Python pandas is an essential tool when working with data – it provides powerful data structures to make data manipulation and analysis quick and easy. One common task that you might need to perform is deleting a row in Excel.

This task can actually be done in Python using the pandas library, which is arguably faster and more efficient compared to manual manipulation in Excel.

## Example

This is a sample.xlsx file:

### Step 1: Install Necessary Libraries

First, you need to ensure you have the necessary libraries installed in your Python environment. Specifically, you will need the **Pandas** and **OpenPyXL** libraries. If you don’t already have them, simply run these commands in your python environment:

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pip install pandas pip install openpyxl |

### Step 2: Load Your Excel File

The first step in deleting a row using Pandas is to load your Excel file. You will be using the pandas **read_excel()** function for this purpose. Let’s say you have an Excel file named ‘sample.xlsx’.

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import pandas as pd # Load excel file df = pd.read_excel('sample.xlsx') # Display the DataFrame print(df) |

### Step 3: Delete the Desired Row

Next, to delete a row from the DataFrame, you’ll use the **drop()** method. For instance, if you want to delete the first row (row with index 0), you can do:

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# Delete the first row df = df.drop([0]) |

### Step 4: Save the DataFrame back to Excel

Now that you’ve deleted the row from the DataFrame, the final step is to save it back to an Excel file. For that, you will use the **to_excel()** function.

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# Save to Excel df.to_excel('sample.xlsx', index=False) |

By adding **index=False**, you ask pandas not to write row indices into the Excel file, which is usually what you want.

## Here is the full code:

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import pandas as pd # Load excel file df = pd.read_excel('sample.xlsx') # Delete the first row df = df.drop([0]) # Save to Excel df.to_excel('sample.xlsx', index=False) |

## Conclusion

With Python’s Pandas library, you can easily manipulate Excel files without even opening them. Removing rows is just one operation among many others, such as adding rows, modifying data, or calculating statistical metrics.

Learning more about pandas will surely speed up your data-preparation tasks.