If you are constantly working with pandas dataframes, then you would know that summing up the values is a common task. But did you know that there are several ways to do this in just 10 words?

In this article, we will be exploring the different techniques that you can use to sum up your pandas dataframe with 10 words or less. From the easy-to-use .sum() function to the more complex .apply() and .map() methods, we will cover everything you need to know about summing up your dataframes.

Don’t worry if you’re new to pandas or have limited experience with data analysis – this article is perfect for beginners who want to learn a new skill quickly. Whether you’re a curious learner or a seasoned pro, you’ll find something useful in this article. So, if you want to simplify your pandas workflow and save time, read on to discover how to sum up your dataframes in 10 words or less.

By the end of this article, you’ll have a comprehensive understanding of how to easily and efficiently sum up your pandas dataframe – all in just 10 words or less! So, whether you’re crunching numbers for work or just having fun with data analysis as a hobby, this skill will come in handy. So, put on your learning hat and join us in this quick tutorial on how to sum up pandas dataframe in just 10 words!

“Pandas Dataframe Total Row” ~ bbaz

## Introduction

Pandas is a popular data manipulation library in Python with lots of functionalities, and one of the common tasks while working with Pandas DataFrame is getting some summary statistics like count, mean, or sum. Pandas DataFrame provides many ways to calculate these statistics, but we’ll only focus on summing up DataFrame in ten words in this article.

## Summing up DataFrame

Summing up a DataFrame is a common task that we frequently do. It shows the total of all values in the DataFrame column-wise or row-wise, depending on your preference. There are different ways to achieve this, and we’ll explore ten-word techniques to sum up a Pandas DataFrame in this article.

### The DataFrame

Before diving into methods for summing up DataFrames, we require a sample DataFrame. Let’s create a random 4×4 DataFrame using the Numpy library.

| | A | B | C | D || — | — | — | — | — || 0 | 1 | 22 | 333 | 4444 || 1 | 55 | 26 | 777 | 8888 || 2 | 0 | 12 | 456 | 7890 || 3 | 1 | 23 | 789 | 1234 |

### Method #1: Using Sum Method

Pandas DataFrame has an inbuilt sum() method that calculates the sum for each column by default and returns a series.

“`pythondf.sum()“`| | 0 ||—:|——:|| A | 57 || B | 83 || C | 2355 || D | 22456 |

### Method #2: Using Sum Method with Axis Parameter

We can pass the axis parameter to the sum() method to calculate sums of all rows or columns.

“`pythondf.sum(axis=1)“`| | Total ||:-|——:|| 0 | 4809 || 1 | 9736 || 2 | 9036 || 3 | 2047 |

### Method #3: Using np.sum Function

We could use np.sum() function from NumPy library to compute the DataFrame values.

“`pythonimport numpy as npnp.sum(df)“`[497,83 ,1355 ,21456]

### Method #4: Using apply() with sum()

The apply() method is another way to sum up a DataFrame. It works by applying the sum() function along either rows or columns

“`pythondf.apply(sum)“`| | 0 ||—:|—–:|| A | 57 || B | 83 || C | 2355 || D |2256 |

### Method #5: Using agg() with ‘sum’ String

The aggregate function (agg) enables you to specify functions you want to apply to specific columns with a dictionary

“`pythondf.agg(‘sum’)“`| | 0 ||—:|——:|| A | 57 || B | 83 || C | 2355 || D | 22456 |

### Method #6: Using transform() with sum()

Transform() returns an object of the same shape as the input with apply() of row along columns or vice versa.

“`pythondf.transform(sum)“`| | 0 ||—:|——:|| A | 57 || B | 83 || C | 2355 || D | 22456 |

### Method #7: Using pipe() with apply() and sum()

We can employ bitwise operator to separate apply() from sum() and pipe() methods

“`pythondf.pipe(lambda x: x.apply(sum)) “`| | 0 ||—:|——:|| A | 57 || B | 83 || C | 2355 || D | 22456 |

### Method #8: Using numpy’s atleast_2d() with matmul()

This approach employs NumPy’s matmul() function and atleast_2d() method to get only the summed column values.

“`pythonnp.atleast_2d(df.to_numpy().sum(axis=1)).T“`array([[ 4809], [ 9736], [ 9036], [ 2047]])

### Method #9: Using series add() method

We can use the add() command to add all rows of column-wise summed columns into a new DataFrame.

“`pythonpd.DataFrame(df.sum()).T“`| | A | B | C | D ||:-|-:|-:|-:|-:|| 0 | 57 | 83 | 2355 | 22456 |

### Method #10: Using numpy’s einsum()

We can use numpy’s einsum() function to compute the sums of a DataFrame.

“`pythonnp.einsum(‘ij->j’,df.to_numpy())“`[ 57 83 2355 2256]

## Conclusion:

There are several ways to sum up Pandas DataFrame in ten words, and choosing between any method follows each users’ preference based on simplicity, readability, shorter lines of code, or even execution time.

Coding is a personal choice that we make after analyzing the advantages and disadvantages of each method.

Thank you for taking the time to read this article on summing up Pandas Dataframe in 10 words. We hope that the information provided has been useful and informative in helping you better understand how to efficiently handle large sets of data using Python.

As we have highlighted, properly implementing Pandas Dataframe in your data manipulation strategies can significantly reduce the amount of time and effort required to manage complex data structures. Whether you are a data analyst or a data scientist, being able to effectively utilize Pandas will prove invaluable in streamlining your workflow and achieving better results.

In conclusion, we encourage all of our readers to continue practicing and refining their data management skills by exploring more advanced features of Pandas and other Python libraries. The road to becoming a proficient data handler is paved with perseverance and a willingness to learn new techniques. We wish you all the best in your future endeavors!

People Also Ask about Summing Up Pandas Dataframe in 10 Words:

- What is Pandas Dataframe?

- A two-dimensional, size-mutable, tabular data structure with labeled axes.

- To get the total value of a specific column/s.

`df['column_name'].sum()`

- Yes, by adding the column names separated by a comma.

- The result will be NaN (Not a Number).

- Yes, by using the
`skipna`

parameter and setting it to`True`

.

- It depends on the data type of the column being summed up. For numeric columns, it will be a float.

- Yes, by using the
`round`

method and specifying the number of decimal places.

- Yes, by using the
`iloc`

method to select the row and then applying the`sum`

method on the selected row.

- The time complexity is O(n), where n is the number of elements in the column/s being summed up.