To sum rows containing specific targets in pandas, you can use the `sum()`

function along with boolean indexing.

First, you can create a boolean mask by applying a condition on the target values in the DataFrame. Then, you can use this mask to filter out the rows that contain the specific targets. Finally, you can apply the `sum()`

function on the filtered rows to get the sum of the values in those rows.

For example, if you have a DataFrame `df`

and you want to sum rows that contain the values 'target1' and 'target2' in columns 'A' and 'B', you can do the following:

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mask = (df['A'] == 'target1') & (df['B'] == 'target2') sum_of_targets = df[mask].sum() |

This will give you the sum of the rows that contain 'target1' in column 'A' and 'target2' in column 'B'.

## What is the output format when summing rows in pandas?

When summing rows in pandas, the output format is a Series object with the sum of each row displayed in a tabular format. The index of the Series will correspond to the index of the DataFrame rows.

## What is the impact of scaling on the sum of rows in pandas?

When scaling data in pandas, the sum of rows may not remain the same. Scaling typically involves transforming the values in each row (or column) by a certain factor, such as standardizing the data to have a mean of 0 and a standard deviation of 1.

This transformation can change the distribution of the data within each row, which in turn can affect the sum of the values in that row. For example, if the original values in a row were all positive, scaling them may result in some negative values, leading to a different sum.

Therefore, it is important to be aware that scaling can impact the sum of rows in pandas and to consider this when interpreting the data or performing calculations that rely on the sum of rows.

## How to handle duplicate rows when summing in pandas?

When summing duplicate rows in pandas, you can use the `groupby`

function along with the `sum`

function to aggregate the values of duplicate rows. Here's an example:

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import pandas as pd # Create a sample DataFrame with duplicate rows data = {'A': [1, 1, 2, 2], 'B': [2, 2, 4, 4]} df = pd.DataFrame(data) # Sum the values of duplicate rows using groupby and sum summed_df = df.groupby(['A', 'B']).sum().reset_index() print(summed_df) |

This code snippet will group the rows by columns 'A' and 'B', and then sum the values of duplicate rows in each group. The `reset_index`

function is used to reset the index of the resulting DataFrame.

After running this code, `summed_df`

will contain the aggregated values of duplicate rows in the original DataFrame `df`

.