count nan pandas
#Python, pandas
#Count missing values for each column of the dataframe df
df.isnull().sum()count nan pandas
#Python, pandas
#Count missing values for each column of the dataframe df
df.isnull().sum()handling missing or NaN values in pandas dataframe
# Six(6) ways to handle NaN values
# 1. Drop/delete any rows with NaN values
df.dropna(axis = 0) #row is axis = 0
# 2. Drop/delete any columns with NaN values
df.dropna(axis = 1) #column is axis = 1
# 3. Replace all NaN values with 0
df.fillna(0)
# 4. Replace NaN values with the previous value in the column, Fill Forward
df.fillna(method = 'ffill', axis = 0) #OR axis = 1 for rows
# 5. Replace NaN values with the next value in the column, Fill Backward
df.fillna(method = 'backfill', axis = 0) #OR axis =1 for rows
# 6. replace NaN values by using linear interpolation using column values
df.interpolate(method = 'linear', axis = 0) #OR axis = 1 for rows
#NB: 1. For the last three options, depending on the method, changes to NaN
# in the first row, last row, first column or last column may not be effected.
# 2. Remember to include inplace = True if you want the original dataframe to
#be modified, else the changes will revert back to the original when you
#reference the dataframe again. Eg.
df.dropna(axis = 0, inplace = True)find nan values in a column pandas
df['your column name'].isnull().sum()find nan value in dataframe python
# to mark NaN column as True
df['your column name'].isnull()to detect if a data frame has nan values
df.isnull().sum().sum()
5to detect if a data frame has nan values
> df.isnull().any().any()
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