Скачать или смотреть 31. dropna | Handling Missing Values Using Pandas | Part 4

Dropna | Handling Missing Values in Pandas | Part 4

Syntax:

data.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
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1. Do you want to drop rows or columns having missing values?
0, or 'index' : Drop rows which contain missing values.
1, or 'columns' : Drop columns which contain missing value.
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data.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

2. Do you want to drop rows or columns having at least one missing value or all missing values?

'any' : If any NA values are present, drop that row or column.
'all' : If all values are NA, drop that row or column.
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data.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

3. Do you want to set some threshold value to drop missing values?

thresh : int, optional
Require that many non-NA values.
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data.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

4. Do you want to drop rows of some selected columns only?

subset: Define in which column(s) to look for missing values.
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data.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

5. Do you want to drop rows or columns temporarily or permanently?

inplace : bool, default False
If True, do operation inplace and return None.
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Github Link: https://github.com/PRIYANG-BHATT/Data...

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