There are several ways to handle missing values using the Python Pandas module. Here are a few options:
Drop rows or columns with missing values:
You can use the dropna() function to drop rows or columns with missing values. For example:
import pandas as pd
df = pd.read_csv("data.csv")
df = df.dropna() # drops rows with any missing values
df = df.dropna(axis=1) # drops columns with any missing values
df = df.dropna(thresh=2) # drops rows with more than 2 missing values
Impute missing values:
You can use the fillna() function to impute missing values with a specific value. For example:
import pandas as pd
df = pd.read_csv("data.csv")
df = df.fillna(0) # fills missing values with 0
df = df.fillna(df.mean()) # fills missing values with the mean of the column
Interpolate missing values:
You can use the interpolate() function to interpolate missing values based on the values of the surrounding data points. For example:
import pandas as pd
df = pd.read_csv("data.csv")
df = df.interpolate() # interpolates missing values using linear interpolation
It's important to choose the appropriate method for handling missing values based on the context and the needs of your analysis.
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