missing-values-basics.py

# Drop the columns where all elements are missing values:
df.dropna(axis=1, how='all')

# Drop the columns where any of the elements are missing values
df.dropna(axis=1, how='any')

# Keep only the rows which contain 2 missing values maximum
df.dropna(thresh=2)

# Drop the columns where any of the elements are missing values
df.dropna(axis=1, how='any')

# Fill all missing values with the mean of the particular column
df.fillna(df.mean())

# Fill any missing value in column 'A' with the column median
df['A'].fillna(df['A'].median())

# Fill any missing value in column 'Depeche' with the column mode
df['Depeche'].fillna(df['Depeche'].mode())

The second and fourth examples are the same.