To sort a pandas dataframe in ascending order row-wise, you can use the `sort_values()`

method along with the `axis=1`

parameter. This will sort the values in each row in ascending order.

Here's an example of how you can sort a pandas dataframe named `df`

row-wise in ascending order:

```
1
``` |
```
df = df.apply(lambda x: x.sort_values(), axis=1)
``` |

This code will sort the values in each row of the dataframe `df`

in ascending order.

## How to sort ascending row-wise in pandas dataframe by selecting specific rows?

To sort rows in a pandas DataFrame in ascending order row-wise while selecting specific rows, you can use the `iloc`

method to select the rows and then use the `sort_values()`

method to sort the selected rows. Here's an example:

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import pandas as pd # Create a sample dataframe data = {'A': [4, 2, 6, 1, 5], 'B': [8, 3, 7, 2, 6], 'C': [10, 5, 9, 4, 8]} df = pd.DataFrame(data) # Select specific rows (in this case, rows 1 and 3) selected_rows = df.iloc[[1, 3]] # Sort selected rows in ascending order row-wise sorted_selected_rows = selected_rows.apply(sorted, axis=1) print(sorted_selected_rows) |

This will output:

1 2 3 |
A B C 1 2 3 5 3 1 2 4 |

In this example, we selected rows 1 and 3 from the original DataFrame `df`

and sorted them in ascending order row-wise.

## How to sort ascending row-wise in pandas dataframe and filter out certain rows?

To sort a pandas DataFrame in ascending order row-wise and filter out certain rows, you can use the following code:

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import pandas as pd # Creating a sample DataFrame data = {'A': [5, 2, 7, 8], 'B': [3, 4, 1, 6], 'C': [9, 2, 6, 5]} df = pd.DataFrame(data) # Sort the DataFrame in ascending order row-wise sorted_df = df.apply(sorted, axis=1) # Filter out rows based on a condition (e.g., filter out rows where column 'A' is less than 5) filtered_df = sorted_df[df['A'] >= 5] print(filtered_df) |

In this code snippet, we first create a sample DataFrame using some dummy data. Next, we apply the `sorted`

function to sort the DataFrame in ascending order row-wise. Finally, we filter out rows where the value in column 'A' is less than 5 by using boolean indexing.

You can modify the filtering condition to suit your specific requirements.

## What is the syntax for sorting ascending row-wise in pandas dataframe?

To sort a pandas DataFrame ascending row-wise, you can use the `sort_values()`

method with the `axis`

parameter set to 1.

Here is the syntax:

```
1
``` |
```
df.sort_values(by=, axis=1, ascending=True)
``` |

**by**: Specifies the column/labels to sort on. If not provided, all columns will be sorted.**axis**: Specifies the axis along which to sort. Use axis=1 for row-wise sorting.**ascending**: Specifies whether to sort in ascending order. Set ascending=True for ascending order.

## How to sort ascending row-wise in pandas dataframe and handle ties?

To sort a pandas DataFrame row-wise in ascending order and handle ties, you can use the `sort_values()`

method with the `axis=1`

parameter set to sort by columns. You can also specify how to handle ties using the `na_position`

parameter.

Here is an example code snippet to achieve this:

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import pandas as pd # Create a sample DataFrame data = {'A': [3, 2, 1, 4], 'B': [5, 3, 7, 1], 'C': [8, 6, 1, 2]} df = pd.DataFrame(data) # Sort the DataFrame row-wise in ascending order and handle ties by placing NaNs at the end sorted_df = df.sort_values(by=list(df.columns), axis=1, na_position='last') print(sorted_df) |

In this example, the DataFrame `df`

is sorted row-wise in ascending order using the `sort_values()`

method with the `axis=1`

parameter. The `na_position='last'`

parameter specifies that NaNs should be placed at the end of the sorted columns. This ensures that ties are handled by placing NaNs after the non-NaN values.

You can adjust the `na_position`

parameter to suit your specific tie-handling preferences.