How can I count if column value is equal to NaN or zero?

Question

I'm using the code below to indicate whether there are any missing values (NaN) or zeros (0.00) in a column.

# Specifying the NaNs
num_nan_totals = df.loc[ (pd.isna(df['Totals'])) , 'Totals' ].shape[0]

# Specifying the zeros
num_zero_totals = df["Totals"] == 0.00

# For output
print(f"There are {num_nan_totals} NaNs in the totals column")
print(f"There are {num_zero_totals} zeros in the totals column")

My output:

There are 0 NaNs in the totals column
There are 433      False
434      False
435      False
436      False
# etc. etc. etc.

Having visually checked the dataset, there should be at least one '0.00' instance, which is how I know it's going wrong. I suspect the issue is with the zeros definition, can anyone give any hints? Thanks!

Answer 1

You are on the right track with building the masks. Assuming, you only want the counts, you can use the sum method from pandas. Info here: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html

For the mask, False is 0 and True is 1 so adding up all values is a quick way to get the count of all true values.

# Count of nan
num_nan_totals = df['Totals'].isna().sum()
# Count of 0
num_zero_totals = (df['Totals'] == 0.00).sum()