dataframe find nan rows
df[df.isnull().any(axis=1)]dataframe find nan rows
df[df.isnull().any(axis=1)]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.isnull().values.any()select rows which have nan values python
df[df['column name'].isna()]check for missing/ nan values in pandas dataframe
In [27]: df
Out[27]:
A B C
1 NaN -2.027325 1.533582
2 NaN NaN 0.461821
3 -0.788073 NaN NaN
4 -0.916080 -0.612343 NaN
5 -0.887858 1.033826 NaN
In [28]: df.isnull().sum() # Returns the sum of NaN values in each column.
Out[28]:
A 2
B 2
C 3
In [29]: df.isnull().sum().sum # Returns the total NaN values in the dataframe
Out[29]:
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