Python

# remove all rows without a value in the 'name' column
df = df[df['name'].notna()]

# making new data frame with dropped NA values 
new_data = df.dropna(axis = 0, how ='any')

df.dropna()     #drop all rows that have any NaN values
df.dropna(how='all')

df = df.dropna(subset=['colA', 'colC'])

df = df.dropna(axis = 1)

df = df[pd.notnull(df['RespondentID'])]   
# Drop the missing value present in the "RespondentID" column

df = df.dropna(how = 'all')

df = df.dropna()

# Drop rows which contain any NaN value in the selected columns
mod_df = df.dropna( how='any',
                    subset=['Name', 'Age'])

In [215]:

df[df['entrytype'].apply(lambda x: str(x).isdigit())]
Out[215]:
  entrytype
0         0
1         1
4         2

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