How do you handle missing data?
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Handling missing data is a crucial step in data cleaning and analysis. In Pandas, there are several methods to detect, remove, or fill missing values efficiently.
1. Detecting Missing Data
Use isnull() or isna():
df.isnull()
df.isnull().sum() # Count missing values per column
2. Removing Missing Data
Drop rows with missing values:
df.dropna()
Drop columns with missing values:
df.dropna(axis=1)
3. Filling Missing Data
Fill with a specific value:
df.fillna(0)
Fill with the mean, median, or mode:
df['column'].fillna(df['column'].mean())
Forward fill or backward fill:
df.fillna(method='ffill') # forward
df.fillna(method='bfill') # backward
4. Replace with Interpolation
df.interpolate()
5. Check for All Missing Rows/Columns
df.dropna(how='all')
Summary:
Pandas provides powerful tools like isnull(), dropna(), fillna(), and interpolate() to handle missing data. Choose the method based on your data and analysis goals—either drop, fill, or impute values to maintain data quality.
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