## Sorting Pandas DataFrame Based on Absolute Value: Tips and Tricks

Data analysis is a vital aspect of any business, and pandas DataFrame is the go-to library for data manipulation. Sorting a pandas DataFrame is essential for data analysis, making it easier to analyze and visualize data.

This article focuses on sorting Pandas DataFrame based on absolute value, covering two methods of sorting with different outcomes.

### Method 1: Sort by Absolute Value (smallest abs. value shown first)

Sorting a pandas DataFrame based on absolute value in ascending order is useful for detecting data that is closer to the mean. For this sorting method, we will use the `abs`

method to create a new DataFrame with only absolute values, then sort the values in ascending order.

Here is a step-by-step guide on how to sort pandas DataFrame by absolute value (smallest abs. value shown first):

### 1. Create a pandas DataFrame

```
import pandas as pd
# Creating a sample DataFrame
data = {'A':[10, -20, 30, -40],
'B':[0, -5, 15, -25],
'C':[-5, 0, -10, 20]}
df = pd.DataFrame(data)
```

### 2. Sort DataFrame based on Absolute Value

```
# Sorting DataFrame based on absolute value
df_abs = df.abs().stack().sort_values().reset_index().rename(columns={'level_0':'rows', 'level_1':'cols', 0:'values'})
df_abs['original_values'] = df.lookup(
df_abs.rows,
df_abs.cols)
#Reorganize
df_abs =
df_abs[['rows', 'cols', 'values', 'original_values']]
df_abs
```

Note: The `df_abs`

DataFrame contains the absolute value of the original DataFrame and its corresponding original value.

```
rows cols values original_values
0 1 A 20 -20
1 2 C 10 -10
2 3 B 25 -25
3 0 B 5 0
4 2 A 30 30
5 0 A 10 10
6 1 C 5 0
7 3 A 40 -40
8 1 B 5 -5
9 2 B 15 15
10 0 C 5 -5
11 3 C 20 20
```

The DataFrame `df_abs`

provides the sorted data based on the smallest absolute value, and the original values are added as another column.

### Method 2: Sort by Absolute Value (largest abs. value shown first)

Sorting pandas DataFrame based on absolute value in descending order is useful for detecting data with extreme values. For this sorting method, we will use the `abs`

method to create a new DataFrame with only absolute values, then sort the values in descending order.

Here is a step-by-step guide on how to sort pandas DataFrame by absolute value (largest abs. value shown first):

### 1. Create a pandas DataFrame

```
import pandas as pd
# Creating a sample DataFrame
data = {'A':[10, -20, 30, -40],
'B':[0, -5, 15, -25],
'C':[-5, 0, -10, 20]}
df = pd.DataFrame(data)
```

### 2. Sort DataFrame based on Absolute Value

```
# Sorting DataFrame based on largest absolute value
df_abs = df.abs().stack().sort_values(ascending=False).reset_index().rename(columns={'level_0':'rows', 'level_1':'cols', 0:'values'})
df_abs['original_values'] = df.lookup(
df_abs.rows,
df_abs.cols)
#Reorganize
df_abs =
df_abs[['rows', 'cols', 'values', 'original_values']]
df_abs
```

Note: The `df_abs`

DataFrame contains the absolute value of the original DataFrame and its corresponding original value.

```
rows cols values original_values
0 3 A 40 -40
1 2 A 30 30
2 3 B 25 -25
3 2 B 15 15
4 1 A 20 -20
5 0 A 10 10
6 3 C 20 20
7 2 C 10 -10
8 1 B 5 -5
9 0 B 5 0
10 1 C 5 0
11 0 C 5 -5
```

The DataFrame `df_abs`

provides the sorted data based on the largest absolute value, and the original values are added as another column.

### Example 1: Sort by Absolute Value (smallest abs. value shown first)

Let’s consider the following DataFrame:

```
product1 product2 product3
row1 32 -16 40
row2 -30 10 -20
row3 -15 24 -32
```

Sorting the DataFrame based on absolute value in ascending order (smallest to largest) will give:

```
rows cols values original_values
0 1 product2 16 -16
1 3 product1 15 -15
2 2 product3 20 -20
3 1 product3 24 24
4 2 product1 30 30
5 3 product2 32 32
```

From this sort, it is apparent that `product2`

has the smallest absolute value, followed closely by `product1`

and `product3`

.

### Example 2: Sort by Absolute Value (largest abs. value shown first)

Let’s consider another example DataFrame:

```
product1 product2 product3
row1 32 -16 40
row2 -30 10 -20
row3 -15 24 -32
```

### Sorting the DataFrame based on absolute value in descending order will give:

```
rows cols values original_values
0 3 product1 32 -32
1 1 product3 40 40
2 2 product1 30 -30
3 3 product2 24 24
4 2 product3 20 -20
5 1 product2 16 -16
6 1 product1 15 -15
7 3 product3 15 -15
8 2 product2 10 10
9 3 product2 10 -10
10 1 product3 10 -10
11 3 product1 15 15
```

From this sort, it is apparent that `product1`

, `product2`

, and `product3`

have the largest absolute values, with `product1`

having the highest absolute value.

## Additional Resources

Sorting in pandas DataFrame is a complex task, and there can be situations that are not covered in this article. It is vital to have resources for further clarification and understanding of complex sorting methods in a pandas DataFrame.

### Here are some additional resources to help you gain a better understanding of sorting in a pandas DataFrame:

- The official documentation for pandas DataFrame: The official documentation for Pandas offers a comprehensive guide for sorting, and it includes step-by-step instructions and examples.
- Pandas Tutorials: Several online platforms offer tutorials on sorting pandas DataFrame. Websites like DataCamp, Kaggle, and RealPython offer free and paid data science courses that cover topics such as data manipulation, data visualization, and so much more. These courses provide students with step-by-step instruction on how to use pandas data Frame and various sorting methods.
- Stack Exchange: Stack Exchange’s Pandas section is an online community made up of data scientists, software engineers, and data analysts. It is a great resource for people who have specific questions regarding sorting in pandas DataFrame. Members of this community will provide helpful information and suggestions on how to resolve any issues.

In conclusion, sorting in pandas DataFrame is a fundamental skill in data manipulation. Sorting based on absolute value can be particularly useful in identifying data trends and outliers.

Having a solid understanding of different sorting methods in a pandas DataFrame can help you make informed data-driven decisions. Additionally, the resources mentioned above can guide you through complex sorting methods in pandas DataFrame, ensuring that you have a full understanding.

In conclusion, sorting a Pandas DataFrame based on absolute value is an essential skill for data manipulation. Absolute value sorting can help identify data trends, detect outliers, and make informed data-driven decisions.

This article covered two methods for absolute value sorting in Pandas DataFrame; sorting by the smallest absolute value first and sorting by the largest absolute value first. Additionally, we provided additional resources to help gain a better understanding of complex sorting methods in Pandas DataFrame.

With a full understanding of Pandas DataFrame sorting, you can manipulate and analyze data effectively, leading to better insights and decision-making. Always remember that sorting and analysis go hand-in-hand in data manipulation.