There are several ways to handle duplicate values using the Python Pandas module. Here are a few options:
Drop duplicate rows:
You can use the drop_duplicates() function to drop duplicate rows. For example:
import pandas as pd
df = pd.read_csv("data.csv")
df = df.drop_duplicates() # drops all duplicate rows
df = df.drop_duplicates(subset=["col1", "col2"]) # drops duplicate rows
based on the values in columns "col1" and "col2"
Mark duplicate rows:
You can use the duplicated() function to mark duplicate rows with a boolean value. For example:
import pandas as pd
df = pd.read_csv("data.csv")
df["duplicate"] = df.duplicated() # marks duplicate rows as
True and non-duplicate rows as False
Remove duplicate rows based on specific columns:
You can use the drop_duplicates() function with the subset parameter to drop duplicate rows based on the values in specific columns. For example:
import pandas as pd
df = pd.read_csv("data.csv")
df = df.drop_duplicates(subset=["col1", "col2"]) # drops duplicate rows based on
the values in columns "col1" and "col2"
It's important to choose the appropriate method for handling duplicate values based on the context and the needs of your analysis.
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