘New York’, ‘Los Angeles’, ‘New York’, ‘Chicago’]

})

“`

## This will create and display the following table:

“`

name age gender city

## 3 Sam 22 M Chicago

“`

Suppose the user wants to remove all rows where the individual’s age is less than or equal to 25 and they live in New York City. One way to achieve this is to use the `query` function with logical operators (`&` and `|`), as follows:

“`

df =

df.query(‘age > 25 | city != “New York”‘)

“`

## The output will be the following table:

“`

name age gender city

## 1 Bob 30 M Los Angeles

“`

This method uses `query` to filter based on age being greater than 25 using `age > 25` and city not equal to New York using `city != “New York”`. The result is a new DataFrame with the required conditions.

## Example DataFrame

When analyzing data in Pandas, the first step is often to read in the data. In this example, the user will generate a small sample dataset using Pandas’ `DataFrame` function:

“`

## import pandas as pd

data = {

‘name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’, ‘Emily’],

‘score’: [63, 79, 92, 85, 95],

‘gender’: [‘F’, ‘M’, ‘M’, ‘M’, ‘F’],

‘major’: [‘Biology’, ‘Mathematics’, ‘Computer Science’, ‘History’, ‘English’],

‘grad_year’: [2019, 2020, 2021, 2022, 2021]

}

df = pd.DataFrame(data)

“`

“`

“`

## Viewing the DataFrame

After creating a DataFrame, it is essential to view the data to ensure it was properly read in and to get a sense of the data’s structure. There are several ways to view a Pandas DataFrame.

In Jupyter Notebook, users can call the DataFrame by itself in a cell to display the entire DataFrame, as follows:

“`

“`

“`

## 4 Emily 95 F English 2021

“`

Users can also use the `head` function to view the first few rows of the DataFrame:

“`

“`

“`

## 4 Emily 95 F English 2021

“`

Using `tail` is a similar function that displays the last few rows:

“`

df.tail()

“`

“`

“`

## Conclusion

In conclusion, there are two methods for dropping rows based on conditions in Pandas: dropping rows based on one condition and based on multiple conditions. These methods provide powerful filtering and data manipulation capabilities that users can use to trim their datasets and pursue exploratory data analysis.

Additionally, it is important to properly view the loaded DataFrame to get a sense of the data’s contents. By following these practices, users can effectively wrangle and analyze their data.

In the previous article, we discussed two methods for dropping rows based on conditions in Pandas DataFrame. In this article, we will further expand on these methods by providing syntax and example code for each method.

Method 1: Drop Rows Based on One Condition

To drop rows based on a single condition, users can use the `drop` function in combination with the `loc` operator. Syntax for Dropping Rows Based on One Condition:

“`

df.drop(

df.loc[

df[‘column_name’] condition].index, inplace=True)

“`

Here, `

df` represents the DataFrame, and `column_name` is the name of the column where the condition will be evaluated. `condition` is the condition that the column values must meet for the rows to be dropped.

“`

## import pandas as pd

df = pd.DataFrame({

‘name’: [‘John’, ‘Bob’, ‘Alice’, ‘Sam’],

‘age’: [25, 30, 28, 22],

‘gender’: [‘M’, ‘M’, ‘F’, ‘M’]

})

df.drop(

df.loc[

df[‘age’] < 25].index, inplace=True)

## df)

“`

In this code, the `age` column is evaluated for rows where the age is less than 25. The `loc` operator filters the DataFrame based on this condition, and the resulting indices are passed to the `drop` function for removal.

Finally, `inplace=true` is used to update the DataFrame with the new result after removing the rows. Method 2: Drop Rows Based on Multiple Conditions

To drop rows based on multiple conditions, users can use the `query` function with logical operators (`&` and `|`).

