Find empty or NaN entry in Pandas Dataframe

Question

I am trying to search through a Pandas Dataframe to find where it has a missing entry or a NaN entry.

Here is a dataframe that I am working with:

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Answer 1

Partial solution: for a single string column tmp = df['A1'].fillna(''); isEmpty = tmp=='' gives boolean Series of True where there are empty strings or NaN values.

Answer 2

Another opltion covering cases where there might be severar spaces is by using the isspace() python function.

df[df.col_name.apply(lambda x:x.isspace() == False)] # will only return cases without empty spaces

adding NaN values:

df[(df.col_name.apply(lambda x:x.isspace() == False) & (~df.col_name.isna())] 

Answer 3

I've resorted to

df[ (df[column_name].notnull()) & (df[column_name]!=u'') ].index

lately. That gets both null and empty-string cells in one go.

Answer 4

Try this:

df[df['column_name'] == ''].index

and for NaNs you can try:

pd.isna(df['column_name'])

Answer 5

you also do something good:

text_empty = df['column name'].str.len() > -1

df.loc[text_empty].index

The results will be the rows which are empty & it's index number.

Answer 6

To obtain all the rows that contains an empty cell in in a particular column.

DF_new_row=DF_raw.loc[DF_raw['columnname']=='']

This will give the subset of DF_raw, which satisfy the checking condition.