xxxxxxxxxx
df = df.dropna(subset=['colA', 'colC'])
xxxxxxxxxx
# remove all rows without a value in the 'name' column
df = df[df['name'].notna()]
xxxxxxxxxx
df.dropna() #drop all rows that have any NaN values
df.dropna(how='all')
xxxxxxxxxx
import pandas as pd
df = pd.DataFrame({'values_1': ['700','ABC','500','XYZ','1200'],
'values_2': ['DDD','150','350','400','5000']
})
df = df.apply (pd.to_numeric, errors='coerce')
df = df.dropna()
df = df.reset_index(drop=True)
print (df)