Adventures in Machine Learning

Efficiently Selecting Rows in Pandas DataFrame Based on Column Values

Selecting Rows in a Pandas DataFrame Based on Column Values

If you are working with a large dataset and want to extract specific information based on column values, you need to know how to select rows in a Pandas DataFrame. There are many methods for selecting particular rows, but in this article, we will focus on two: the

Between Function Usage and the Negated

Between Function Usage.

Between Function Usage

The

Between Function Usage is a straightforward method for selecting rows that meet a certain requirement. Suppose you have a DataFrame that contains the information about sales revenue of your company from different regions and you want to extract all the sales made in a particular period.

In this example, the DataFrame has columns for Region, Sales, and Date. To select rows between two dates, you can use the between() function to specify the start and end dates.

The between() function returns True for each row that falls within the specified range of dates. Here is an example code snippet to select all the rows between two dates:

import pandas as pd

df = pd.read_csv(‘sales_data.csv’)

start_date = ‘2021-01-01’

end_date = ‘2021-06-30’

sales_range = df.loc[df[‘Date’].between(start_date, end_date)]

In this example, sales_range is the resulting DataFrame, which contains all the rows between 2021-01-01 and 2021-06-30. Negated

Between Function Usage

The Negated

Between Function Usage is the inverse of the

Between Function Usage.

It selects all rows that are outside the specified range. The following code snippet demonstrates how to use the negated_between() function to select all rows outside the specified range of dates:

import pandas as pd

df = pd.read_csv(‘sales_data.csv’)

start_date = ‘2021-01-01’

end_date = ‘2021-06-30’

outside_sales_range = df.loc[~df[‘Date’].between(start_date, end_date)]

In this example, outside_sales_range is the resulting DataFrame, which contains all rows outside the range between 2021-01-01 and 2021-06-30.

DataFrame Example

To better understand how to select rows in a Pandas DataFrame based on column values, let’s create a DataFrame example. Suppose you want to store the information of your company’s employees, such as name, age, department, and salary.

Here is how to create a DataFrame in Python:

import pandas as pd

data = {‘Name’: [‘John’, ‘Alex’, ‘Lisa’, ‘Maggie’],

‘Age’: [28, 24, 30, 34],

‘Department’: [‘Sales’, ‘HR’, ‘IT’, ‘Marketing’],

‘Salary’: [50000, 60000, 55000, 58000]}

df = pd.DataFrame(data)

In this example, the DataFrame consists of four columns: Name, Age, Department, and Salary, and four rows, each representing an employee’s information.

Viewing the Pandas DataFrame

After creating the DataFrame, you can view its contents using the head() function, which shows the first five rows of the DataFrame. You can also use the tail() function to show the last five rows of the DataFrame.

Here is an example code to view the contents of the created DataFrame:

print(df.head())

Output:

Name Age Department Salary

0 John 28 Sales 50000

1 Alex 24 HR 60000

2 Lisa 30 IT 55000

3 Maggie 34 Marketing 58000

In Conclusion

Selecting rows in a Pandas DataFrame based on column values is a crucial skill for data analysts. The

Between Function Usage and Negated

Between Function Usage offer a simple and efficient way to extract specific data from large datasets.

By utilizing these methods, you can save time and extract only the data you need. I hope this article has helped you understand how to select rows in a Pandas DataFrame and how to view its contents.

Selecting Rows Where Column Values are Between Two Specific Values

When working with large datasets, selecting rows where the column values fall within a specific range is a common task. In this section, we will explore how to filter a Pandas DataFrame based on column values between two specific values using the

Filtered DataFrame Selection method.

Filtered DataFrame Selection

The

Filtered DataFrame Selection method is an intuitive way to select rows with column values between two specific values. Suppose you have a DataFrame that contains details of student scores in a test and you want to select rows where the scores fall between 60 and 80.

In this example, the DataFrame has columns for Name, Score, and Grade. To select the rows where the scores fall between 60 and 80, you can use the conditional statement and the logical operator “&” (and) or “|” (or) to filter the DataFrame.

Here is an example of filtering the DataFrame to return all the rows with scores between 60 and 80:

import pandas as pd

df = pd.read_csv(‘scores.csv’, index_col=’Name’)

filtered_df = df[(df[‘Score’] >= 60) & (df[‘Score’] <= 80)]

In this example, the filtered_df is the resulting DataFrame, which contains all the rows with scores between 60 and 80.

Viewing Filtered DataFrame

After filtering the DataFrame, you can view the contents of the selected rows using the head() function. This function displays the first rows of the selected data.

Here is an example code to view the contents of the filtered DataFrame:

print(filtered_df.head())

Output:

Score Grade

Name

John 75 B

Lisa 78 B

Alex 69 C

The output shows the contents of the rows with scores between 60 and 80. Not

Selecting Rows Where Column Values are Between Two Specific Values

In certain cases, you may want to select rows where the column values do not fall within a specific range.

In this section, we will look at how you can use the negated function to filter a Pandas DataFrame.

Filtered DataFrame Selection (negated function)

The

Filtered DataFrame Selection method can be used to select rows where the column values do not fall within a specific range using the tilde sign (~) operator. Suppose you have the same DataFrame of student scores in a test and you want to select rows where the scores are not between 60 and 80.

To select rows where the score is not between 60 and 80, you can use the tilde sign (~) operator to negate the logical statement. Here is an example code snippet:

import pandas as pd

df = pd.read_csv(‘scores.csv’, index_col=’Name’)

negated_filtered_df = df[~(df[‘Score’] >= 60) & (df[‘Score’] <= 80)]

In this example, the negated_filtered_df is the resulting DataFrame, which contains all the rows where the scores are not between 60 and 80.

Viewing Filtered DataFrame (negated function)

After filtering the DataFrame, you can view the contents of the selected rows using the head() function. Here is an example code to view the contents of the filtered DataFrame:

print(negated_filtered_df.head())

Output:

Score Grade

Name

Maggie 53 D

The output shows the contents of the rows where the scores are not between 60 and 80. In conclusion, selecting rows in a Pandas DataFrame based on column values between two specific values is an essential skill for data analysts.

The

Filtered DataFrame Selection method is an efficient and intuitive way to extract specific data from large datasets. By understanding how to use the

Filtered DataFrame Selection method and the negated function, you can filter a DataFrame to select or not select rows where the column values fall within a specific range.

I hope this article has helped you understand how to filter a Pandas DataFrame based on column values between two specific values and how to view its contents. In this article, we explored how to select rows in a Pandas DataFrame based on column values between two specific values using two methods: the

Filtered DataFrame Selection method and the negated function.

By using these methods, you can extract specific data from a large dataset efficiently and accurately. Furthermore, we learned how to view the contents of the selected DataFrame.

It is essential to master these skills for data analysts who work with large datasets. The takeaway from this article is that selecting rows in a Pandas DataFrame based on column values between two specific values is an essential skill.

By understanding how to use these methods, you can simplify your data analysis tasks and focus on extracting the data you need.

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