How to Update Values in a Pandas DataFrame Based on Matching IDs?

Changing Values Based on Matched IDs in Pandas

In Python, Pandas provides efficient data manipulation capabilities. To modify values based on matching IDs, follow these steps:

1. Import Pandas: Begin by importing the Pandas library.
2. Load Data: Read data from a CSV file using pandas.read_csv.
3. Identify Matches: Use the == operator to create a logical condition to identify rows where ID matches a specific value (e.g., df.ID == 103).
4. Overwrite Values: Utilize slicing and indexing to select rows satisfying the condition and overwrite values in the desired columns. For example, df.loc[condition, 'column'] = 'new value'.

A sample code to change FirstName and LastName for ID 103:

<code class="python">import pandas as pd

df = pd.read_csv("test.csv")
df.loc[df.ID == 103, 'FirstName'] = "Matt"
df.loc[df.ID == 103, 'LastName'] = "Jones"</code>

Additional Notes:

• You can update multiple columns at once using a list of columns: df.loc[condition, ['column1', 'column2']] = ['new value1', 'new value2'].
• Chained assignment can also be used, but it may have unexpected behaviors and is discouraged in newer Pandas versions.
• Ensure you have the appropriate Pandas version (0.11 or newer) for overwriting values using .loc.

The above is the detailed content of How to Update Values in a Pandas DataFrame Based on Matching IDs?. For more information, please follow other related articles on the PHP Chinese website!