How To Find Missing Values In A DataFrame?
Table Of Contents:
- Syntax ‘isna( )’ Method In Pandas.
- Examples ‘isna( )’ Method.
(1) Syntax:
DataFrame.isna()Description:
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as None or
numpy.NaN, gets mapped to True values.Everything else gets mapped to False values. Characters such as empty strings
''ornumpy.infare not considered NA values (unless you setpandas.options.mode.use_inf_as_na = True).
Returns:
- DataFrame
- Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
(2) Examples Of isna() Method:
Example-1:
df = pd.DataFrame(dict(age=[5, 6, np.NaN],
born=[pd.NaT, pd.Timestamp('1939-05-27'),
pd.Timestamp('1940-04-25')],
name=['Alfred', 'Batman', ''],
toy=[None, 'Batmobile', 'Joker']))
dfOutput:
# Show which entries in a DataFrame are NA.
df.isna()Output:
# Count Of Missing Values In Each Column
df.isna().sum()Output:
age 1
born 1
name 0
toy 1
dtype: int64# Percentage Of Missing Values In Each Column
(df.isna().sum()/len(df))*100Output:
age 33.333333
born 33.333333
name 0.000000
toy 33.333333
dtype: float64