## Syntax for Dropping Rows Based on Multiple Conditions:

“`

df =

df.query(‘condition1 operator condition2’)

“`

Here, `

df` represents the DataFrame, `condition1` and `condition2` are conditions evaluated for columns in the DataFrame, and `operator` is either the AND operator (`&`) or the OR operator (`|`). Example Code for Dropping Rows Based on Multiple Conditions:

“`

## import pandas as pd

df = pd.DataFrame({

‘name’: [‘John’, ‘Bob’, ‘Alice’, ‘Sam’],

‘age’: [25, 30, 28, 22],

‘gender’: [‘M’, ‘M’, ‘F’, ‘M’],

‘city’: [‘New York’, ‘Los Angeles’, ‘New York’, ‘Chicago’]

})

df =

df.query(‘age > 25 | city != “New York”‘)

## df)

“`

In this code, we want to drop rows where the age is less than or equal to 25 and where the individual lives in New York. The `query` function is used to filter the DataFrame based on two conditions: age greater than 25 using `age > 25` and the city not equal to New York using `city != “New York”`.

The output of this code is a new DataFrame with the desired conditions.

## Conclusion:

In summary, when filtering data in Pandas, users can drop rows based on conditions using two methods: dropping rows based on one condition and dropping rows based on multiple conditions. We have provided syntax and example code for each method, which will help users to quickly apply these techniques to their data.

By utilizing these techniques, users can effectively manipulate and analyze their data in a simple and efficient manner. In this article, we discussed two methods for dropping rows based on conditions in Pandas DataFrame, along with syntax and example code for each method.

In this addition, we will provide additional resources on working with Pandas DataFrames. Pandas is a popular data manipulation library in Python that provides powerful tools for data analysis.

With Pandas, users can easily read, manipulate, and analyze data using various functions and methods. Here are some additional resources that can help users learn more about working with Pandas DataFrames.

1. Pandas Documentation

The official documentation for Pandas is a great resource for users who are looking to get started with the library.

The docs provide in-depth explanations of various functions and methods, along with code examples that demonstrate how to use them. The documentation is well-organized and easy to navigate, making it a great reference for users at all levels.

2. “Python for Data Analysis” by Wes McKinney

“Python for Data Analysis” is a popular book authored by Wes McKinney, the creator of Pandas.

The book provides a comprehensive guide to working with data in Python using Pandas and is a great resource for users who are new to the library. The book covers a broad range of topics, including data cleaning, visualization, and machine learning, making it a great resource for users who want to learn more about working with data in Python.

3. Kaggle Tutorials

Kaggle is a popular online platform that provides tools for data science and machine learning.

The platform offers a wide range of tutorials and resources on working with Pandas DataFrames, as well as other common data science tools and technologies. Kaggle’s tutorials are well-designed and provide hands-on experience working with Pandas, making it a great resource for users who want to gain practical experience with the library.

4. DataCamp

DataCamp is an online learning platform that provides courses on various topics in data science, including working with Pandas DataFrames.

The platform offers both free and paid courses, with hands-on exercises and projects that help users develop their data analysis skills. DataCamp’s courses are well-structured and provide a structured learning experience, making it a great resource for users who want a more guided approach to learning Pandas.

5. Stack Overflow

Stack Overflow is a popular community-driven Q&A website that provides answers to a wide range of questions on various topics, including Pandas DataFrames.

Users can search for questions related to their specific needs or ask their own questions, and the community provides answers and solutions. Stack Overflow is a great resource for users who are stuck on a specific problem and need help from the community.

In conclusion, working with Pandas DataFrames can be an essential part of data analysis and manipulation in Python. The resources provided in this article, including the official documentation, “Python for Data Analysis” book, Kaggle tutorials, DataCamp, and Stack Overflow, can help users learn more about using Pandas and gain practical experience in data manipulation and analysis.

In this article, we discussed two methods for dropping rows based on conditions in Pandas DataFrame, along with their syntax and example codes. Pandas is a popular data manipulation library for Python that enables users to perform in-depth data analysis easily.

It is essential to be able to manipulate data in Pandas DataFrames to get the best possible insights. We emphasized the importance of properly viewing loaded DataFrames and provided additional resources like the official documentation, “Python for Data Analysis,” Kaggle Tutorials, DataCamp, and Stack Overflow that can further assist users in working with DataFrames in Pandas.

By using these resources, one can learn how to manipulate and analyze data in Pandas, perform better data exploration, and derive powerful insights from the data.